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Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
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Speakers

Hear from innovative programmers, talented managers, and senior developers who are doing amazing things with artificial intelligence. More speakers will be announced; please check back for updates.

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Huma Abidi is the engineering director of the Artificial Intelligence Product Group at Intel, where she is responsible for deep learning framework software optimization for Intel Xeon processors. Huma joined Intel as software engineer and has since worked in a variety of engineering, validation, and management roles in the area of compilers, binary translation, and machine learning and deep learning. She received the Intel Achievement Award for her work in the Software and Services Group and was twice recognized with the Intel Software Quality award. She is passionate about women’s education and serves on the board of directors at ROSHNI, a philanthropic organization that educates and supports underprivileged girls in India. Huma holds a BS in pre-med and chemistry and an MS in computer science from the University of Massachusetts.

Presentations

Accelerating AI on Xeon through SW optimization Keynote

Huma Abidi discusses the importance of optimization to deep learning frameworks and shares Xeon performance results and work that Intel is doing with its framework partners, such TensorFlow.

Jitin (Jit) Agarwal is vice president of enterprise products at EPAM, where he is focused on productizing and monetizing EPAM’s intellectual property. Jit is a serial entrepreneur who has successfully led startups to scale. Most recently, he was the venture leader of assetSERV at Cognizant, where he developed the product from inception and grew revenues from $100K to $100M+ in TCV over his tenure. He has expertise in all aspects of the product and venture lifecycle, including ideation, funding, development, sales, and marketing as well as postsales customer success. In addition to leading startups, he has worked at a number of leading technology firms, including Microsoft and Gartner. Jit holds degrees from the University of Michigan and Northwestern University.

Presentations

Less firefighting, more strategizing: Lessons learned from implementing AI for TechOps (sponsored by TelescopeAI by EPAM) Session

Beginning in 2010, EPAM started developing a platform to manage and drive its complex IT services business with greater AI analytics. By processing project, team, and individual data, the platform helps IT teams make more-informed decisions faster. Jitin Agarwal shares lessons learned from creating this AI-driven TechOps platform to improve IT performance.

Alasdair Allan is a scientist and researcher who has authored more than 80 peer-reviewed papers and eight books and has been involved with several standards bodies. Originally an astrophysicist, Alasdair now works as a consultant and journalist, focusing on open hardware, machine learning, big data, and emerging technologies, with expertise in electronics, especially wireless devices and distributed sensor networks, mobile computing, and the internet of things. He runs a small consulting company and has written for Make: magazine, Motherboard/VICE, Hackaday, Hackster.io, and the O’Reilly Radar. In the past, he has mesh-networked the Moscone Center, caused a US Senate hearing, and contributed to the detection of what was at the time the most distant object yet discovered.

Presentations

Do-it-yourself artificial intelligence Session

Google's AIY Projects kits bring Google's machine learning algorithms to developers with limited experience in the field, allowing them to prototype machine learning applications and smart hardware more easily. Alasdair Allan walks you through setting up and building the kits and demonstrates how to use the kits' Python SDK for machine learning both in the cloud and locally on a Raspberry Pi.

Karmel Allison is an engineering manager at Google, where she leads a team of engineers working to make TensorFlow high-level APIs easy to use and flawless to scale. Karmel has over 10 years of experience in software development and machine learning. Previously, she led engineering teams building a DNA sequencer at Genia and serving real-time recommendations at Quora. She holds a PhD in bioinformatics from the University of California, San Diego.

Presentations

Ready, set, go: Using TensorFlow to prototype, train, and productionalize your models (sponsored by Google) Session

Building machine learning models is a multistage process. TensorFlow's high-level APIs make this process smooth and easy, whether you're starting small or going big. Karmel Allison walks you through a practical example of building, training, and debugging a model and then exporting it for serving using these APIs.

Varun Arora is a senior AI product engineer and manager at Baidu USA, where he works with AI researchers on productization of deep learning technologies. He is also involved in the development and adoption of Baidu’s deep learning platform, PaddlePaddle. Previously, Varun was the CEO of YC-backed education technology company OpenCurriculum, where he worked with thousands of teachers and hundreds of education administrators in improving curriculum in K–12 classrooms. He has also worked for Inkling, the UN, and a One Laptop Per Child deployment. He holds a bachelor’s and master’s degree from CMU.

Presentations

AI for improving teaching and learning Session

We haven't figured out how to make the perfect robot tutors. But we have figured out how make them much more effective in improving student learning outcomes with modern AI techniques. Varun Arora covers some of those important techniques, along with real-world examples.

David Arpin is a product manager and data scientist at AWS. He works closely with the teams that develop built-in algorithms and deep learning frameworks for Amazon SageMaker. He also actively contributes to, and helps maintain, the SageMaker examples GitHub repository (https://github.com/awslabs/amazon-sagemaker-examples). Prior to that he led a data science team in Fulfillment By Amazon, and worked at two beer companies, and a large grocery retailer.

Presentations

Building deep learning applications with Amazon SageMaker Tutorial

David Arpin offers an overview of the Amazon SageMaker machine learning platform, walking you through setup and using Amazon SageMaker Notebook (a hosted Jupyter Notebook server). You'll get hands-on experience with SageMaker's built-in deep learning algorithm as you dive into building your own neural network architecture using SageMaker's prebuilt TensorFlow containers.

Valentin “Val” Bercovici is founder and CEO at PencilDATA, democratizing trust throughout digital transformation. Val is also cofounder and a senior advisor at Peritus.ai, a company focused on completing the autonomous data center vision by addressing the gap in automated tech support via machine learning. He was a founding member of the governing board at the Cloud Native Compute Foundation (CNCF), the Linux Foundation’s home for Google’s Kubernetes, the Open Container Initiative (OCI), and many other related cloud-native projects. Val has enjoyed a long leadership career. Previously, at NetApp/SolidFire, he launched multibillion-dollar storage and compliance products, created the competitive team and strategy, directed new research investments for the NetApp Data Fabric roadmap, and served as SolidFire’s CTO. A pioneer in the cloud industry, Val led the creation of NetApp’s cloud strategy and introduced the first international cloud standard to the marketplace as CDMI (ISO INCITS 17826) in 2012. Val advises numerous data-driven startups and is passionate about improving diversity within the tech industry. He has several patents issued and pending around data center applications of augmented reality and data authenticity.

Presentations

Turning the "fat protocol" on its side: Making the case for simple distributed ledgers Blockchain

Valentin Bercovici explains why a simple distributed ledger (SDL) offers the right combination of blockchain value to serve diverse enterprise workflows while abstracting away the governance, economic, security, development, implementation, and other operational details of multiple underlying blockchain specifics.

Levent Besik leads the product management teams for Google Cloud’s core AI and AutoML product portfolio. Previously, he led the app install ads measurement products and the display ads in apps business for Google Ads. In that capacity, he was the founder of many innovative products for mobile ads that applied machine learning, such as the display online-to-offline targeting and measurement products and the cross-device and identity platform that powered the majority of cross-device products for display ads. Prior to Google, Levent cofounded two startups focused on multisided markets and was an engineering manager at Microsoft developing web technologies. He holds a bachelor’s degree in computer science with minors in math and physics and an MBA from Haas School of Business at UC Berkeley.

Presentations

Customized ML for the enterprise (sponsored by Google Cloud) Keynote

Levent Besik explains how can enterprises stay ahead of the game with customized ML in our ever-changing world of AI capabilities and limited data science resources.

Mayukh Bhaowal is a director of product management at Salesforce Einstein working on automated machine learning. Previously, Mayukh worked at startups in the domain of machine learning and analytics. He served as head of product of ML platform startup Scaled Inference, backed by Khosla Ventures, and led product at ecommerce startup Narvar, backed by Accel. He was also a principal product manager at Yahoo and Oracle. Mayukh holds a master’s degree in computer science from Stanford University.

Presentations

Trustworthiness of machine learning applications Session

Machine learning is eating software. As decisions are automated, model interpretability must become an integral part of the ML pipeline rather than an afterthought. In the real world, the demand for being able to explain a model is rapidly gaining on model accuracy. Mayukh Bhaowal discusses the steps Salesforce Einstein is taking to make machine learning more transparent and less of a black box.

Wahid Bhimji is a big data architect at the NERSC supercomputing center based at the Berkeley National Laboratory. His interests in AI include generative models and deep learning applied to fundamental science such as high-energy physics. He is also involved in optimizing other aspects of scientific big data workflows running on high-performance computing resources. Previously, Wahid was heavily involved in data management and analysis for the Large Hadron Collider at CERN and the UK government. He holds a PhD in high-energy particle physics.

Presentations

Frontiers of TensorFlow: Space, statistics, and probabilistic ML (sponsored by Google) Session

Join in for two talks on TensorFlow in space and mathematics. Josh Dillon discusses TensorFlow Probablity (TFP), and Wahid Bhimji discusses deep learning for fundamental sciences using high-performance computing.

Lukas Biewald is the founder and chief data scientist of Weights & Biases, a data enrichment platform that taps into an on-demand workforce to help companies collect training data and do human-in-the-loop machine learning. Previously, he led the Search Relevance team for Yahoo Japan and worked as a senior data scientist at Powerset. Lukas was recognized by Inc. magazine as a 30 under 30. Lukas holds a BS in mathematics and an MS in computer science from Stanford University. He is also an expert Go player.

Presentations

Practical issues in building and deploying deep learning models Session

Lukas Biewald offers an overview of real-world deployments of deep learning models at companies like Home Depot, P&G, Coca-Cola, and Uber, covering practical issues that come up training and iterating on models and problems that can arise post deployment. Lukas also discusses recent research that is directly relevant to industry such as active learning, multitask learning, and transfer learning.

Sarah Bird is a technical program manager at Facebook AI Research and its Applied Machine Learning Lab, where she leads strategic projects at the intersection of research and product.

Presentations

Artificial intelligence open source libraries Session

Earlier this year, Amazon, Facebook, and Microsoft partnered to create the Open Neural Network Exchange (ONNX)—an open format to represent deep learning models. Sarah Bird explains in detail how the ONNX framework can help you take AI from research to reality as quickly as possible.

Cormac Brick is director of machine intelligence in the Movidius Group at Intel Corporation, where he builds new foundational algorithms for computer vision and machine intelligence to enhance the Myriad VPU product family. Cormac contributes to internal architecture and helps customers build products using the very latest techniques in deep learning and embedded vision through a set of advanced applications and libraries. He has worked with Movidius since its early days and has contributed heavily to the design of the ISA and the hardware systems as well as computer vision software development and tools. Cormac holds a BEng in electronic engineering from University College Cork.

Presentations

Portability and performance in embedded deep learning: Can we have both? Session

In recent years, there has been lots of work done on low-precision inference that shows that by training for quantization, large gains in energy efficiency can be achieved. Cormac Brick offers a look at industry challenges and progress needed to close the portability performance gap.

Greg Brockman is cofounder and CTO at OpenAI, a nonprofit artificial intelligence research company working to ensure that artificial general intelligence benefits all of humanity. Previously, he was the CTO at Stripe, a payments startup now valued at over $9B USD. Greg studied mathematics at Harvard and computer science at MIT.

Presentations

OpenAI and the path toward safe AGI Keynote

OpenAI has recently demonstrated systems capable of advanced robotic manipulation, holding their own against professionals in the massively complex game Dota 2, as well as unprecedented language understanding. Greg Brockman discusses the increasing generality of these systems and their implication for how we should think about and plan for creating safe AGI.

Chris Butler is the director of AI at Philosophie, where he leads the firm in human-centered AI engagements. Chris has over 18 years of product and business development experience at companies like Microsoft, KAYAK, and Waze. He was first introduced to AI through graph theory and genetic algorithms while studying computer systems engineering at Boston University and has worked on AI-related projects at his startup Complete Seating (data science and constraint programming), Horizon Ventures (advising portfolio companies like Affectiva), and Philosophie (AI consulting and coaching). He has created techniques like empathy mapping for the machine and confusion mapping to create cross-team alignment while building AI products.

Presentations

Design thinking for AI Tutorial

Purpose, a well-defined problem, and trust from people are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment.

Paris Buttfield-Addison is cofounder of Secret Lab, a game development studio based in beautiful Hobart, Australia. Secret Lab builds games and game development tools, including the multi-award-winning ABC Play School iPad games, Night in the Woods, the Qantas airlines Joey Playbox games, and the Yarn Spinner narrative game framework. Previously, Paris was mobile product manager for Meebo (acquired by Google). Paris particularly enjoys game design, statistics, the blockchain, machine learning, and human-centered technology research and writes technical books on mobile and game development (more than 20 so far) for O’Reilly Media. He holds a degree in medieval history and a PhD in computing.

Presentations

Learning from video games Session

Video games have used sophisticated AI techniques for decades to drive everything from area design to navigation to enemies to conversation and planning. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent offer an overview of the history of AI in video games and explain how the needs that drove AI advancement in the game development world map to almost-identical problems in the real world.

Yishay Carmiel is the founder of IntelligentWire, a company that develops and implements industry-leading deep learning and AI technologies for automatic speech recognition (ASR), natural language processing (NLP), and advanced voice data extraction, and the head of Spoken Labs, the strategic artificial intelligence and machine learning research arm of Spoken Communications. Yishay and his teams are currently working on bleeding-edge innovations that make the real-time customer experience a reality—at scale. Yishay has nearly 20 years’ experience as an algorithm scientist and technology leader building large-scale machine learning algorithms and serving as a deep learning expert.

Presentations

How to build privacy and security into deep learning models Session

In recent years, there's been a quantum leap in the performance of AI, as deep learning made its mark in areas from speech recognition to machine translation and computer vision. However, as artificial intelligence becomes increasingly popular, data privacy issues also gain traction. Yishay Carmiel reviews these issues and explains how they impact the future of deep learning development.

Amanda Casari is a Principal Product Manager + Data Scientist for Concur Labs at SAP Concur, where she leads prototypes, interfaces, and future tech for travel and expense. Amanda’s experience ranges from operations research, underwater robotics, complex networks, and data science to serving as a lieutenant in the United States Navy. Most recently, she coauthored Feature Engineering for Machine Learning with Alice Zheng. She is a frequent speaker, working to foster an inclusive data community with groups like PyLadies and NASA Datanauts.

Presentations

Executive Briefing: When privacy scales—Intelligent product design under global data privacy regulation Session

Data-driven companies making intelligent products must design for security and privacy to be competitive globally. Amanda Casari details the high-level changes that EU General Data Protection Regulation (GDPR)-compliant businesses face and how this translates to teams designing products driven by machine learning and artificial intelligence.

Michelle Casbon is a senior engineer on the Google Cloud Platform developer relations team, where she focuses on open source contributions and community engagement for machine learning and big data tools. Michelle’s development experience spans more than a decade and has primarily focused on multilingual natural language processing, system architecture and integration, and continuous delivery pipelines for machine learning applications. Previously, she was a senior engineer and director of data science at several San Francisco-based startups, building and shipping machine learning products on distributed platforms using both AWS and GCP. She especially loves working with open source projects and is a contributor to Kubeflow. Michelle holds a master’s degree from the University of Cambridge.

Presentations

Kubeflow: Portable machine learning on Kubernetes (sponsored by Google) Session

Michelle Casbon offers an overview of Kubeflow. By providing a platform that reduces variability between services and environments, Kubeflow enables applications that are more robust and resilient, resulting in less downtime, quality issues, and customer impact. It also supports the use of specialized hardware such as GPUs, which can reduce operational costs and improve model performance.

Vas Chellappa manages the big data analytics team at Pure Engineering, which sifts through 24 TB of streaming data a day to find test failures so that engineers can focus on much more fun things. Vas holds a PhD in electrical and computer engineering with a focus on computer systems from Carnegie Mellon University.

Presentations

High-performance input pipelines for scalable deep learning Session

Vas Chellappa explains how to keep your GPUs fed with data as you train the next generation of deep learning architectures and shares a new benchmark suite for evaluating and tuning input pipelines.

Frank Chen is a software engineer on the Google Brain team at Google, working to help make TensorFlow and TPUs faster and easier to use. Previously, he was one of the founding software engineers at Coursera, where he worked on online education platforms. When not working, Frank enjoys photography and musical theater and has seen over 30 Broadway shows. He holds both a bachelor’s and master’s degree in computer science from Stanford.

Presentations

AutoGraph and Cloud TPUs (sponsored by Google) Session

Alexandre Passos and Frank Chen offer an overview of TensorFlow AutoGraph, which automatically converts plain Python code into the TensorFlow equivalent, using source code transformation. They then lead a technical deep dive into Google's Cloud TPU accelerators and show you how to program them.

Roger Chen is cofounder and CEO of Computable Labs and program chair for the O’Reilly Artificial Intelligence Conference. Previously, he was a principal at O’Reilly AlphaTech Ventures (OATV), where he invested in and worked with early-stage startups primarily in the realms of data, machine learning, and robotics. Roger has a deep and hands-on history with technology. Before startups and venture capital, he was an engineer at Oracle, EMC, and Vicor. He also developed novel nanoscale and quantum optics technology as a PhD researcher at UC Berkeley. Roger holds a BS from Boston University and a PhD from UC Berkeley, both in electrical engineering.

Presentations

Closing remarks Keynote

Program chairs Ben Lorica and Roger Chen offer closing remarks.

Decentralized data markets for training AI models Session

Blockchain technologies offer new internet primitives for creating open and online data marketplaces. Roger Chen explores how data markets can be constructed and how they offer a shared resource on the internet for AI-based research, discovery, and development.

Friday opening remarks Keynote

Program chairs Ben Lorica and Roger Chen open the second day of keynotes.

Thursday opening remarks Keynote

Program chairs Ben Lorica and Roger Chen open the first day of keynotes.

Unlocking innovation in AI Keynote

Ben Lorica and Roger Chen describe the state of adoption of AI technologies and provide a glimpse into tools and trends that are poised to accelerate innovation and the introduction of new applications.

Chris Coad is director of customer development at Aginity, focusing on helping companies realize the value of reusing their analytics across their organization. Chris has many years of experience operationalizing analytics for companies in the automotive, financial services, and education industries. Previously, he founded and led a connected car company, where he helped a wide range of clients leverage IoT data. Chris holds a BS in management from Purdue University.

Presentations

Hit a home run making baseball decisions using artificial intelligence and machine learning Session

Join Ari Kaplan, Erin LeDell, Chris Coad, and David Kearns to see where AI meets business intelligence, as they explore the latest ML technologies and concepts powering today's decisions, including Hortonworks, Aginity Amp, H2O.ai, IBM Data Science Experience, and more—using real-life baseball data to illustrate the concepts.

Ira Cohen is a cofounder and chief data scientist at Anodot, where he is responsible for developing and inventing the company’s real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has over 12 years of industry experience.

Presentations

Deep learning for time series data Session

Ira Cohen shares a novel approach for building more reliable prediction models by integrating anomalies in them. Arun Kejariwal then walks you through how to marry correlation analysis with anomaly detection, discusses how the topics are intertwined, and details the challenges you may encounter based on production data.

Simon Crosby is the CTO of SWIM Inc. Simon brings an established record of technology industry success, most recently as cofounder and CTO of security technology company Bromium, where he built a highly secure virtualized system to protect applications. Previously, he was the CTO of the Virtualization and Management Division at Citrix, the cofounder and CTO of XenSource (acquired by Citrix), a principal engineer at Intel, and the founder of CPlane, a network-optimization software vendor. Simon has been a tenured faculty member at the University of Cambridge and was named one of InfoWorld’s top 25 CTOs in 2007.

Presentations

Edge intelligence: Machine learning at the enterprise edge Session

Simon Crosby details an architecture for learning on time series data using edge devices, based on the distributed actor model. This approach flies in the face of the traditional wisdom of cloud-based, big-data solutions to ML problems. You'll see that there are more than enough resources at “the edge” to cost-effectively analyze, learn from, and predict from streaming data on the fly.

Jerry Cuomo is vice president of blockchain technologies at IBM, where he and his team demonstrate how blockchain can revolutionize business and redefine companies and economies. Jerry is recognized as one of the most prolific contributors to IBM’s software business, producing products and technologies that have profoundly impacted how the industry conducts commerce over the World Wide Web while dramatically improving the consumer experience. Jerry has exhibited a repeating pattern of breakthrough innovations in software design, engineering, and business strategy across IBM’s most financially successful and industry-recognizable software product offerings. Jerry holds the prestigious title of IBM Fellow—the highest technical position at IBM, with only 300 Fellows having been named in the 106 years of IBM’s existence. Jerry has pioneered emerging technology projects in the areas of the blockchain, API economy, mobile computing, cloud computing, web application servers, integration software, Java, and instant messaging software, filing over 60 US patents across these areas. Jerry is most recognized as one of the founding fathers of WebSphere Software, whose innovations defined WebSphere as the industry-leading application server, currently serving over 80,000 customers. His inventions in web server security, performance, scalability, and availability are the reasons why many of the world’s most visible institutions are able to securely conduct commerce over the web. Jerry’s most visible patent is the first use of the “Someone is typing…” indicator found in instant messaging applications. This invention is used by billions of users around the world every day, via their iPhones, Microsoft’s Messenger, and IBM’s Sametime. In March 2016 and February 2018, Jerry was called upon by the US government as an expert witness to testify to the US Energy and Commerce Committee on Digital Currency and Blockchain. During his testimony, Jerry urged the Obama administration to adopt the blockchain as a primary means to protect citizen identity and enhance national security. His testimony can be seen on YouTube and is often referred to in social media.

Presentations

The blockchain is changing everyday life. Blockchain

Would you believe that the blockchain is changing everyday life? Maybe it’s too soon to make such a bold statement, but as the second half of 2018 rolls on, there's compelling evidence that this is true. Jerry Cuomo explores three examples of live blockchain networks that are poised to change life for the positive for citizens like you and me.

Rob Currie is CTO at the UCSC Genomics Institute. Rob has over 25 years of experience as a senior executive at Silicon Valley early-stage technology companies, including Universal Audio, Dash Navigation/Blackberry, Strangeberry/TiVo, Marimba/BMC, and Digidesign/Avid in fields including distributed systems management, signal processing, and geospatial navigation. Rob holds a BS in EECS from UC Berkeley and an MBA from the University of Chicago Booth School of Business.

Presentations

Sharing clinical data on the blockchain Blockchain

Robert Currie offers an overview of the Cancer Genome Trust, which was developed to enable providers to openly share consented patients’ deidentified health data using Ethereum and IPFS at a clinical-relevant time scale. Robert also discusses a pilot at UCSF that includes genetic, clinical, and imaging patient data.

Adam Cutler is a founding member of IBM Design and one of the first three distinguished designers at the company. He was responsible for the design and build out of the flagship IBM Design Studio in Austin, TX, as well as for the competency, culture, and practices of design and designers at IBM. This includes IBM design thinking, the IBM design language, and IBM design research. Adam’s mission at IBM is driving development of the company’s point of view on the practice of AI design. He recently gave a TED talk on creating meaningful human-machine relationships. Previously, he was the director of user experience design at IBM Interactive Experience, where he led internal initiatives and helped to guide clients—including OPENPediatrics for Boston Children’s Hospital, Nordea, the JFK Museum and Library, Liberty Mutual, Bank of America, Nationwide, Wachovia, L.L.Bean, State Street, American Express, IBM, Segway, Chubb Insurance, and Tiffany & Co.—in the creation of valuable, dynamic, and effective user experiences. Prior to joining IBM, Adam worked with Michael Jordan while at an advertising agency in Chicago and helped to pioneer the first ecommerce transaction from outer space.

Presentations

Forming meaningful relationships between human and machine Session

Here at the dawn of the cognitive era, we must focus our design talents upon a new type relationship with machines—machines that can hold a conversation, interpret nonverbal cues, and draw from vast stores of human knowledge. But what should these relationships look like? Adam Cutler explains why designing for AI requires new considerations and new rules.

Brian Dalessandro is the head of data science at Zocdoc, an online doctor marketplace and booking tool, and an adjunct professor for the NYU Center for Data Science graduate program. Previously, Brian was vice president of data science at online advertising firm Dstillery. A veteran data scientist and leader with over 15 years of experience developing machine learning-driven practices and products, Brian holds several patents and has published dozens of peer-reviewed articles on the subjects of causal inference, large-scale machine learning, and data science ethics. Brian is also the drummer for the critically acclaimed indie rock band Coastgaard.

Presentations

Reverse engineering your AI prototype and the road to reproducibility Session

With the help of better software, cloud infrastructure, and pretrained networks, AI models have become easier to build. But once your solution veers from a common path, hidden challenges in reproducibility and implementation arise. Brian Dalessandro and Chris Smith share their experience and lessons learned while building a computer vision and OCR app for reading and classifying insurance cards.

Banibrata De is a seasoned software engineer in Microsoft’s Algorithms and Data Science Group in Redmond, where he is the engineer for the Data Science Virtual Machine (DSVM) and works on a variety of solutions to help people democratize AI and ML using cutting-edge tools. Previously, he worked with the Windows Defender team to help protect Microsoft customers against various security vulnerabilities and was a performance software engineer for key Microsoft services and products helping to make the end-user experience enjoyable. He holds a degree in computer science from Jadavpur University, Kolkata, India.

Presentations

Distributed deep learning in the cloud: Build an end-to-end application involving computer vision and geospatial data Tutorial

High-resolution land cover maps help quantify long-term trends like deforestation and urbanization but are prohibitively costly and time intensive to produce. Mary Wahl and Banibrata De demonstrate how to use Microsoft’s Cognitive Toolkit and Azure cloud resources to produce land cover maps from aerial imagery by training a semantic segmentation DNN—both on single VMs and at scale on GPU clusters.

Danielle Dean is a principal data scientist lead at Microsoft in the Algorithms and Data Science Group within the Artificial Intelligence and Research Division, where she leads a team of data scientists and engineers building predictive analytics and machine learning solutions with external companies utilizing Microsoft’s Cloud AI Platform. Previously, she was a data scientist at Nokia, where she produced business value and insights from big data through data mining and statistical modeling on data-driven projects that impacted a range of businesses, products, and initiatives. Danielle holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill, where she studied the application of multilevel event history models to understand the timing and processes leading to events between dyads within social networks.

Presentations

How to use transfer learning to bootstrap image classification and question answering (QA) Session

Transfer learning enables you to use pretrained deep neural networks and adapt them for various deep learning tasks (e.g., image classification, question answering, and more). Join Wee Hyong Tok and Danielle Dean to learn the secrets of transfer learning and discover how to customize these pretrained models for your own use cases.

Joshua V. Dillon is a software engineer for research and machine intelligence at Google. His research interests include approximate inference techniques for probabilistic models, uncertainty in machine learning, and designing probabilistic programming tools and languages. He holds a PhD in machine learning from the Georgia Institute of Technology. In his free time, Josh enjoys spending time with his family, cycling, and woodworking.

Presentations

Frontiers of TensorFlow: Space, statistics, and probabilistic ML (sponsored by Google) Session

Join in for two talks on TensorFlow in space and mathematics. Josh Dillon discusses TensorFlow Probablity (TFP), and Wahid Bhimji discusses deep learning for fundamental sciences using high-performance computing.

Armen Donigian is team lead for modeling tools and explainability at ZestFinance. He started his career working on outdoor navigation algorithms using Kalman filters and later transitioned to build assisted GPS point positioning solutions at NASA’s Jet Propulsion Laboratory. After the landing of the Mars Curiosity Rover, he helped build data-driven products at several startups. Armen holds undergraduate and graduate degrees in computer science from UCLA and USC.

Presentations

Explaining machine learning models Session

What does it mean to explain a machine learning model, and why is it important? Armen Donigian addresses those questions while discussing several modern explainability methods, including traditional feature contributions, LIME, and DeepLift. Each of these techniques offers a different perspective, and their clever application can reveal new insights and solve business requirements.

Allison Duettmann is an AI safety researcher at the Foresight Institute, where she conducts research and coordinates the institute’s technical programs. Her research focuses on the reduction of existential risks, especially from artificial general intelligence. At Existentialhope.com, she keeps an index of readings, podcasts, organizations, and people that inspire an optimistic long-term vision for humanity. The index is collaborative and for everyone who wants to improve the world but doesn’t know where to start. Allison speaks and moderates panels on existential risks and existential hope, AI safety, longevity, the blockchain, ethics in technology, and more. Previously, she hosted and planned TEDx talks, panels, workshops, debates, and conferences for governments, companies, think tanks, NGOs, and the public in Germany, France, Colombia, the UK, and the US. Allison holds an MS in philosophy and public policy (summa cum laude) from the London School of Economics, where she developed a moral framework for artificial general intelligence that relies on natural language processing.

Presentations

Executive Briefing: AI safety—Problems, state of the art, and alternatives Session

Allison Duettmann offers an overview of AI philosophy and explains why traditional approaches need updating, because they pave the way for a singleton AI that will hardly be benevolent. Allison then discusses potential alternative AI safety strategies and their shortcomings and shares a brief survey of interesting problems in AI safety and what we can hope for if we get it right.

Kishore Durg is the growth and strategy lead for Accenture Technology Services, where he helps in ventures and acquisitions, technology strategy, and strategic growth initiatives. Kishore also leads the global Accenture Testing Services Group, focusing on testing business strategy, sales, automation, AI, alliances, and platforms.

Presentations

Raising AI to benefit business and society (sponsored by Accenture) Keynote

Much more than just a technological tool, AI has grown to the point where it often has as much influence as the people putting it to use, both within and outside the company. Kishore Durg explains why deploying AI is no longer just about training it to perform a given task. It’s about “raising” it to act as a responsible representative of the business and a contributing member of society.

Teach and test your AI systems (sponsored by Accenture) Session

As AI grows its reach throughout society and makes decisions that affect people, any business looking to capitalize on AI’s potential must also acknowledge its impact. Kishore Durg and Teresa Escrig explain why businesses must teach and test their AI systems to act as responsible representatives so that they reflect business and societal norms of responsibility, fairness, and transparency.

Brian Eberman is an experienced technology professional with over 25 years of experience bringing new technologies and companies to market. Most recently, he was CTO and CEO of Jibo, building the world’s first social robot and bringing it to thousands of people’s homes. Previously, Brian was president and COO of EnglishCentral, a company that pioneered a unique video and speech technology approach to English language learning, and CEO of Avenue100, an online education marketing company (acquired by the Washington Post Company in 2007). Brian brings decades of experience in speech technology, robotics, and using analytics for decision making. This includes over 10 years in speech recognition with a stint as vice president of product management at ScanSoft (now Nuance Communication), the leader in speech technology, and prior research and development experience at SpeechWorks and Digital Equipment. Brian holds multiple patents in speech technology, internet search, and robotics. He has coauthored technical specifications for the IETF. Brian holds a BS, MS, and PhD from MIT.

Presentations

Inside and out: The impact of AI and data on transforming the enterprise Session

Join in for a panel discussion on how AI and data are transforming the enterprise as we know it. Panelists will discuss data, talent, and technology challenges, enterprise adoption barriers, areas of disruption, and talent concerns (enterprises' willingness to pay) as AI turns the enterprise world as we know inside and out.

Jana Eggers is CEO of Nara Logics, a neuroscience-inspired artificial intelligence company providing a platform for recommendations and decision support. A math and computer nerd who took the business path, Jana has had a career that has taken her from a three-person business to 50,000+-person enterprises. She opened the European logistics software offices as part of American Airlines, dove into the internet in ’96 at Lycos, founded Intuit’s corporate Innovation Lab, helped define mass customization at Spreadshirt, and researched conducting polymers at Los Alamos National Laboratory. Her passions are working with teams to define and deliver products customers love, algorithms and their intelligence, and inspiring teams to do more than they thought possible.

Presentations

The wiring diagram of arXiv's AI papers Session

In neuroscience, the wiring diagram of the brain is a connectome. Jana Eggers and Elsie Kenyon built a connectome of the AI/ML "brain" via arXiv papers. They share the results of how papers, topics, keywords, authors, institutions, publication dates, citations, and more are linked and with what strength, offering interesting insights on how the AI research world is connected.

Carlos Escapa is the global AI and ML practice leader of AWS’s Consulting Partner Network. Previously, he was the cofounder and CEO of VirtualSharp Software, where he led the company to a successful exit to Unitrends Inc. (Insight Venture Partners); the general manager of Southern Europe at VMware; vice president of channels at CA Technologies in Europe; and business development director at Sterling Software Japan. Carlos holds an MS in computer science from Virginia Tech and a BS from Illinois State University.

Presentations

Framing business problems as ML problems (sponsored by AWS) Session

Carlos Escapa explains how to identify use cases for ML, shares best practices for framing problems in a way that key stakeholders and senior management can understand and support, and helps you set the stage for delivering successful ML-based solutions to your business.

Teresa Escrig is an AI innovation lead at Accenture Technology. Previously, Teresa was a researcher and professor in AI, including the areas of qualitative modeling, cognitive vision, robotics, and cybersecurity. She is the author of three books and more than 100 research articles and is the recipient of numerous awards.

Presentations

Teach and test your AI systems (sponsored by Accenture) Session

As AI grows its reach throughout society and makes decisions that affect people, any business looking to capitalize on AI’s potential must also acknowledge its impact. Kishore Durg and Teresa Escrig explain why businesses must teach and test their AI systems to act as responsible representatives so that they reflect business and societal norms of responsibility, fairness, and transparency.

Susan Etlinger is an industry analyst at Altimeter. Her research focuses on the impact of artificial intelligence, data and advanced technologies on business and culture and is used in university curricula around the world. Susan’s TED talk, “What Do We Do with All This Big Data?,” has been translated into 25 languages and has been viewed more than 1.2 million times. She is a sought-after keynote speaker and has been quoted in such media outlets as the Wall Street Journal, the BBC, and the New York Times.

Presentations

Executive Briefing: Ethical AI—How to build products that customers will love and trust Session

Susan Etlinger explores how AI fundamentally changes the relationship between people and businesses, lays out its risks and opportunities, and demonstrates emerging best practices for designing customer-centric and ethical products and services.

Andrew Feldman is founder and CEO of Cerebras Systems, a venture-backed stealth-mode startup located in Los Altos, California. Previously, Andrew was founder and CEO of SeaMicro (acquired by AMD for $355 million). A pioneer in energy efficient computation, SeaMicro invented the microserver category and forever changed the trajectory of the server industry by creating a new class of the high-density, energy-efficient servers. Prior to SeaMicro, he was vice president of marketing and product management at Force10 Networks (acquired by Dell for $800 Million) and vice president of corporate marketing and corporate development for Riverstone Networks. Andrew is passionate about building teams that solve hard problems by recognizing emerging opportunities, listening closely to customer requirements, building industry-transforming products, and bringing those products to market in compelling fashion. He is a sought-after advisor to startups and currently serves on the board of directors at Aleveo Energy and on the advisory boards of more than a dozen startups. Andrew is a frequent keynote speaker and guest lecturer at the Stanford Graduate School of Business. He holds a bachelor’s degree and an MBA from Stanford University.

Presentations

Accelerating AI: The path forward Session

Session by Andrew Feldman

Byron is a business leader, strategist and original thinker with a knack for finding creative solutions to thorny problems. His greatest contribution to most engagements is his ability to see all sides of a situation, and develop insights that advance initiatives. Byron has worked as an executive and a consultant in sectors ranging from automotive to financial services, packaged goods to publishing, sporting goods to technology. The common threads running through his experience are an unusually centered enthusiasm, an ability to say hard things in constructive ways, and an applied appreciation for the things that unite us in our humanity.

Presentations

Taming dragons: A breakthrough approach to AI for business leaders Tutorial

Utilizing AI technologies to advance business goals remains one of the most daunting challenges for many business leaders. Beth Partridge, assisted by Nick Paquin and Annie O'Connor, shares a breakthrough approach that bridges the gap between data science and business. Join in to gain a clear understanding of what AI can do for your business and how to go about implementing it.

Alex Ge is a data scientist at SAS, where he focuses on applying machine learning and statistical modeling to solve real-world problems. He is passionate about working with data and making an impact through collaboration and is always striving to learn something new. Alex holds a BS in applied mathematics from Columbia University.

Presentations

Building and deploying AI: A modern platform for the enterprise (sponsored by SAS) Session

AI is an area of fast-paced innovation and a tool that's more accessible than ever for the enterprise, but integrating AI into businesses is not without its challenges. Alex Ge outlines a practical framework for working with AI, from dealing with the data to building and interpreting models to deploying and operationalizing—all while keeping collaboration front and center within the enterprise.

Marina Rose Geldard, more commonly known as Mars, is a final-year computing student from Down Under in Tasmania. Entering the world of technology relatively late as a mature-age student, she has found her place in the world: an industry where she can apply her lifelong love of mathematics and optimization. When she is not busy being the most annoyingly eager student ever, she compulsively volunteers at industry events, dabbles in research, and serves on the executive committee for her state’s branch of the Australian Computer Society (ACS).

Presentations

Learning from video games Session

Video games have used sophisticated AI techniques for decades to drive everything from area design to navigation to enemies to conversation and planning. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent offer an overview of the history of AI in video games and explain how the needs that drove AI advancement in the game development world map to almost-identical problems in the real world.

Alex Gladstein is chief strategy officer at the Human Rights Foundation and vice president of strategy for the Oslo Freedom Forum, where he has served since its inception in 2009. In his work, Alex has connected hundreds of dissidents and civil society groups with business leaders, technologists, journalists, philanthropists, policy makers, and artists to promote free and open societies. Alex’s writing and views on human rights and technology have appeared in media outlets across the world, including the Atlantic, the BBC, CNN, Fast Company, the Guardian, Monocle, the New York Times, NowThis, NPR, Quartz, Time, Wired, the New Republic, and the Wall Street Journal. He has spoken at universities ranging from MIT to Stanford, presented at the European Parliament, and lectured at Singularity University summits from Berlin to Johannesburg. He has also spoken at a range of blockchain events about why Bitcoin and decentralized technology matter for freedom. He currently lives in San Francisco. You can reach him at alex@hrf.org.

Presentations

Why decentralized technology matters Blockchain

Alex Gladstein provides a tour of how technology is being used against us and shows how it just might be the one thing that can save our privacy and freedom.

Zachary Glassman is a data scientist in residence at the Data Incubator. Zachary has a passion for building data tools and teaching others to use Python. He studied physics and mathematics as an undergraduate at Pomona College and holds a master’s degree in atomic physics from the University of Maryland.

Presentations

AI and data science for managers 2-Day Training

Michael Li and Zachary Glassman offer a nontechnical overview of AI and data science. You'll learn basic concepts and vocabulary and develop a framework that will allow you to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

AI and data science for managers (Day 2) Training Day 2

Michael Li and Zachary Glassman offer a nontechnical overview of AI and data science. You'll learn basic concepts and vocabulary and develop a framework that will allow you to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

Dan Golden is the director of machine learning at Arterys, a startup focused on streamlining the practice of medical image interpretation and postprocessing. Previously, he founded a machine learning team at CellScope that used the then-nascent field of deep learning to diagnose ear disease and streamline the process of recording ear exams at home and was a postdoc at Stanford, focusing on using machine learning to predict outcomes and disease characteristics in cancer patients. He holds a PhD in electrical engineering from Stanford.

Presentations

Lung cancer detection and segmentation using deep learning Session

Modern radiological lung cancer screening is an entirely manual process, leading to high costs and inter-reader variability. Daniel Golden offers an overview of a deep learning-based system that automatically detects and segments lung nodules in lung CT exams and explains how it was tested for safety and efficacy. The system is FDA cleared and segments nodules as accurately as a clinician.

Danny Goodman is founder of Switchback Ventures, a company building deep learning and data science products for enterprises like Prudential, McDonald’s, eBay, Oliver Wyman, and J.Crew. Previously, Danny was vice president of platform at MetaMind (Salesforce Research), where he helped create a deep learning platform that enabled thousands of customers to train and deploy their own state-of-the-art text and image classifiers, and director of data science at MetroMile, where he built trip understanding algorithms enabling the only per-mile insurance in the US as well as a variety of driver services like street sweeping alerts, saving drivers millions in avoided tickets, gas price alerts saving $200/driver, and car maintenance advice. Danny studied mathematics at Harvard and machine learning at MIT and Stanford. He has authored four publications and six patents. He’s also an amateur chess player and pianist.

Presentations

Reinforcement learning and the future of software Session

Danny Goodman discusses reinforcement learning and the future of software.

Martin Görner works in developer relations at Google, where he focuses on parallel processing and machine learning. Passionate about science, technology, coding, algorithms, and everything in between, Martin’s first role was in the Computer Architecture Group at STMicroelectronics. He also spent 11 years shaping the nascent ebook market, starting at Mobipocket, which later became the software part of the Amazon Kindle and its mobile variants. He holds a degree from Mines Paris Tech.

Presentations

TensorFlow, deep learning, and modern convolutional neural nets, without a PhD (sponsored by Google) Session

Martin Görner explores the newest developments in image recognition and convolutional neural network architectures and shares tips, engineering best practices, and pointers to apply these techniques in your projects. No PhD required.

Sean Gourley is founder and CEO of Primer. Previously, he was cofounder and CTO of augmented intelligence company Quid and worked on self-repairing nanocircuits at NASA’s Ames Research Center. He also served as a political advisor, briefed USCENTCOM at the Pentagon, and addressed the United Nations in Vienna. Sean sits on the Knight Commission, serves on the board of directors at Anadarko, and is a TED fellow. He is also a two-time New Zealand track and field champion. Sean holds a PhD in physics from Oxford, where his research as a Rhodes Scholar focused on complex systems and the mathematical patterns underlying modern war. This research was published on the cover of Nature.

Presentations

Building machines that can read and write Session

Technology has opened up access to more information than ever before, but it’s still on humans to turn that data into knowledge. To solve this problem, organizations are turning to AI and natural language processing to augment human analysts. Sean Gourley explores how the world’s largest organizations use AI to summarize thousands of documents and scale human analysts.

Manish Goyal is the director and global leader for the Artificial Intelligence (AI) Practice in IBM’s Global Business Services Group, where he and his team are responsible for bringing the best AI solutions to help enterprises transform their businesses and capture the tremendous value from the disruption AI offers. Manish is also responsible for the AI offerings, practice management, and Global P&L. Previously, Manish held various product management roles in IBM’s Watson businesses, including serving as director of product management for the machine learning and data science platform that helped enterprises simplify their data science and AI workflows, improving the productivity of their data scientists and AI developers; leading the team that created the Watson Platform offering that provides developers access to cognitive services (APIs) for natural language processing, speech, vision, conversation, and discovery; and leading the creation of Watson products for the healthcare industry—Watson Oncology and Watson Clinical Trial Matching—working closely with Memorial Sloan Kettering Cancer Center and the Mayo Clinic. Manish holds an MBA from the NYU Stern School of Business and a BS in computer science from Pune University, India.

Presentations

Four success factors for building your AI business journey (sponsored by IBM Watson) Keynote

AI is real and has immense value potential for enterprises. However, there is a lot of hype and confusion around AI, creating a critical need for every business to be thoughtful about developing the right strategy and vision for AI within the organization. Join Manish Goyal to explore four success factors for an AI journey and learn how you can best unlock the value of enterprise AI.

Making AI a Killer App for your Data: A Practical Guide (sponsored by IBM Watson) Session

To help enterprises formulate their strategies for actionable and effective use of AI, HfS and IBM have jointly developed a practical guide to starting your AI journey, leveraging insights from IBM’s Institute for Business Value (IBV) and recent HfS research, as well as real-world experiences, gleaned from interviews with clients and field practitioners.

Goodman Xiaoyuan Gu is head of machine learning architecture at Boston-based Cogito, where he leads operations of large-scale real-time augmented intelligence platform. Previously, he headed marketing data engineering at Atlassian and was vice president of technology at CPXi, director of engineering at Dell, and general manager at Amazon, where he built marketing, analytics and machine learning applications. He has served on technical program committees of two IEEE flagship conferences and is the author of over a dozen academic publications in high-profile IEEE and ACM journals and conferences. Goodman holds a degree in engineering and management from MIT.

Presentations

Leaving no one behind: Make equal access to social good possible with deep learning Session

Over 400M people worldwide have some sort of speech or hearing disorder that prevents them from participating in the job market. Goodman Gu offers an overview of Stride4All, an initiative using AI to open work up for disabled people and empower them for teamwork, and showcases a prototype that uses deep learning and computer vision technologies for gesture recognition of American Sign Language.

Priya Gupta is a software engineer on the TensorFlow team at Google, where she works on making it easier to run TensorFlow in a distributed environment. She is passionate about technology and education and wants machine learning to be accessible to everyone. Previously, she worked at Coursera and on the mobile ads team at Google.

Presentations

Distributed TensorFlow training using Keras and Kubernetes Session

Magnus Hyttsten and Priya Gupta demonstrate how to perform distributed TensorFlow training using the Keras high-level APIs. They walk you through TensorFlow's distributed architecture, how to set up a distributed cluster using Kubeflow and Kubernetes, and how to distribute models created in Keras.

Sandeep Gupta is a product manager at Google, where he helps develop and drive the roadmap for TensorFlow—Google’s open source library and framework for machine learning—for supporting machine learning applications and research. His current focus is on improving TensorFlow’s usability and driving adoption in the community and enterprise. Sandeep is excited about how machine learning and AI are transforming our lives in a wide variety of ways, and he works with the Google team and external partners to help create powerful, scalable solutions for all. Previously, Sandeep was the technology leader for advanced imaging and analytics research and development at GE Global Research with specific emphasis on medical imaging and healthcare analytics.

Presentations

Building AI with TensorFlow: An Overview (sponsored by Google) Session

TensorFlow is one of the world’s biggest open source projects, and it continues to grow in adoption and functionality. Laurence Moroney, Edd Wilder-James, and Sandeep Gupta share major recent developments and highlight some future directions. Join in to learn how you can become more involved in the TensorFlow community.

Sharad Gupta is the director of enterprise architecture at Blue Shield of California, where he is responsible for the strategic direction, technology strategy, technology selection, and architectures for business transformation and innovation initiatives. Sharad is also part of the adjunct faculty at the University of California, Davis, in the Master of Science in Business Analytics (MSBA) program and teaches data design and machine learning. Sharad is also a founder of LittleTechMasters that is a nonprofit grassroots project focused on educating kids about technology in local communities. Sharad holds a BS in computer science from National Institute of Technology, Allahabad, India, and an MBA with a focus on technology management and marketing from the University of California, Davis.

Presentations

Executive Briefing: A multichannel chatbot strategy Session

AI-powered chatbots are increasingly becoming viable solutions for customer service use cases. Technology leaders must consider adopting a multichannel chatbot strategy to avoid siloed chatbot solutions. Sharad Gupta shares a framework to ensure long-term strategic investment in chatbots.

Mark Hamilton is a software engineer in Microsoft’s Azure Machine Learning Group in Cambridge, MA. He works on integrating the deep learning framework CNTK with the distributed computing framework Spark. Mark studied physics, mathematics, and automated theorem proving at Yale University. His current academic research focuses mainly on deep learning, unsupervised learning, and NLP.

Presentations

Distributed deep domain adaptation for automated poacher detection (sponsored by Microsoft) Session

Mark Hamilton shares a novel deep learning approach for creating a robust object detection network for use in an infrared, UAV-based poacher recognition system.

Kristian Hammond is Narrative Science’s chief scientist and a professor of computer science and journalism at Northwestern University. His research has been primarily focused on artificial intelligence, machine-generated content, and context-driven information systems. He currently sits on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). Kris was also named 2014 innovator of the year by the Best in Biz Awards. He holds a PhD from Yale.

Presentations

CANCELLED: Bringing AI into the enterprise Tutorial

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making.

Mark Hammond is cofounder and CEO at Bonsai. Mark has a deep passion for understanding how the mind works and has been thinking about AI throughout his career. He has held positions at Microsoft and numerous startups and in academia, including turns at Numenta and in the Yale Neuroscience Department. He holds a degree in computation and neural systems from Caltech.

Presentations

Reinforcement learning unchained: How to leverage machine teaching to build AI into complex, real-world systems Session

Building complex, real-world reinforcement learning systems requires leveraging techniques such as curriculum learning, hierarchical RL, and reward shaping. Mark Hammond explores many of these techniques and illustrates how they can be effectively combined into a comprehensive machine teaching program.

Mike Henry is cofounder and CEO at Mythic, an AI hardware startup based in Redwood City, CA, and Austin, TX. Under his leadership, Mythic has raised $56M in investment from top-tier VCs and developed novel chip technology that is up to 100 times better than incumbents. Mike holds both a BS and a PhD in computer engineering from Virginia Tech.

Presentations

AI demand is highly elastic: How cost-effective AI inference hardware will open massive markets Session

As prices drop, new markets open up. AI inference will likely follow the same trends as general purpose compute or storage, and the market for AI hardware and software stacks could approach $100B in the next 10 years. Michael Henry dives into AI innovation at a hardware and software level.

Joel Hestness is a systems research scientist at Baidu Research Silicon Valley AI Lab (SVAIL). He studies the scaling characteristics of machine and deep learning applications and techniques to scale out model training runs on large-scale clusters. His prior research focused on general-purpose GPU microarchitecture and memory hierarchies to improve programmability, performance, and energy efficiency in heterogeneous processors. Joel contributes to gem5-gpu, gem5, and TensorFlow. He holds a PhD in computer architecture from the University of Wisconsin-Madison.

Presentations

Your deep learning applications want scale (and how you can support them) Session

Deep learning (DL) creates impactful advances following a virtuous recipe: a model architecture search, creating large training datasets, and scaling computation. Joel Hestness discusses research done by Baidu Research's Silicon Valley AI Lab on new model architectures and features for speech recognition (Deep Speech 3), speech generation (Deep Voice 3), and natural language processing.

Drew Hodun is an ML specialist on the Google Cloud team, where he advises financial, autonomous, and tech customers implementing cutting-edge ML use cases and systems on Google Cloud and in hybrid environments. His work ranges from operationalizing ML to TensorFlow to GPU/TPU perf tuning.

Presentations

End-to-end machine learning with TensorFlow on GCP Day (sponsored by Google Cloud) Event

In this day-long presentation, you'll walk through the process of building a complete machine learning pipeline, from ingest and exploration to training, evaluation, deployment, and prediction.

Magnus Hyttsten is a developer advocate for TensorFlow at Google, where he works on developing the TensorFlow product. A developer fanatic, Magnus is an appreciated speaker at major industry events such as Google I/O, the AI Summit, AI Conference, ODSC, GTC, QCon, and others on machine learning and mobile development. Right now, he’s focusing on reinforcement learning models and making model inference effective on mobile.

Presentations

Distributed TensorFlow training using Keras and Kubernetes Session

Magnus Hyttsten and Priya Gupta demonstrate how to perform distributed TensorFlow training using the Keras high-level APIs. They walk you through TensorFlow's distributed architecture, how to set up a distributed cluster using Kubeflow and Kubernetes, and how to distribute models created in Keras.

Forrest Iandola is CEO of DeepScale, a company focused entirely on building perception systems for automated vehicles, drawing on the advances in scalable training and efficient implementation of deep neural networks that emerged from Forrest’s graduate research. DeepScale has a number of engagements with automakers and automotive suppliers. Forrest holds a PhD in electrical engineering and computer science from UC Berkeley, where his research focused on deep neural networks.

Presentations

Executive Briefing: How to develop a full stack deep learning team Session

Now more than ever, success in AI requires expertise in multiple disciplines, including big data, efficient software, and novel models and algorithms. Forrest Iandola shares an approach for developing a full stack AI team that can use all of these diverse skills to execute on industrial-scale AI problems.

Anand Iyer is a product manager for the Google Cloud Platform, where he focuses on delivering industry-leading solutions to build production machine learning pipelines. He’s particularly passionate about the intersection of data, machine learning, and open source. Previously, he built and delivered big data and machine learning platforms at Cloudera and LinkedIn. Anand holds a master’s degree in computer science from Stanford and a bachelor’s degree from the University of Arizona.

Presentations

Create customer value with Google Cloud AI (sponsored by Google Cloud) Session

Google extends the "AI-first" DNA to all enterprises via the cloud. Anand Iyer walks you through the Google Cloud AI platform, from managed services to specialized ML accelerators, and shares examples of how businesses have leveraged the platform to overcome challenges in delivering customer value.

Ankit Jain is a senior data scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of deep learning methods to a variety of Uber’s problems, ranging from forecasting and food delivery to self-driving cars. Previously, he held a variety of data science roles at Bank of America, Facebook, and other startups. Ankit holds an MFE from UC Berkeley and a BS from IIT Bombay (India). Outside of his job, he likes to mentor students in data science, run marathons, and travel.

Presentations

Achieving personalization with LSTMs Session

Personalization is a common theme in social networks and ecommerce businesses. Personalization at Uber involves an understanding of how each driver and rider is expected to behave on the platform. Ankit Jain explains how Uber employs deep learning using LSTMs and its huge database to understand and predict the behavior of each and every user on the platform.

Lukasz Kaiser is a staff research scientist on the Google Brain team at Google, where he works on fundamental aspects of deep learning and natural language processing. He has codesigned state-of-the-art neural models for machine translation, parsing, and other algorithmic and generative tasks and coauthored the TensorFlow system and the Tensor2Tensor library. Previously, Lukasz was a tenured researcher at University Paris Diderot, where he worked on logic and automata theory. He holds a PhD from RWTH Aachen University and an MSc from the University of Wroclaw, Poland.

Presentations

Tensor2Tensor (sponsored by Google) Session

Lucasz Kaiser offers an overview of Tensor2Tensor, a library of deep learning models and datasets that facilitates the creation of state-of-the art models for a wide variety of ML applications, such as translation, parsing, image captioning, and more, enabling the exploration of various ideas much faster than previously possible.

Ari Kaplan is principal enterprise analytics consultant at Aginity. A leading figure in analytics, technology, and business, Ari is also known for his “Moneyball” background, which includes creating and leading the Analytics Department for the Chicago Cubs and helping transform the championship organization. He was president of the Independent Oracle Users Group for the world’s largest business software firm during its key acquisitions of MySQL, Peoplesoft, and Siebel. Caltech named him “Alumni of the Decade” for his innovation in analytical solutions.

Presentations

Hit a home run making baseball decisions using artificial intelligence and machine learning Session

Join Ari Kaplan, Erin LeDell, Chris Coad, and David Kearns to see where AI meets business intelligence, as they explore the latest ML technologies and concepts powering today's decisions, including Hortonworks, Aginity Amp, H2O.ai, IBM Data Science Experience, and more—using real-life baseball data to illustrate the concepts.

Max Katz is a solutions architect at NVIDIA, where he supports the US Department of Energy in deploying GPU-accelerated supercomputers and provides assistance to the scientific user community on the use of these systems. His background is in computational astrophysics, a field he still actively does research in. Max holds a PhD in physics from Stony Brook University and a BS and MS in physics from Rensselaer Polytechnic Institute.

Presentations

NVIDIA Deep Learning Institute: AI for computer vision and digital content creation 2-Day Training

Max Katz and Eric Levin walk you through the fundamentals of deep learning, from training neural networks to using results to improve performance and capabilities. You'll then apply your newfound knowledge to digital content creation and game development, as you create digital assets with deep learning. No prior deep learning experience is required.

NVIDIA Deep Learning Institute: AI for computer vision and digital content creation (Day 2) Training Day 2

Max Katz and Eric Levin walk you through the fundamentals of deep learning, from training neural networks to using results to improve performance and capabilities. You'll then apply your newfound knowledge to digital content creation and game development, as you create digital assets with deep learning. No prior deep learning experience is required.

David Kearns is an offering manager on the analytics ecosystem team at IBM, focusing on ISVs in the data science market. Previously, David was an offering manager for IBM Netezza and IBM Industry Models, where he worked with large banks and insurance companies such as Bank of America, Lloyds, Raymond James, Citi, Bank of Montreal, and IF Insurance. David has many years of experience in UML, SOA architecture, and web services. From the ages of 12 to 22, David represented Ireland in track and field and was an Irish schools champion in the 400 m and 400 m hurdles. David holds a BSc and an MBA from Dublin City University and is currently working on an MSc in multimodal human language technology at the Institute of Technology Blanchardstown.

Presentations

Hit a home run making baseball decisions using artificial intelligence and machine learning Session

Join Ari Kaplan, Erin LeDell, Chris Coad, and David Kearns to see where AI meets business intelligence, as they explore the latest ML technologies and concepts powering today's decisions, including Hortonworks, Aginity Amp, H2O.ai, IBM Data Science Experience, and more—using real-life baseball data to illustrate the concepts.

Until recently, Arun Kejariwal was a statistical learning principal at Machine Zone (MZ), where he led a team of top-tier researchers and worked on research and development of novel techniques for install and click fraud detection and assessing the efficacy of TV campaigns and optimization of marketing campaigns. In addition, his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection. Previously, Arun worked at Twitter, where he developed and open-sourced techniques for anomaly detection and breakout detection. His research includes the development of practical and statistically rigorous techniques and methodologies to deliver high-performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been presented at international conferences and published in peer-reviewed journals.

Presentations

Deep learning for time series data Session

Ira Cohen shares a novel approach for building more reliable prediction models by integrating anomalies in them. Arun Kejariwal then walks you through how to marry correlation analysis with anomaly detection, discusses how the topics are intertwined, and details the challenges you may encounter based on production data.

Mayank Kejriwal is a computer scientist at the USC Information Sciences Institute, where he conducts research on the IARPA HFC, and DARPA LORELEI, CauseEx, D3M, and MEMEX projects, the latter of which has been covered by 60 Minutes, Forbes, Scientific American, the Wall Street Journal, the BBC, and Wired. He holds a PhD from the University of Texas at Austin. His dissertation, "Populating a Linked Data Entity Name System,” received the Best Dissertation Award by the Semantic Web Science Association in 2017. Mayank is currently coauthoring a textbook on knowledge graphs.

Presentations

Fighting human trafficking with AI Session

Human trafficking is a form of modern-day slavery. Online sex advertisement activity on portals like Backpage provide important clues that, if harnessed and analyzed at scale, can help resource-strapped law enforcement crack down on trafficking activity. Mayank Kejriwal details an AI architecture called DIG that law enforcement have used (and are using) to combat sex trafficking.

Eli-Shaoul Khedouri is CEO of Intuition Machines, which provides AI/ML products and services to some of the largest companies in the world. Its new ML annotation platform, hCaptcha, recently launched on the Ethereum blockchain, powered by the HUMAN Protocol. A recognized leader in AI who has applied machine intelligence at scale in a wide variety of industries over the past decade, Eli has cofounded four tech companies as CTO or CEO, applied his expertise in venture capital at AI/ML-focused firms like Array Ventures, and serves on a variety of advisory boards for early- and mid-stage companies.

Presentations

Growing two-sided markets with blockchain tech Blockchain

Today's blockchain technology has both great promise and severe limitations. Eli-Shaoul Khedouri details an immediate practical application of blockchain tech in two-sided market growth for AI and explores the unique advantages it offers when compared to other models.

Anirudh Koul is a senior data scientist at Microsoft Research and the founder of Seeing AI, a talking camera app for the blind community. Anirudh brings over a decade of production-oriented applied research experience on petabyte-scale datasets, with features shipped to about a billion people. An entrepreneur at heart, he has run ministartup teams within Microsoft, prototyping ideas using computer vision and deep learning techniques for augmented reality, productivity, and accessibility and building tools for communities with visual, hearing, and mobility impairments. A regular at hackathons, Anirudh has won close to three dozen awards, including top-three finishes for four years consecutively in the world’s largest private hackathon, with 18,000 participants. Some of his recent work, which IEEE has called “life changing,” has been showcased at a White House event, on Netflix, and in National Geographic and received awards from the American Foundation for the Blind and Mobile World Congress.

Presentations

Deep learning on mobile: The how-to guide Session

Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially for computer vision. Anirudh Koul explains how to bring the power of convolutional neural networks and deep learning to memory- and power-constrained devices like smartphones.

Nick Kreeger is a software engineer on the TensorFlow team at Google working on TensorFlow.js. Nick has 10+ years of industry experience in the desktop, mobile, server, and client-side areas.

Presentations

TensorFlow for JavaScript (sponsored by Google) Session

TensorFlow.js is the recently released JavaScript version of TensorFlow that runs in the browser and Node.js. Nick Kreeger and Ping Yu offer an overview of the TensorFlow.js ML framework and demonstrate how to perform the complete machine learning workflow, including training, client-side deployment, and transfer learning.

Danielle Krettek is the founder and principal of Google’s Empathy Lab. An award-winning creative force with work across design, technology, film, art, architecture, and social impact for Nike (W+K), Apple, and Google X, Danielle has brought vibrant humanity, beauty, and emotion to products for 18 years. In 2015, she launched Google’s Empathy Lab to design more emotionally attuned and human-kind experiences. She believes the future is feeling and leads Google AI’s design team with a mission to bring deep humanity to deep learning. Her personal sources of rocket fuel are modern art, film, neuroscience, indigenous medicine, and sixties culture. Naturally, her spirit animal is a cross between Stevie Nicks, Spike Jonze, and Oprah.

Presentations

Empathic design for AI: The future is feeling. Session

We now live in the age of assistive and AI companions, where everything is coming alive. Danielle Krettek demonstrates how to design for the "invisible" emotional layer of experience that marks this new wave of technology.

Besir Kurtulmus is an algorithm engineer at Algorithmia helping making complicated things simpler by developing machine learning algorithms that are designed to scale. He also helps maintain the development environment for running machine learning models in the Algorithmia marketplace. Besir believes that machine intelligence will have a huge impact on our lives in the days to come and hopes to have a defining role in shaping this new future.

Presentations

Trustless machine learning contracts: Evaluating and exchanging machine learning models on the Ethereum blockchain Session

Machine learning algorithms are being developed and improved at an incredible rate but are not necessarily accessible to the broader community. A. Besir Kurtulmus offers an overview of DanKu, a new blockchain-based protocol for evaluating and purchasing ML models on a public blockchain such as Ethereum that provides everyone access to high-quality, objectively measured machine learning models.

Roy Labban is the director of computer modeling and simulation in the Information Systems Department at Consolidated Contractors Company (CCC), which is ranked among the top 20 international contractors in 2017 by ENR. Roy has 20+ years of experience in software engineering and database application development, business intelligence and analytics, and computer modeling and simulation. Roy is the cofounder and managing partner of a boutique consulting firm focused on delivering business intelligence and analytics for higher education enrollment management. Roy is also the founder and director of a postgraduate coding bootcamp diploma program focusing on new technologies such as the blockchain, artificial intelligence, machine learning, and mobile apps. Roy serves as a member of the Industry Advisory Board of the Computer Science Program (ABET Accredited) at the American University of Science and Technology. He is also a part-time university instructor teaching graduate level courses on computer simulation and machine learning. Roy holds a PhD in construction engineering and management from the University of Alberta and a BE in computer and communications engineering from the American University of Beirut.

Presentations

Machine learning for optimizing construction Session

Estimating the performance of heavy earth-moving equipment on large construction projects is a complex task that can be riddled with uncertainty. Ramzi Roy Labban details how CCC uses machine learning, leveraging large datasets of actual performance of trucks on construction sites, to more accurately predict future performance and allow the company to make realistic performance assumptions.

Danny Lange is vice president of AI and machine learning at Unity Technologies, where he leads multiple initiatives around applied artificial intelligence. Previously, Danny was head of machine learning at Uber, where he led the efforts to build a highly scalable machine learning platform to support all parts of Uber’s business, from the Uber app to self-driving cars; general manager of Amazon Machine Learning, where he provided internal teams with access to machine intelligence and launched an AWS product that offers machine learning as a cloud service to the public; principal development manager at Microsoft, where he led a product team focused on large-scale machine learning for big data; CTO of General Magic, Inc.; and founder of his own company, Vocomo Software, where he worked on General Motor’s OnStar Virtual Advisor, one of the largest deployments of an intelligent personal assistant until Siri. Danny started his career as a computer scientist at IBM Research. He is a member of ACM and IEEE Computer Society and has numerous patents to his credit. Danny holds an MS and PhD in computer science from the Technical University of Denmark.

Presentations

On the road to artificial general intelligence Session

Danny Lange discusses the role of intelligence in biological evolution and learning and demonstrates why a game engine is the perfect virtual biodome for AI’s evolution. You'll discover how the scale and speed of simulations is changing the game of AI while learning about new developments in reinforcement learning.

Jason Laska is the head of engineering at Clara Labs. Previously, he spearheaded the computer vision program at Dropcam (acquired by Google in 2014), developing massive scale online vision systems for the product. Jason holds a PhD in electrical engineering from Rice University, where he made contributions to inverse problems, dimensionality reduction, and optimization. He briefly dabbled in publishing as a cofounder and editor of Rejecta Mathematica, a publication for previously rejected mathematics articles.

Presentations

Speed versus specificity: Designing text annotation tasks for the people and algorithms that drive human-in-the-loop (HIL) products Session

Clara’s human-in-the-loop scheduling service combines the precision of machine intelligence and the judgement of an expert team. Jason Laska explores the trade-offs between text annotations defined for fast data entry and those meant solely for training machine learning models, using the application of DateTime text as it pertains to meeting-attendee availability to guide the discussion.

Erin Ledell is the chief machine learning scientist at H2O.ai, the company that created the open source distributed machine learning platform H2O. Previously, she was the principal data scientist at Wise.io (acquired by GE Digital in 2016) and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc. Erin holds a PhD from the University of California, Berkeley, where her research focused on scalable machine learning and statistical computing, as well as a BS and MA in mathematics.

Presentations

Hit a home run making baseball decisions using artificial intelligence and machine learning Session

Join Ari Kaplan, Erin LeDell, Chris Coad, and David Kearns to see where AI meets business intelligence, as they explore the latest ML technologies and concepts powering today's decisions, including Hortonworks, Aginity Amp, H2O.ai, IBM Data Science Experience, and more—using real-life baseball data to illustrate the concepts.

Kai-Fu Lee is the chairman and CEO of Sinovation Ventures and president of the company’s Artificial Intelligence Institute. Sinovation Ventures is a leading venture capital firm, managing US$1.7 billion dual currency investment funds, that is focusing on developing the next generation of Chinese high-tech companies. Kai-Fu has been in artificial intelligence research, development, and investment for more than 30 years. Previously, he was the president of Google China and held executive positions at Microsoft, SGI, and Apple. He founded Microsoft Research China, which was named as the hottest research lab by MIT Technology Review. Later renamed Microsoft Research Asia, this institute trained the great majority of AI leaders in China, including CTOs or AI heads at Baidu, Tencent, Alibaba, Lenovo, Huawei, and Haier. At Apple, he led AI projects in speech and natural language, which were featured on Good Morning America and the front page of the Wall Street Journal. Kai-Fu has authored 10 US patents and more than 100 journal and conference papers. He is a fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the author of seven best-selling books in Chinese. His latest, AI Superpowers, launches internationally in the fall of 2018. Kai-Fu has over 50 million followers on social media. He holds a bachelor’s degree in computer science from Columbia University and a PhD from Carnegie Mellon University, as well as honorary doctorate degrees from both Carnegie Mellon and the City University of Hong Kong.

Presentations

China: AI superpower Keynote

With the US leading the AI revolution for decades, it would almost seem inconceivable that China could catch up. But China is rapidly catching up with the US in AI applications. Kai-Fu Lee discusses the five key factors that enabled this rapid ascension: tenacious entrepreneurs, speed and execution, tremendous capital, a big market and bigger data, and techno-utilitarian government policies.

Fireside chat with Tim O'Reilly and Kai-Fu Lee Keynote

Fireside chat with Tim O'Reilly and Kai-Fu Lee

Q&A with Kai Fu Lee Session

Q&A with Kai Fu Lee

Wilson Lee is a senior software engineer in the AI CTO Office at Microsoft, where he works with teams to envision and innovate new end-to-end experiences that illustrate what is possible with the present and the future of Microsoft AI. Wilson strongly believes that story-driven innovation paired with great software architecture can create real change in the world, making the impossible possible. He holds a bachelor of computer science from the University of Waterloo.

Presentations

Building intelligent mobile applications in healthcare Tutorial

Xiaoyong Zhu, Gheorghe Iordanescu, Wilson Lee, and Ivan Tarapov walk you through building a deep learning model and intelligent applications on edge devices running iOS, Android, and Windows, using a working example that helps clinicians in areas with less access to radiologists identify possible lung diseases.

Eric Levin is a virtual reality developer at NVIDIA, where he works on projects ranging from meditation and anxiety relief for hospitalized pediatric patients and a learn-to-DJ app to social VR dancing and painting experiences. He’s fascinated by the vast potential of the creations that will emerge and evolve as a result of humans and AIs playing and working in partnership together ever more intimately and gracefully.

Presentations

NVIDIA Deep Learning Institute: AI for computer vision and digital content creation 2-Day Training

Max Katz and Eric Levin walk you through the fundamentals of deep learning, from training neural networks to using results to improve performance and capabilities. You'll then apply your newfound knowledge to digital content creation and game development, as you create digital assets with deep learning. No prior deep learning experience is required.

NVIDIA Deep Learning Institute: AI for computer vision and digital content creation (Day 2) Training Day 2

Max Katz and Eric Levin walk you through the fundamentals of deep learning, from training neural networks to using results to improve performance and capabilities. You'll then apply your newfound knowledge to digital content creation and game development, as you create digital assets with deep learning. No prior deep learning experience is required.

Sergey Levine is a professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. His research focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms, and includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more. Applications of this work include autonomous robots and vehicles and computer vision and graphics. Sergey’s work has been featured in many popular press outlets, including the New York Times, the BBC, MIT Technology Review, and Bloomberg Business. Sergey holds a BS, MS, and PhD in computer science, all from Stanford University.

Presentations

Deep reinforcement and meta-learning: Building flexible and adaptable machine intelligence Session

Sergey Levine shares techniques in reinforcement learning that allow you to tackle sequential decision-making problems that arise across a range of real-world deployments of artificial intelligence systems and explains how emerging technologies in meta-learning make it possible for deep learning systems to learn from even small amounts of data.

Tianhui Michael Li is the founder and CEO of the Data Incubator. Michael has worked as a data scientist lead at Foursquare, a quant at D.E. Shaw and JPMorgan, and a rocket scientist at NASA. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves. He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.

Presentations

AI and data science for managers 2-Day Training

Michael Li and Zachary Glassman offer a nontechnical overview of AI and data science. You'll learn basic concepts and vocabulary and develop a framework that will allow you to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

AI and data science for managers (Day 2) Training Day 2

Michael Li and Zachary Glassman offer a nontechnical overview of AI and data science. You'll learn basic concepts and vocabulary and develop a framework that will allow you to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

Lydia T. Liu is a PhD student in computer science at the University of California, Berkeley, where she is advised by Moritz Hardt and Michael I. Jordan. She is affiliated with RISELab and BAIR. Her research interest is designing machine learning algorithms that have reliable and robust performance guarantees and positive long-term societal impact.

Presentations

Delayed impact of fair machine learning

Lydia Liu discusses the results of research on how static fairness criteria interact with temporal indicators of well-being. These results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria and suggest a range of new challenges and trade-offs.

Shaoshan Liu is the cofounder and chairman of PerceptIn, a company working on developing a next-generation robotics platform. Previously, he worked on autonomous driving and deep learning infrastructure at Baidu USA. Shaoshan holds a PhD in computer engineering from the University of California, Irvine.

Presentations

Enabling affordable but reliable autonomous driving Session

Shaoshan Liu explains how PerceptIn built a reliable autonomous vehicle with a total cost under $10,000.

Ben Lorica is the chief data scientist at O’Reilly Media. Ben has applied business intelligence, data mining, machine learning, and statistical analysis in a variety of settings, including direct marketing, consumer and market research, targeted advertising, text mining, and financial engineering. His background includes stints with an investment management company, internet startups, and financial services.

Presentations

Closing remarks Keynote

Program chairs Ben Lorica and Roger Chen offer closing remarks.

Friday opening remarks Keynote

Program chairs Ben Lorica and Roger Chen open the second day of keynotes.

Thursday opening remarks Keynote

Program chairs Ben Lorica and Roger Chen open the first day of keynotes.

Unlocking innovation in AI Keynote

Ben Lorica and Roger Chen describe the state of adoption of AI technologies and provide a glimpse into tools and trends that are poised to accelerate innovation and the introduction of new applications.

Hagay Lupesko is part of the deep learning leadership team at Amazon Web Services, and currently works to democratize Artificial Intelligence and Deep Learning through cloud services and open source projects such as MXNet and ONNX. He has been busy building software for the past 15 years, and still enjoys every bit of it (literally)! He engineered and shipped products across various domains: from 3D cardiac imaging with real time in-vessel tracking, through semi-conductors fab systems that measures structures the size of molecules, and up to web-scale systems with global distribution.

Presentations

Machine learning in the cloud Keynote

AWS puts machine learning in reach of every developer and data scientist. Matt Wood explores key trends in machine learning, the importance of designing models for scale, and the impact that machine learning innovation has had on startups and enterprises alike.

Jennifer Marsman is the principal software engineer for Microsoft’s AI for Earth Group, where she uses data science, machine learning, and artificial intelligence to aid with clean water, agriculture, biodiversity, and climate change. She has been featured in Bloomberg for her work using EEG and machine learning to perform lie detection. Previously, Jennifer was a software developer in Microsoft’s Natural Interactive Services Division, where she authored two patents related to search and data mining algorithms. She has also held positions with Ford Motor Company, National Instruments, and Soar Technology. Since 2016, Jennifer has been recognized as one of the top 100 most influential individuals in artificial intelligence and machine learning by Onalytica, reaching the #2 slot in 2018, and in 2009 was chosen as the “techie whose innovation will have the biggest impact” by X-OLOGY for her work with GiveCamps, a weekend-long event where developers code for charity. She has also received many honors from Microsoft, including the Best in Role award for technical evangelism, Central Region Top Contributor Award, Heartland District Top Contributor Award, DPE Community Evangelist Award, CPE Champion Award, MSUS Diversity and Inclusion Award, Gold Club, and Platinum Club. Jennifer is a frequent speaker at software development conferences around the world. She holds a bachelor’s degree in computer engineering and a master’s degree in computer science and engineering from the University of Michigan in Ann Arbor, where she specialized in artificial intelligence and computational theory. To learn more, check out her blog.

Presentations

AI for Earth: Using machine learning to monitor, model, and manage natural resources Session

Microsoft's AI for Earth team helps NGOs apply AI to challenges in conservation biology and environmental science. Jennifer Marsman outlines Microsoft’s objectives for AI for Earth and highlights recent successes in applying AI to agriculture, poacher detection, animal identification in camera trap and citizen scientist photography, and more.

David Martinez is associate division head in the Cyber Security and Information Sciences Division at the MIT Lincoln Laboratory. His areas of expertise include cybersecurity, analytics, artificial intelligence, and high-performance computing. David was elected IEEE Fellow “for technical leadership in the development of high performance embedded computing for real-time defense systems,” was awarded the Eminent Engineer Award from the College Engineering at NMSU, and was elected to the NMSU Klipsch Electrical and Computer Engineering Academy. He is a member of the Deans of Engineering Council at NMSU and the advisory board in the School of Computing and Information Sciences at the Florida International University as well as a member of MIT/LL steering committee. Previously, he served on the Army Science Board. David coauthored High Performance Embedded Computing, A Systems Perspective. He holds a BS from New Mexico State University (NMSU), an MS from MIT, an EE degree in electrical and oceanographic engineering jointly from MIT and the Woods Hole Oceanographic Institution, and an MBA from SMU. He was born in El Paso, TX, and is fluent in Spanish. In his free time, he’s an avid golfer, saltwater fisherman, and outdoorsman.

Presentations

AI canonical architecture and cybersecurity examples

David Martinez discusses an AI canonical architecture suitable for a number of different classes of applications and shares examples focused on cybersecurity to illustrate an application area that benefits from an end-to-end AI architecture.

Rob May is the cofounder and CEO of Talla, a leading AI-enabled enterprise platform for HR and IT. Rob has over a decade of startup experience and is a thought leader in the AI space as the editor of InsideAI, a leading AI newsletter with more than 25,000 subscribers. Previously, he was the cofounder and CEO of Backupify (acquired by Datto), the senior vice president of business development at Datto, and a digital design engineer at Harris Corporation, where he worked on graphics processing chips for the Comanche helicopter. Rob holds a BS in electrical engineering and an MBA, both from the University of Kentucky.

Presentations

Inside and out: The impact of AI and data on transforming the enterprise Session

Join in for a panel discussion on how AI and data are transforming the enterprise as we know it. Panelists will discuss data, talent, and technology challenges, enterprise adoption barriers, areas of disruption, and talent concerns (enterprises' willingness to pay) as AI turns the enterprise world as we know inside and out.

Clemens Mewald is a product manager in Google’s Research and Machine Intelligence Group. He is the product lead for TensorFlow Extended (TFX), an end-to-end ML platform based on TensorFlow, and several other TensorFlow products. Clemens holds an MSc in computer science from UAS Wiener Neustadt, Austria, and an MBA from MIT Sloan.

Presentations

TensorFlow Extended: An end-to-end machine learning platform for TensorFlow (sponsored by Google) Session

As machine learning evolves from experimentation to serving production workloads, so does the need to effectively manage the end-to-end training and production workflow including model management, versioning, and serving. Clemens Mewald offers an overview of TensorFlow Extended (TFX), the end-to-end machine learning platform for TensorFlow that powers products across all of Alphabet.

Risto Miikkulainen is CTO at Sentient Technologies and a professor of computer science at the University of Texas at Austin. His recent research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing and vision. Risto has published over 370 articles in these research areas and has 16 patents pending. He is an IEEE fellow and a recipient of the 2017 Gabor Award of the International Neural Network Society. Risto holds an MS in engineering from the Helsinki University of Technology, Finland, and a PhD in computer science from UCLA.

Presentations

Evolutionary computation: The next deep learning Session

Deep learning (DL) has transformed much of AI and demonstrated how machine learning can make a difference in the real world. With DL, the massive expansion of available training data and compute gave neural networks a new instantiation that significantly increased their power. Evolutionary computation (EC) is on the verge of a similar breakthrough. Risto Miikkulainen explains why.

Mehdi Miremadi is a partner at McKinsey & Company, where he serves technology, materials, and financial clients on a variety of strategy, artificial intelligence, and automation projects. A leader in automation, artificial intelligence, and machine learning, Mehdi co-leads McKinsey’s research on the impact of automation and artificial intelligence on the future of work as well as artificial intelligence potentials and limitations; he also co-leads McKinsey Digital Capability Center (DCC) in Chicago, intended as a hub to showcase best practices in digital and automation and cutting-edge technologies. His articles have appeared in Fortune, Harvard Business Review, and McKinsey Quarterly, among others.

Presentations

Executive Briefing: Have we reached peak human? The impact of AI on the workforce Session

Drawing on the McKinsey Global Institute's groundbreaking research, Mehdi Miremadi explores commonly asked questions relating to AI and its impact on work, including: How big is the AI opportunity? Which sectors and functions will capture the most value? As AI takes hold, will there be enough jobs for humans? What will they be, and what skills are needed? What are the implications for leaders?

Aleksandra (Saška) Mojsilovic is a scientist, head of AI foundations at IBM Research, codirector of IBM Science for Social Good, and an IBM fellow. Previously, she was a member of the technical staff at Bell Laboratories. Saška is the author of over 100 publications and holds 16 patents. Her work has been recognized with several awards, including the IEEE Signal Processing Society Young Author Best Paper Award, the INFORMS Wagner Prize, the IBM Extraordinary Accomplishment Award, and the IBM Gerstner Prize. Saška is a fellow of the IEEE and a member of IBM Academy of Technology. She also serves on the board of directors of Neighborhood Trust Financial Partners, which provides financial literacy and economic empowerment training to low-income individuals. Saška holds a PhD in electrical engineering from the University of Belgrade, Serbia.

Presentations

AI for Good Session

AI possesses an incredible potential to help address the challenges of our planet. Drawing on her experience as the head of AI foundations and codirector of Science for Social Good at IBM Research, Aleksandra Mojsilovic shares innovative examples of applying AI to humanitarian problems and discusses gaps that challenge us from making larger impact with our work.

Piero Molino is a research scientist at Uber AI Labs, working on natural language processing and dialogue systems. Previously, Piero was cofounder and CTO of QuestionCube, a startup building next-gen question-answering systems, and worked for Yahoo Labs in Barcelona on learning to rank, at IBM Watson in New York on question-answering, and at Geometric Intelligence on grounded language understanding. He holds a PhD in computer science from the University of Bari, Italy.

Presentations

Improving customer support with natural language processing and deep learning Session

Uber has implemented an ML and NLP system that suggests the most likely solutions to a ticket to its customer support representatives, making them faster and more accurate while providing a better user experience. Piero Molino, Huaixiu Zheng, and Yi-Chia Wang describe how Uber built the system with traditional and deep learning models and share the lessons learned along the way.

Philipp Moritz is a PhD candidate in EECS at UC Berkeley, with broad interests in artificial intelligence, machine learning, and distributed systems. He is a member of the Statistical AI Lab and the RISELab.

Presentations

Building reinforcement learning applications with Ray Tutorial

Ray is a new distributed execution framework for reinforcement learning applications. Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art reinforcement learning algorithms.

Laurence Moroney is a developer advocate on the Google Brain team at Google, working on TensorFlow and machine learning. He’s the author of dozens of programming books, including several best sellers, and a regular speaker on the Google circuit. While not Googling, he’s also a published novelist, comic book writer, and screenwriter.

Presentations

Building AI with TensorFlow: An Overview (sponsored by Google) Session

TensorFlow is one of the world’s biggest open source projects, and it continues to grow in adoption and functionality. Laurence Moroney, Edd Wilder-James, and Sandeep Gupta share major recent developments and highlight some future directions. Join in to learn how you can become more involved in the TensorFlow community.

TensorFlow: Machine learning for programmers (sponsored by Google) Session

Laurence Moroney dives into machine learning, AI, deep learning, and more and explains where they fit in the programmers toolkit. Along the way, he walks you through what it's all about, cutting through the hype to show the opportunities that are available in machine learning.

David Mueller is director of product management at Teradata, where he manages product innovation in AI for Teradata’s Technology Innovation Office. He focuses on technology that enables enterprises to benefit from machine and deep learning at scale. David’s background is in digital customer and marketing analytics. Previously, he headed the regional data science practice based in Singapore for Think Big Analytics, Teradata’s business analytics consultancy, where he led an international team of data scientists supporting customer projects across Southeast Asia, India, Pakistan, and South Korea as experts in the application of advanced statistical and analytical methods to the solution of business problems across industries. Earlier in his career, he led the data science team at a German ad tech company.

Presentations

PyTorch: A flexible approach for computer vision models Tutorial

From social network photo filters to self-driving cars, computer vision has brought applied deep learning to the masses. Built by the pioneers of computer vision software, PyTorch enables developers to rapidly build computer vision models. Mo Patel and David Mueller offer an overview of computer vision fundamentals and walk you through using PyTorch to build computer vision applications.

Jayanti Venkata Sai Narayana Murty is the regional CTO at Digitate. An experienced IT leader with nearly two decades of industry experience, Jayanti has held a variety of IT leadership roles with a focus on business development, innovation, and technology consulting at Digitate and its parent company TCS. Previously, he was global head of strategic accounts for the company’s ignio and Cloud Plus platforms and led the TCS IT performance consulting practice for North America, where his responsibilities included defining a future vision and strategy for the group and delivering solid growth. He has led and delivered large, high-profile engagements for customers in the telecom, banking, retail, healthcare, transportation, logistics, and government sectors in markets including the Americas, the UK, Asia-Pacific, and India. Jayanti’s key areas of interest include analytics, high-performance computing, architecture and design, modeling, and optimization for large IT systems. He has authored whitepapers on performance engineering and capacity planning that have been published by ROSATEA and CMG and has worked as evaluator and chair for the TCS Technical Architects Conference. Jayanti holds a bachelor of technology in mechanical engineering from the National Institute of Technology, Warangal in India.

Presentations

Removing complexity for workload automation with machine learning (sponsored by Digitate) Session

Do you have constantly changing business environments across many business units and processes with multiple job schedulers and infrastructure platforms? Do you struggle with end-to-end visibility and a lot of alerts? Award-winning ignio can help. Jayanti Murty explains how and shares real-world examples of companies that have reduced operational risks and outages and technology and labor costs.

Paco Nathan is known as a “player/coach” with core expertise in data science, natural language processing, machine learning, and cloud computing. He has 35+ years of experience in the tech industry, at companies ranging from Bell Labs to early-stage startups. His recent roles include director of the Learning Group at O’Reilly Media and director of community evangelism at Databricks and Apache Spark. Paco is the co-chair of JupyterCon, and an advisor for Amplify Partners, Deep Learning Analytics, and Recognai. He was named one of the top 30 people in big data and analytics in 2015 by Innovation Enterprise.

Presentations

Executive Briefing: Best practices for human in the loop—The business case for active learning Session

Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data.

Open source decentralized data markets for training AI in areas of large shared risk Blockchain

Paco Nathan examines decentralized data markets. With components based on blockchain technologies—smart contracts, token-curated registries, DApps, voting mechanisms, etc.—decentralized data markets allow multiple parties to curate ML training datasets in ways that are transparent, auditable, and secure and allow equitable payouts that take social values into account.

Robert Nishihara is a fourth-year PhD student working in the UC Berkeley RISELab with Michael Jordan. He works on machine learning, optimization, and artificial intelligence.

Presentations

Building reinforcement learning applications with Ray Tutorial

Ray is a new distributed execution framework for reinforcement learning applications. Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art reinforcement learning algorithms.

Peter Norvig is a director of research at Google. In his prior role, as director of search quality, he directed Google’s core Search Algorithms Group, which means he was the manager of record responsible for answering more queries than anyone else in the history of the world. Previously, he was the head of the Computational Sciences Division at the NASA Ames Research Center (NASA’s senior computer scientist) and received the NASA Exceptional Achievement Award in 2001. He was also an assistant professor at the University of Southern California and a research faculty member in the Computer Science Department at the University of California, Berkeley. He has over 50 publications in computer science, concentrating on artificial intelligence, natural language processing, and software engineering. He is the author of Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX and coauthor of Artificial Intelligence: A Modern Approach, the leading textbook in the field. He is also the author of the Gettysburg Powerpoint Presentation and has written the world’s longest palindromic sentence. Peter is a fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. He holds a PhD from UC Berkeley, where he was recognized with a distinguished alumni award in 2006.

Presentations

The breadth of AI applications: The ongoing expansion Keynote

In 2011, we saw a sudden increase in the abilities of computer vision systems brought about by academic researchers in deep learning. Today, Peter Norvig explains, we see continued progress in those fields, but the most exciting aspect is the diversity of applications in fields far astray from the original breakthrough areas, as well as the diversity of the people making these applications.

Tim Nugent pretends to be a mobile app developer, game designer, tools builder, researcher, and tech author. When he isn’t busy avoiding being found out as a fraud, Tim spends most of his time designing and creating little apps and games he won’t let anyone see. He also spent a disproportionately long time writing this tiny little bio, most of which was taken up trying to stick a witty sci-fi reference in. . .before he simply gave up.

Presentations

Learning from video games Session

Video games have used sophisticated AI techniques for decades to drive everything from area design to navigation to enemies to conversation and planning. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent offer an overview of the history of AI in video games and explain how the needs that drove AI advancement in the game development world map to almost-identical problems in the real world.

Tim O’Reilly is the founder and CEO of O’Reilly Media, Inc. His original business plan was simply “interesting work for interesting people,” and that’s worked out pretty well. O’Reilly Media delivers online learning, publishes books, runs conferences, urges companies to create more value than they capture, and tries to change the world by spreading and amplifying the knowledge of innovators. Tim has a history of convening conversations that reshape the computer industry. In 1998, he organized the meeting where the term “open source software” was agreed on and helped the business world understand its importance. In 2004, with the Web 2.0 Summit, he defined how “Web 2.0” represented not only the resurgence of the web after the dot-com bust but a new model for the computer industry based on big data, collective intelligence, and the internet as a platform. In 2009, with his Gov 2.0 Summit, he framed a conversation about the modernization of government technology that has shaped policy and spawned initiatives at the federal, state, and local level and around the world. He has now turned his attention to implications of AI, the on-demand economy, and other technologies that are transforming the nature of work and the future shape of the business world. This is the subject of his new book from Harper Business, WTF: What’s the Future and Why It’s Up to Us. In addition to his role at O’Reilly Media, Tim is a partner at early-stage venture firm O’Reilly AlphaTech Ventures (OATV) and serves on the boards of Maker Media (which was spun out from O’Reilly Media in 2012), Code for America, PeerJ, Civis Analytics, and PopVox.

Presentations

Fireside chat with Tim O'Reilly and Kai-Fu Lee Keynote

Fireside chat with Tim O'Reilly and Kai-Fu Lee

Carl Osipov is a program manager focused on helping Google’s customers and business partners get trained and certified to run machine learning and data analytics workloads on Google Cloud. Carl has more than 16 years of experience in the IT industry and has held leadership roles for programs and projects in the areas of big data, cloud computing, service-oriented architecture, machine learning, and computational natural language processing at some of the world’s leading technology companies across the United States and Europe. Carl has written over 20 articles in professional, trade, and academic journals and holds six patents from the USPTO. He has received three corporate awards from IBM for his innovative work. You can find out more about Carl on his blog.

Presentations

Image classification models in TensorFlow Tutorial

Carl Osipov walks you through creating increasingly sophisticated image classification models using TensorFlow.

Serverless machine learning with TensorFlow on GCP Day (sponsored by Google Cloud) Event

Carl Osipov leads an introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-ons labs, you'll learn machine learning (ML) and TensorFlow concepts and develop skills in developing, evaluating, and productionizing ML models.

Richard Ott is a data scientist in residence at the Data Incubator, where he gets to combine his interest in data with his love of teaching. Previously, he was a data scientist and software engineer at Verizon. Rich holds a PhD in particle physics from the Massachusetts Institute of Technology, which he followed with postdoctoral research at the University of California, Davis.

Presentations

Deep learning with Apache Spark and BigDL, with Keras and TensorFlow support 2-Day Training

BigDL is a powerful tool for leveraging Hadoop and Spark clusters for deep learning. Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL's components and explaining how to use it to implement machine learning algorithms. You'll use your newfound knowledge to build algorithms that make predictions using real-world datasets.

Deep learning with Apache Spark and BigDL, with Keras and TensorFlow support (Day 2) Training Day 2

BigDL is a powerful tool for leveraging Hadoop and Spark clusters for deep learning. Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL's components and explaining how to use it to implement machine learning algorithms. You'll use your newfound knowledge to build algorithms that make predictions using real-world datasets.

Finding, attracting, placing, and developing the next generation of technology and IT professionals requires something other than the scattershot approach to professional matchmaking that drives most outsourced recruitment efforts. As workforce enablement director at milk+honey, Nick Paquin augments best practices in recruitment and relationship management with milk+honey’s proprietary skills matrix to ensure eligible candidates possess the skills, professional understanding, and personality to carry out and complete machine learning projects for milk+honey’s clients.

Presentations

Taming dragons: A breakthrough approach to AI for business leaders Tutorial

Utilizing AI technologies to advance business goals remains one of the most daunting challenges for many business leaders. Beth Partridge, assisted by Nick Paquin and Annie O'Connor, shares a breakthrough approach that bridges the gap between data science and business. Join in to gain a clear understanding of what AI can do for your business and how to go about implementing it.

Beth Partridge is founder, CEO, and chief data scientist at milk+honey, a company born from her hard-won understanding that success in business now requires a deep commitment to data-driven decision making and development of a culture of experimentation and innovation at all levels of an organization. Beth is that rare executive with a powerhouse combination of natural leadership, deep technical experience, and impeccable execution skills. She is filled with excitement about the data revolution and the profound transformation that’s upon us. Beth has nearly 30 years of executive-level experience in manufacturing, product engineering, quality control, technical support, and operations. She holds a BS in electrical engineering and a master of information and data science from UC Berkeley.

Presentations

Taming dragons: A breakthrough approach to AI for business leaders Tutorial

Utilizing AI technologies to advance business goals remains one of the most daunting challenges for many business leaders. Beth Partridge, assisted by Nick Paquin and Annie O'Connor, shares a breakthrough approach that bridges the gap between data science and business. Join in to gain a clear understanding of what AI can do for your business and how to go about implementing it.

Alexandre Passos is a software engineer on the TensorFlow team at Google, where most recently he worked on Eager execution and related usability projects. He studied at UMass under Andrew McCallum.

Presentations

AutoGraph and Cloud TPUs (sponsored by Google) Session

Alexandre Passos and Frank Chen offer an overview of TensorFlow AutoGraph, which automatically converts plain Python code into the TensorFlow equivalent, using source code transformation. They then lead a technical deep dive into Google's Cloud TPU accelerators and show you how to program them.

Labhesh Patel is CTO and chief scientist at Jumio, where he is responsible for driving the company’s innovation in the identity verification space with deep learning, computer vision, and augmented intelligence—an alternative conceptualization of artificial intelligence that focuses on AI’s assisted role to enhance human intelligence. An accomplished leader with over 15 years of experience in corporate and entrepreneurial settings, Labhesh has proven experience leading engineering teams, launching new online services (from concept creation to customer delivery), and developing ground-breaking technologies at companies including Cisco, Abzooba, xpresso.ai, Spotsetter, and CellKnight. He has 175 patents filed with another 134 patents issued under his name. Labhesh holds an MS in electrical engineering (MSEE) from Stanford University and a BT from the Indian Institute of Technology in Kanpur.

Presentations

Productionalizing deep learning for computer vision Session

Labhesh Patel explains how deep learning is informing Jumio's computer vision through smarter data extraction, fraud detection, and risk scoring and how Jumio is leveraging massive datasets and human review to dramatically improve the accuracy of its machine learning algorithms to detect bogus IDs and streamline the verification process of legitimate documents.

Mo Patel is an independent deep learning consultant advising individuals, startups, and enterprise clients on strategic and technical AI topics. Mo has successfully managed and executed data science projects with clients across several industries, including cable, auto manufacturing, medical device manufacturing, technology, and car insurance. Previously, he was practice director for AI and deep learning at Think Big Analytics, a Teradata Company, where he mentored and advised Think Big clients and provided guidance on ongoing deep learning projects; he was also a management consultant and a software engineer earlier in his career. A continuous learner, Mo conducts research on applications of deep learning, reinforcement learning, and graph analytics toward solving existing and novel business problems and brings a diversity of educational and hands-on expertise connecting business and technology. He holds an MBA, a master’s degree in computer science, and a bachelor’s degree in mathematics.

Presentations

PyTorch: A flexible approach for computer vision models Tutorial

From social network photo filters to self-driving cars, computer vision has brought applied deep learning to the masses. Built by the pioneers of computer vision software, PyTorch enables developers to rapidly build computer vision models. Mo Patel and David Mueller offer an overview of computer vision fundamentals and walk you through using PyTorch to build computer vision applications.

David Patterson is a professor emeritus at UC Berkeley, a distinguished engineer at Google Brain, and vice chair of the board of the RISC-V Foundation. His most successful research projects are reduced instruction set computers (RISC), redundant arrays of inexpensive disks (RAID), and network of workstations, which together led to multibillion-dollar industries, seven books, and about 40 honors, including election to the National Academy of Engineering, the National Academy of Sciences, and the Silicon Valley Engineering Hall of Fame. He also shared the ACM Turing award, the IEEE von Neumann Medal, and NEC C&C prize with John Hennessy, past president of Stanford University and coauthor of two of his books. David holds an AB, MS, and PhD, all from UCLA.

Presentations

A new golden age for computer architecture Keynote

High-level, domain-specific languages and architectures and freeing architects from the chains of proprietary instruction sets will usher in a new golden age. David Patterson explains why, despite the end of Moore’s law, he expects an outpouring of codesigned ML-specific chips and supercomputers that will improve even faster than Moore’s original 1965 prediction.

Ariel joined Taboola as Vice President of Information Technology in February 2014. With over 20 years of experience in Information Security and IT systems, Ariel leads a team of IT professionals that work to implement state-of-the-art solutions, from Open Source to home grown to traditional enterprise software, across the company’s global infrastructure. Ariel has held multiple positions as CISO and CIO for web-facing companies from startups to publicly traded firms including programmatic ad solution provider myThings and casino, poker and gaming provider 888 Holdings. Ariel writes about security, cloud and IT on his blog. He holds a BSc in Computer Science from Mercy College.

Presentations

Beyond hype: AI in the real world Keynote

In this keynote, Julie Choi explores three real-world use cases featuring a diverse set of data centric problems and Intel solutions. Julie also welcomes Ariel Pisetzky, the Vice President of IT at Taboola to discuss how AI is transforming their business as they create personalized content through their predictive recommendation engine.

Jake Porway is the founder and executive director of DataKind, a nonprofit that harnesses the power of data science in the service of humanity. He is an alum of the New York Times R&D Lab and has worked at Google and Bell Labs. A recognized leader in the Data for Good Movement, he has spoken at IBM, Microsoft, Google, and the White House. Jake is also a PopTech Social Innovation fellow and a National Geographic Emerging Explorer. He holds a BS in computer science from Columbia University and an MS and PhD in statistics from UCLA.

Presentations

AI: A force for good Session

Jake Porway sheds light on AI’s true potential to impact the world in a positive way. Drawing on his experience as the head of DataKind, an organization applying AI for social good, Jake shares best practices, discusses the importance of using human-centered design principles, and addresses ethical concerns and challenges encountered when using AI to tackle complex humanitarian issues.

Anand Raman is the chief of staff for the AI CTO office at Microsoft. Previously, he was the chief of staff for the Microsoft Azure Data Group, covering data platforms and machine learning, and ran the company’s product management and the development teams for Azure Data Services and the Visual Studio and Windows Server user experience teams; he also worked several years as researcher before joining Microsoft. Anand holds a PhD in computational fluid mechanics.

Presentations

Distributed deep domain adaptation for automated poacher detection (sponsored by Microsoft) Session

Mark Hamilton shares a novel deep learning approach for creating a robust object detection network for use in an infrared, UAV-based poacher recognition system.

Bharath Ramsundar is cofounder and CTO at Computable, where he is focused on designing the decentralized protocols that will unlock data and AI to create the next stage of the internet. Bharath created the DeepChem open source project to grow the deep drug discovery open source community and cocreated the MoleculeNet benchmark suite to facilitate development of molecular algorithms. Bharath is the lead author of TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning, a developer’s introduction to modern machine learning, from O’Reilly Media. He holds a BA and BS in EECS and mathematics from UC Berkeley, where he was valedictorian of his graduating class in mathematics, and a PhD in computer science from Stanford University, where he studied the application of deep learning to problems in drug discovery. Bharath’s graduate education was supported by a Hertz Fellowship, the most selective graduate fellowship in the sciences.

Presentations

Decentralized data markets in theory and practice Blockchain

Today's data economy critically depends on access to high-quality datasets. However, the owners of such datasets are incentivized to keep them private, since there is no fair and efficient market for purchasing high-value datasets. Bharath Ramsundar explains how to build a decentralized data marketplace by exploiting the capabilities of smart contract platforms such as Ethereum.

Soups Ranjan is the director of data science at Coinbase, one the largest bitcoin exchanges in the world. He manages the risk and data science team that is chartered with preventing avoidable losses to the company due to payment fraud or account takeovers. Previously, Soups led the development of machine learning pipelines to improve performance advertising at Yelp and Flurry. He is the founder of RiskSalon.org, a roundtable forum for risk professionals in San Francisco to share ideas on stopping bad actors. Soups holds a PhD in ECE focusing on network security from Rice University.

Presentations

AI at Scale at Coinbase (sponsored by Amazon Web Services) Keynote

Cryptocurrencies such as Bitcoin, Ethereum and others are a transformative force that could one day create an open financial network for the world. Soups manages the data science and risk team at Coinbase, one of the largest cryptocurrency exchanges in the world.

Delip Rao is the founder of R7 Speech Science, a San Francisco-based company focused on building innovative products on spoken conversations. Previously, Delip was the founder of Joostware, which specialized in consulting and building IP in natural language processing and deep learning. Delip is a well-cited researcher in natural language processing and machine learning and has worked at Google Research, Twitter, and Amazon (Echo) on various NLP problems. He is interested in building cost-effective, state-of-the-art AI solutions that scale well. Delip has an upcoming book on NLP and deep learning from O’Reilly.

Presentations

Natural language processing with deep learning 2-Day Training

Delip Rao explores natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.

Natural language processing with deep learning (Day 2) Training Day 2

Delip Rao explores natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.

Ujjwal Ratan is a senior machine learning specialist solution architect on the global healthcare and life sciences team at Amazon Web Services (AWS). Ujjwal has over 13 years of experience serving in technology-enabling roles in the healthcare industry, working with large enterprises and small startups alike to design and implement solutions to solve problems in healthcare and life sciences by applying machine learning. He has been an evangelist for AWS healthcare AI and a vocal supporter for the use of machine learning in the field of healthcare and life sciences and has presented at conferences and published blogs and whitepapers on the topic.

Presentations

From ingest to predict: Building an effective ML pipeline (sponsored by AWS) Session

Data is your most important asset, so you don't want to move it around too much just to cater to varying compute needs. Machine learning (ML) is no exception to this rule. Ujjwal Ratan shares an end-to-end workflow for building an ML pipeline.

Rachael Rekart leads the machine assistance team at Autodesk, responsible for developing and delivering the Autodesk Virtual Agent (AVA). AVA currently has over 100,000 conversations with customers per month and has reduced call resolution times for its most repetitive support contacts by 99%, enabling Autodesk’s agents to focus on more complex customer issues. Rachael believes that AI and machine learning will continue to redefine the way Autodesk engages with customers for their service and support needs. She’s an active evangelist of AI both inside and outside the company and speaks about AVA’s evolution and capabilities at conferences and internal engagements and as a consultant for companies getting started with AI. Previously, Rachael led Autodesk teams focused on implementing process optimizations and system improvements to ensure sustainable business growth. Prior to Autodesk, she worked in demand planning and supply chain management.

Presentations

How Autodesk is humanizing customer support with AI: Meet AVA Session

Rachael Rekart offers an overview of Autodesk Virtual Agent (AVA), which has revolutionized the way Autodesk approaches customer service. Customers chat with AVA as they would a human, in natural language, and AVA processes transactions quickly, returns accurate answers, or gathers information to pass to a human counterpart to resolve the query.

Dmitry Rizshkov is a machine learning engineer at Intel.

Presentations

Accelerating deep learning inference using OpenVINO across Intel platforms Session

Dmitry Rizshkov offers an overview of OpenVINO and explores real customer case studies that exceeded the most challenging inference requirements.

Ofer Ronen is the general manager of Chatbase, a conversational analytics service brought to you by Area 120 (a products incubator operated by Google). Previously, he was CEO of Pulse.io, an app performance monitoring service (acquired by Google), and CEO of ad network Sendori (acquired by IAC). Ofer is a startup mentor at Stanford and an angel investor in Lyft, Palantir, and Klout. He holds an MS in artificial intelligence from Michigan and an MBA from Cornell.

Presentations

A data-driven approach to building AI-powered bots and virtual agents Session

For developers building a bot or virtual agent, the critical question is which bot to build and why? Today, most can’t answer it without a manual intent discovery process, largely based on guesswork, that uncovers only a percentage of possible opportunities. Ofer Ronen demonstrates techniques, based on machine learning, for faster, more efficient intent discovery.

Joe Rothermich is a senior director of quant research and data science for Thomson Reuters Labs, where he heads a research team in San Francisco developing new quantitative models and analytics for systematic and fundamental investors, using traditional quantitative finance techniques and factor-based modeling. The lab also performs research in machine learning, data science, and text mining/NLP and is investigating new data sources for investment research, including big data and alternative data. Previously, Joe was a quantitative portfolio manager at Lincoln Vale LLC, a fintech startup founder, and a consultant. He is a Chartered Financial Analyst (CFA) and a member of the CFA Institute. Joe holds an MSc in natural computation from the University of Birmingham, UK, and a BS in systems analysis from Miami University.

Presentations

Applications of AI for quantitative finance at Thomson Reuters Session

After a slow start, the finance industry is quickly catching up with others in its adoption of AI. Joe Rothermich explains how Thomson Reuters Labs is using AI to perform research in building quantitative investment models and discusses the company's research in deep learning for credit risk, machine learning and NLP for unlocking alternative datasets, and financial graph-based analytics.

Vikram Saletore is a principal engineer and a performance architect in the Customer Solutions, Artificial Intelligence Products, and Data Center Groups at Intel. Vikram leads performance optimizations for distributed machine learning (ML) and deep learning (DL) workloads and collaborates with industry enterprise and government partners, OEMs, HPC, and CSP customers on deep learning scale-out training and inference and machine learning analytics on Intel architectures. Vikram is also a technical coprincipal investigator for distributed deep learning research with European members of Intel’s Parallel Computing Center. Vikram has 25+ years of experience and has led many data center initiatives. As a research scientist with Intel Labs, he led research collaboration with HP Labs. Prior to Intel, Vikram was a tenure-track faculty member in the Computer Science Department at Oregon State University and led NSF-funded research in parallel programming and distributed computing, supervising eight graduate students. He also worked at DEC and AMD. He has many patents and has authored ~45 peer-reviewed research publications. Vikram holds a PhD in EE with a focus on parallel programming and distributed computing from the University of Illinois Urbana-Champaign and an MS from UC Berkeley.

Presentations

Efficient neural network training on Intel Xeon-based supercomputers Session

Vikram Saletore and Luke Wilson discuss a collaboration between SURFSara and Intel to advance the state of large-scale neural network training on Intel Xeon CPU-based servers, highlighting improved time to solution on extended training of pretrained models and exploring how various storage and interconnect options lead to more efficient scaling.

Jake Saper is principal at Emergence, where he co-leads the company’s practice focused on machine learning-enabled enterprise applications. Jake is passionate about the role machine learning can play in helping to augment workers, an approach Emergence has dubbed “coaching networks.” Previously, Jake worked in Kleiner Perkins’s Green Growth Fund, where he sourced and led diligence on companies in the geospatial, agricultural tech, and enterprise SaaS sectors. He currently serves as on the boards of DroneDeploy, Guru, Comfy, and Textio. Jake holds a BA (magna cum laude) from Yale University, an MBA from Stanford’s Graduate School of Business, where he was an Arjay Miller Scholar, and an MS in environment and resources, also from Stanford. He loves antique swords and musical parodies.

Presentations

AI is my copilot. Session

Much attention in enterprise AI today is focused on automation. Jake Saper explains why the more interesting applications focus on worker augmentation and offers an overview of coaching networks, which gather data from a distributed network of workers and identify the best techniques for getting things done.

Robert Schroll is a data scientist in residence at the Data Incubator. Previously, he held postdocs in Amherst, Massachusetts, and Santiago, Chile, where he realized that his favorite parts of his job were teaching and analyzing data. He made the switch to data science and has been at the Data Incubator since. Robert holds a PhD in physics from the University of Chicago.

Presentations

Deep learning with TensorFlow 2-Day Training

The TensorFlow library provides for the use of dataflow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. Robert Schroll offers an overview of the TensorFlow graph using its Python API.

Deep learning with TensorFlow (Day 2) Training Day 2

The TensorFlow library provides for the use of dataflow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. Robert Schroll offers an overview of the TensorFlow graph using its Python API.

Noah Schwartz is cofounder and CEO of Quorum AI, a San Francisco-based AI SaaS company that specializes in lightweight, distributed AI architectures. Previously, Noah spent 12 years in academic research, most recently at Northwestern University as the assistant chair of Neurobiology, where his work focused on information processing in the brain. He has translated his research into products in augmented reality, sensor fusion, brain-computer interfaces, computer vision, and embedded robotics control systems. Noah was also senior data scientist at Lumos Labs, creators of the popular Lumosity brain training app.

Presentations

The future of AI is distributed: Peer-to-peer learning and multi-agent AI at the edge Session

Noah Schwartz explores the most recent advances in cooperative learning systems, including distributed and federated learning systems for real-world, edge-based AI. He also considers the pros and cons of multi-agent systems and demonstrates how Quorum AI is working to bridge the gap with the Quorum AI Framework.

Andrew Selle is one of the main developers of TensorFlow Lite at Google. Previously, he worked on the TensorFlow Python API and its NumPy-like features. Before joining Google Brain, he worked extensively on numeric physical simulation techniques for film special effects. He holds a PhD in computer science from Stanford University.

Presentations

Swift for TensorFlow and TensorFlow Lite (sponsored by Google) Session

Richard Wei and Andrew Selle discuss Swift for TensorFlow and TensorFlow Lite, covering the current status of development and the latest developments. They then teach you how to prepare your model for mobile and how to write code that executes it on a variety of different platforms.

Rudina Seseri is a founder and managing partner at Glasswing Ventures, an early-stage venture capital firm dedicated to investing in the next generation of AI-powered technology companies that connect consumers and enterprises and secure the ecosystem. With over 15 years of investing and transactional experience, Rudina has led technology investments and acquisitions in startup companies in the fields of robotics, the internet of things (IoT), SaaS marketing technologies, and digital media. Rudina’s portfolio investments include Talla, Celtra, CrowdTwist, Jibo, and SocialFlow. Rudina has been appointed by the dean of the Harvard Business School (HBS) for a fourth consecutive year to serve as entrepreneur in residence for the Business School and executive in residence for Harvard University’s Innovation Labs and has most recently been named to the 2018 HBS inaugural group of Rock Venture Capital Partners. She is also a member of the Business Leadership Council of Wellesley College. Rudina is also an advisor for L’Oréal USA’s Women in Digital, the director of the board of the Massachusetts Innovation and Technology Exchange (MITX), and a member of the Board of Overseers for Boston Children’s Hospital. She has been named one of Boston Business Journal’s 2017 “power 50 newsmakers,” a 2014 “women to watch” honoree by Mass High Tech, and one of Boston Business Journal’s 2011 “40 under 40” honorees for her professional accomplishments and community involvement. She holds a BA in economics and international relations (magna cum laude) from Wellesley College and an MBA from Harvard Business School. She is a member of Phi Beta Kappa and Omicron Delta Epsilon honor societies.

Presentations

Inside and out: The impact of AI and data on transforming the enterprise Session

Join in for a panel discussion on how AI and data are transforming the enterprise as we know it. Panelists will discuss data, talent, and technology challenges, enterprise adoption barriers, areas of disruption, and talent concerns (enterprises' willingness to pay) as AI turns the enterprise world as we know inside and out.

Nachum Shacham is a distinguished data scientist in Teradata’s Technology and Innovation Office (TIO), where he leads the Data Science Practice, exploring and applying technologies and practices. He works on developing statistical and machine learning methods for optimizing the operation of large-scale analytics platforms and benchmarking their performance under complex workloads. He also collaborates with business leaders to identify opportunities to leverage the Teradata Analytics Platform’s data science and machine learning functionality to drive business value from large-scale data. Previously, Nachum worked at eBay and PayPal, where he led projects on Teradata and Hadoop workload analytics, developed customer-oriented machine learning models, and taught advanced analytics using R. He is a fellow of the IEEE. Nachum holds a BSc in EE and an MSc in EE from the Technion-Israel Institute of Technology and a PhD in EECS from UC Berkeley.

Presentations

Closing the knowing-doing gap in AI: From model interpretability to better decisions (sponsored by Teradata) Session

Businesses can use AI techniques to make accurate predictions but still not act effectively on this knowledge. Since business value derives from actions rather than knowledge, it’s essential to identify a clear path from model predictions to effective actions. Nachum Shacham outlines a path that leverages model interpretability and Teradata Analytics Platform functions to guide effective actions.

Julie Shin Choi is head of marketing for AI at Intel, where she is responsible for marketing a portfolio of hardware and software products for building end-to-end AI solutions at the edge, data center, network, and cloud. Previously, Julie led product marketing at Hewlett Packard Enterprise, Mozilla, and Yahoo, focused on developer and enterprise audiences. She has produced over 50 developer conferences and hackathons, including SheCodes, a one-day conference for women technologists featuring Google, Facebook, Yahoo, Mozilla, Twitter, GitHub, Hackbright, Women Who Code, and others. Julie holds a bachelor’s degree from MIT and a master’s degree from Stanford, both in management science.

Presentations

Beyond hype: AI in the real world Keynote

In this keynote, Julie Choi explores three real-world use cases featuring a diverse set of data centric problems and Intel solutions. Julie also welcomes Ariel Pisetzky, the Vice President of IT at Taboola to discuss how AI is transforming their business as they create personalized content through their predictive recommendation engine.

Alyssa is a customer-driven product leader dedicated to building products that surprise, delight and bring new value to market. Her experience in scaling products from conception to large-scale ROI has been proven at both startups and large enterprises alike. As Director of Product Management at IBM Watson, Alyssa saw first-hand how thoughtful, sophisticated use of data has the power to transform industries. During her recent tenure at IBM, she oversaw the development of a large portfolio of AI products including vision, speech, emotional intelligence and machine translation.

Presentations

Reality Check: Beyond the Hype. Real Companies Doing Real Business Getting Real Value with AI Session

AI - everyone is talking about it but who is actually doing it (and generating business results). This session takes an industry by industry perspective on true AI adoption disambiguating the hype from the reality, the theoretical from the practical and the research labs from ROI.

Avesh Singh is an engineer at Cardiogram, a startup that applies deep learning to wearable data. Previously, Avesh worked at Nest Labs and Google. He holds a a BS and MS in computer science from Carnegie Mellon University.

Presentations

Debuggable deep learning Session

Deep learning is often called a black box, so how do you diagnose and fix problems in a deep neural network (DNN)? Avesh Singh and Kevin Wu explain how they systematically debugged DeepHeart, a DNN that detects cardiovascular disease from heart rate data. You'll leave with an arsenal of tools for debugging DNNs, including Jacobian analysis, TensorBoard, and DNN unit tests.

Ranjeeta Singh is vice president and general manager of data science and artificial intelligence at Teradata, where she is focused on driving artificial intelligence capabilities in the Teradata Platform and making it a solid workbench for data scientists. Ranjeeta has been in the technology industry for over 20 years, with the last 18 years spent at Intel, where she was most recently senior director of incubation and segment general manager of the connected worker portfolio and environmental monitoring in the Internet of Things (IoT) Group. A hands-on entrepreneur, she has jump-started multiple businesses from the ground up at Intel and has held multiple leadership positions spanning strategy, business planning, product management and marketing, architecture, and engineering across various platforms. She holds five patents at Intel, primarily in the IPv6 space. She has authored multiple publications and has been an invited speaker at a number of conferences. She is passionate about mentoring and coaching women in technology. Ranjeeta holds a BS in computer science and engineering from Birla Institute of Technology, India, an MS in computer science from Rensselaer Polytechnic Institute, and an MBA from Santa Clara University.

Presentations

Achieving transformative business outcomes with artificial intelligence (sponsored by Teradata) Session

The time to gain market advantage through AI is now. By 2021, Gartner projects that 40% of new enterprise applications will include AI, but the majority of AI-enhanced use cases have not been developed. Ranjeeta Singh and Nick Switanek shed light on pain points and use cases where AI can drive business value for companies that make the right investments in people, process, and technology.

Presentations

CANCELED The human-centered and ethical approach to designing intelligent systems with multidisciplinary teams Session

The public dialogue around artificial intelligence takes place at extremes, with some focusing on how robots will take everyone’s job and others speaking of a utopia where AI will do everything from cure cancer to solve world hunger. Irmak Sirer offers an alternative to these extreme views of AI: instead of artificial intelligence, we instead must focus on augmenting people’s intelligence.

Joseph Sirosh is the corporate vice president of the Cloud AI Platform at Microsoft, where he leads the company’s enterprise AI strategy and products such as Azure Machine Learning, Azure Cognitive Services, Azure Search, and Bot Framework. Previously, he was the corporate vice president for Microsoft’s Data Platform; the vice president for Amazon’s Global Inventory Platform, responsible for the science and software behind Amazon’s supply chain and order fulfillment systems, as well as the central Machine Learning Group, which he built and led; and the vice president of research and development at Fair Isaac Corp., where he led R&D projects for DARPA, Homeland Security, and several other government organizations. He is passionate about machine learning and its applications and has been active in the field since 1990. Joseph holds a PhD in computer science from the University of Texas at Austin and a BTech in computer science and engineering from the Indian Institute of Technology Chennai.

Presentations

Connected arms (sponsored by Microsoft) Keynote

Will artificial intelligence revolutionize prosthetic care and assistance? Join Microsoft’s Joseph Sirosh for an intriguing story on AI-infused prosthetics that are able to see, grip, and feel and discover how this is enabling affordable and functional prosthetic care.

Chris Smith is a senior principal software engineer at online doctor marketplace and booking startup Zocdoc, where he is developing deep learning models to help Zocdoc’s patients understand the complex world of medical insurance. Through machine learning, Chris is helping to bring data-driven products to an industry traditionally resistant to change. Over his career at Zocdoc, he’s built numerous patient-focused products, rebuilt the company’s CI/CD systems, helped migrate Zocdoc from the data center to AWS, and set up the first version of its microservice infrastructure.

Presentations

Reverse engineering your AI prototype and the road to reproducibility Session

With the help of better software, cloud infrastructure, and pretrained networks, AI models have become easier to build. But once your solution veers from a common path, hidden challenges in reproducibility and implementation arise. Brian Dalessandro and Chris Smith share their experience and lessons learned while building a computer vision and OCR app for reading and classifying insurance cards.

Caroline Sofiatti is a data and machine learning expert at Computable Labs. Previously, she was head of data science at Noon Home, an IoT consumer electronic startup. She holds a master’s degree in astrophysics from UC Berkeley, where she worked with Nobel laureate Saul Perlmutter.

Presentations

The role of a decentralized data marketplace in the future of AI Blockchain

Caroline Sofiatti explains how a token economy and a privacy-centric approach can reduce analysis times, cut data preparation costs, solve insufficient data problems, improve data security and governance, and enable the next generation of AI algorithms.

Dawn Song is a professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning, security, and the blockchain. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, distributed systems security, applied cryptography, the blockchain, and smart contracts to the intersection of machine learning and security. She is also a serial entrepreneur. Previously, she was a faculty member at Carnegie Mellon University. She is the recipient of various awards, including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, the Faculty Research Award from IBM, Google and other major tech companies, and best paper awards from top conferences in computer security and deep learning. She is ranked the most cited scholar in computer security (AMiner Award). Dawn holds a PhD from UC Berkeley.

Presentations

AI and security: Lessons, challenges, and future directions Keynote

Dawn Song details challenges and exciting new opportunities at the intersection of AI and security and explains how AI and deep learning can enable better security and how security can enable better AI. You'll learn about secure deep learning and approaches to ensure the integrity of decisions made by deep learning.

Evan Sparks is cofounder and CEO of Determined AI, a software company that makes machine learning engineers and data scientists fantastically more productive. Previously, Evan worked in quantitative finance and web intelligence. He holds a PhD in computer science from UC Berkeley, where, as a member of the AMPLab, he contributed to the design and implementation of much of the large-scale machine learning ecosystem around Apache Spark, including MLlib and KeystoneML. He also holds an AB in computer science from Dartmouth College.

Presentations

Software development in the age of deep learning Session

In spite of the enormous excitement about the potential of deep learning, several key challenges—from prohibitive hardware requirements to immature software offerings—are impeding its widespread enterprise adoption. Evan Sparks and Ameet Talwalkar detail fundamental challenges facing organizations looking to adopt deep learning and present novel solutions to overcome several of them.

Joseph Spisak is the product manager for Facebook’s AI open source platform, including PyTorch and ONNX. Previously, he led AI partnerships and deep learning product at Amazon Web Services, where he and his team were dedicated to building tools and solutions to help democratize deep learning for the developer community and ultimately accelerate the development of deep learning-based applications. Joseph holds a bachelor’s degree in electrical engineering from Michigan State University and an MBA and MS in finance from the University of Denver. He is a proud graduate of the Entrepreneurial and Innovation Program at Stanford University’s Graduate School of Business.

Presentations

Accelerating research to production with PyTorch 1.0 Session

Facebook's strength in AI innovation comes from its ability to quickly bring cutting-edge research into large-scale production using a multifaceted toolset. Joseph Spisak explains how PyTorch 1.0 helps to accelerate the path from research to production by making AI development more seamless and interoperable.

Ramesh Sridharan is a machine learning engineering manager at Captricity. Ramesh is passionate about using technology for social good, and his research has helped enable a cross-collaboration between researchers and doctors to understand large, complex medical image collections, particularly in predicting the effects of diseases such as Alzheimer’s on brain anatomy. He holds a PhD in electrical engineering and computer science from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), where his thesis focused on developing machine learning and computer vision technologies to enhance medical image analysis.

Presentations

How Captricity built a human-level handwriting recognition engine using data-driven AI Session

Captricity has deployed a machine learning pipeline that can read handwriting at human-level accuracy. Ramesh Sridharan discusses the big ideas the company learned building and deploying this system, using data to identify specific problems to solve using AI and to evaluate and validate the algorithm itself and the overall system once deployed.

Ashok N. Srivastava is the senior vice president and chief data officer at Intuit, where he is responsible for setting the vision and direction for large-scale machine learning and AI across the enterprise to help power prosperity across the world—and in the process is hiring hundreds of people in machine learning, AI, and related areas at all levels. Ashok has extensive experience in research, development, and implementation of machine learning and optimization techniques on massive datasets and serves as an advisor in the area of big data analytics and strategic investments to companies including Trident Capital and MyBuys. Previously, Ashok was vice president of big data and artificial intelligence systems and the chief data scientist at Verizon, where his global team focused on building new revenue-generating products and services powered by big data and artificial intelligence; senior director at Blue Martini Software; and senior consultant at IBM. He is an adjunct professor in the Electrical Engineering Department at Stanford and is the editor-in-chief of the AIAA Journal of Aerospace Information Systems. Ashok is a fellow of the IEEE, the American Association for the Advancement of Science (AAAS), and the American Institute of Aeronautics and Astronautics (AIAA). He has won numerous awards, including the Distinguished Engineering Alumni Award, the NASA Exceptional Achievement Medal, the IBM Golden Circle Award, the Department of Education Merit Fellowship, and several fellowships from the University of Colorado. Ashok holds a PhD in electrical engineering from the University of Colorado at Boulder.

Presentations

Executive Briefing: Moving AI off your product roadmap and into your products Session

Ashok Srivastava explains how to make your organization AI ready, determine the right AI applications for your business and products, and accelerate your AI efforts with speed and scale.

Ion Stoica is a professor in the EECS Department at the University of California, Berkeley, where he does research on cloud computing and networked computer systems. Ion’s previous work includes dynamic packet state, chord DHT, internet indirection infrastructure (i3), declarative networks, and large-scale systems, including Apache Spark, Apache Mesos, and Alluxio. He is the cofounder of Databricks—a startup to commercialize Apache Spark—and Conviva—a startup to commercialize technologies for large-scale video distribution. Ion is an ACM fellow and has received numerous awards, including inclusion in the SIGOPS Hall of Fame (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001).

Presentations

Building reinforcement learning applications with Ray Tutorial

Ray is a new distributed execution framework for reinforcement learning applications. Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art reinforcement learning algorithms.

Nick Switanek is a field data scientist and works in product marketing for advanced analytics at Teradata. Previously, he was a professor at the Kellogg School of Management at Northwestern University and a data scientist at McKinsey & Co. Nick holds a PhD in business and an MS in statistics and machine learning from Stanford.

Presentations

Achieving transformative business outcomes with artificial intelligence (sponsored by Teradata) Session

The time to gain market advantage through AI is now. By 2021, Gartner projects that 40% of new enterprise applications will include AI, but the majority of AI-enhanced use cases have not been developed. Ranjeeta Singh and Nick Switanek shed light on pain points and use cases where AI can drive business value for companies that make the right investments in people, process, and technology.

David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe, and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a PhD in computer science and master’s degrees in both computer science and business administration.

Presentations

Executive Briefing: What you must know to build AI systems that understand natural language Session

New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby shares challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past six years.

Ameet Talwalkar is cofounder and chief scientist at Determined AI and an assistant professor in the School of Computer Science at Carnegie Mellon University. His research addresses scalability and ease-of-use issues in the field of statistical machine learning, with applications in computational genomics. Ameet led the initial development of the MLlib project in Apache Spark. He is the coauthor of the graduate-level textbook Foundations of Machine Learning (MIT Press) and teaches an award-winning MOOC on edX, Distributed Machine Learning with Apache Spark.

Presentations

Software development in the age of deep learning Session

In spite of the enormous excitement about the potential of deep learning, several key challenges—from prohibitive hardware requirements to immature software offerings—are impeding its widespread enterprise adoption. Evan Sparks and Ameet Talwalkar detail fundamental challenges facing organizations looking to adopt deep learning and present novel solutions to overcome several of them.

Neil Tan is an ARM developer evangelist with a keen interest on IoT and machine learning. He works closely with open source developer communities in Asia. Neil is the main author of uTensor. He is a speaker at events such as FOSDEM and has served as a judge for a design contest.

Presentations

uTensor: How small can AI get? Session

Would you believe that AI inferencing can be done on chips that cost less than a dollar? uTensor, a custom TensorFlow runtime for microcontrollers (MCUs), lets you do just that. Neil Tan offers an overview of uTensor, the first framework to streamline model deployments on MCUs, allowing you to push AI to the edge rather than sending everything to the cloud.

Ivan Tarapov is a senior program manager at Microsoft Research working on Project InnerEye, which is focused on using state-of-the-art machine learning to build a platform that will transform the way doctors interact with medical images. Previously, Ivan worked for a global software consultancy, contributing to high-risk medical software projects such as implantable pacemakers, external defibrillators, and insulin pumps in the areas of software design and development, engineering processes, and software architecture. Ivan is a certified Scrum Product Owner and excels at execution; he has a proven track record of building high-performing Agile software development teams and driving them to build great products. He holds a master’s degree in applied mathematics.

Presentations

Building intelligent mobile applications in healthcare Tutorial

Xiaoyong Zhu, Gheorghe Iordanescu, Wilson Lee, and Ivan Tarapov walk you through building a deep learning model and intelligent applications on edge devices running iOS, Android, and Windows, using a working example that helps clinicians in areas with less access to radiologists identify possible lung diseases.

Abhishek Tayal is a senior software engineer with Cortex, the machine learning platform team at Twitter, where he leads the entity embeddings team. Abhishek started his journey with Twitter as part of the ads prediction team for its direct response ad products. Previously, Abhishek worked with Tellapart, an ad tech startup (acquired by Twitter), and the Prime Video recommendations team at Amazon, where he led the development of the first-generation ML-based recommendation system for videos. He holds a master’s degree from the University of Southern California in LA.

Presentations

Making machine learning easy with embeddings Session

Abhishek Tayal offers insight into how Twitter's ML platform team, Cortex, is developing models, related tooling, and infrastructure with the objective of making entity embeddings a first-class citizen within Twitter's ML platform. Abhishek also shares success stories on how developing such an ecosystem increases efficiency and productivity and leads to better outcomes across product ML teams.

Ben Taylor is a cofounder at Ziff.ai, delivering automated deep learning into production. Ben has over 14 years of machine learning experience and is known for pushing the boundaries for what is possible with deep learning, including predicting country of origin, genetic haplogroups from a face, and even the ranking of ABC’s The Bachelor contestants. Previously, he worked in the semiconductor industry for Intel and Micron in photolithography, process control, and yield prediction and as a Wall Street quant building sentiment stock models for a hedge fund trading the S&P 1500 on news content on a 600 GPU cluster. Ben left finance and the semiconductor industry to work for Sequoia-backed startup HireVue, where he led the company’s machine learning efforts around digital interview prediction and adverse impact mitigation.

Presentations

What we’ve learned solving business problems with deep learning (sponsored by Dell EMC) Session

What if you could QA everything and make your best employees 10–100x more efficient? Ben Taylor shares real use cases of business transformation and realized value in production using deep learning and discusses some of the executive conversations and behaviors Dell EMC is seeing in the market.

Wee Hyong Tok is a principal data science manager with the AI CTO office at Microsoft, where he leads the engineering and data science team for the AI for Earth program. Wee Hyong has worn many hats in his career, including developer, program and product manager, data scientist, researcher, and strategist, and his track record of leading successful engineering and data science teams has given him unique superpowers to be a trusted AI advisor to customers. Wee Hyong coauthored several books on artificial intelligence, including Predictive Analytics Using Azure Machine Learning and Doing Data Science with SQL Server. Wee Hyong holds a PhD in computer science from the National University of Singapore.

Presentations

How to use transfer learning to bootstrap image classification and question answering (QA) Session

Transfer learning enables you to use pretrained deep neural networks and adapt them for various deep learning tasks (e.g., image classification, question answering, and more). Join Wee Hyong Tok and Danielle Dean to learn the secrets of transfer learning and discover how to customize these pretrained models for your own use cases.

Akhilesh Tripathi is the chief commercial officer at Digitate. Previously, Akhilesh was head of Canada for TCS, where he was responsible for setting up the Canadian entity and growing it to become the top 10 IT services company in Canada in a short span of time, and head of enterprise solutions and technology practices for TCS in North America, where he was responsible for TCS’s initiatives in these two areas across the continent. In his more than 20-year career with TCS, he also held leadership roles in the management of strategic alliances with software vendors and served on the advisory councils of several strategic vendor partners. Akhilesh is actively involved with not-for-profit organizations. He has served on the national board of IT Association of Canada (ITAC), the board of Canada India Business Council, the advisory board of ICTC’s coaching to career initiative, and the board of Operation Eyesight Universal, an international charitable organization dedicated to preventing and curing blindness in the developing world. Akhilesh holds an MBA and a bachelor’s degree in electronics. He lives in the San Francisco Bay Area with his wife and two children.

Presentations

Using machine learning in workload automation (sponsored by Digitate) Keynote

Our world is one of constantly changing business environments across various business units, limited end-to-end visibility, and high alerts. Join Maitreya Natu to learn how to use machine learning to identify root causes of problems in minutes instead of hours or days by automating routine tasks without scripting or preprogramming.

KC Tung is the principal data scientist at AT&T Advertising & Analytics, which is the premier business partner to AT&T AdWorks, the leader in addressable TV advertising. KC is the lead data scientist and key architect of many industry-first, novel, scalable open source model-based solutions to inventory planning, audience targeting or matching, cross-channel execution, testing, and optimization within advertising media. KC’s innovations leverage the latest machine learning and artificial intelligence (AI) algorithms to solve problems in audience measurement, attribution models, programmatic advertising, web browsing, bioinformatics, and social networks. His technical specialties include algorithms and methods that are important for programmatic advertising ecosystem, such as deep learning, LSTM, neural networks, gradient boosting, random forest, parametric and nonparametric Bayesian methods, stochastic processes, the Dirichlet process mixture multinomial model, and Markov chain Monte Carlo (MCMC). He is a sought-after collaborator and guest speaker at conferences. KC holds a PhD in molecular biophysics from the University of Texas Southwestern Medical Center in Dallas, TX.

Presentations

A novel adoption of LSTM in customer touchpoint prediction problems Session

KC Tung explains why LSTM provides great flexibility to model the consumer touchpoint sequence problem in a way that allows just-in-time insights about an advertising campaign's effectiveness across all touchpoints (channels), empowering advertisers to evaluate, adjust, or reallocate resources or investments in order to maximize campaign effectiveness.

Mike Tung is the CEO of Diffbot, an adviser at the Stanford StartX accelerator, and the leader of Stanford’s entry in the DARPA Robotics Challenge. In a previous life, he was a patent lawyer, a grad student in the Stanford AI lab, and a software engineer at eBay, Yahoo, and Microsoft. Mike studied electrical engineering and computer science at UC Berkeley and artificial intelligence at Stanford.

Presentations

Executive Briefing: Knowledge graphs for AI Session

Leveraging structured knowledge will be a critical ingredient in the design of the next wave of intelligent applications. Mike Tung offers an overview of the current open source and commercial knowledge graphs and explains how consumer and business applications are already taking advantage of these to provide intelligent experiences and enhanced business efficiency.

Ayin Vala is the founder of DeepMD and cofounder and chief data scientist at the nonprofit organization Foundation for Precision Medicine, where he and his research and development team work on statistical analysis and machine learning, pharmacogenetics, molecular medicine, and sciences relevant to the advancement of medicine and healthcare delivery. Ayin has won several awards and patents in the healthcare, aerospace, energy, and education sectors. Ayin holds master’s degrees in information management systems from Harvard University and mechanical engineering from Georgia Tech.

Presentations

Predicting Alzheimer’s: Generating neural networks to detect the neurodegenerative disease Session

Complex diseases like Alzheimer’s cannot be cured by pharmaceutical or genetic sciences alone, and current treatments and therapies lead to mixed successes. Ayin Vala explains how to use the power of big data and AI to treat challenging diseases with personalized medicine, which takes into account individual variability in medicine intake, lifestyle, and genetic factors for each patient.

Benjamin Vigoda is the CEO of Gamalon Machine Intelligence. Previously, Ben was technical cofounder and CEO of Lyric Semiconductor, a startup that created the first integrated circuits and processor architectures for statistical machine learning and signal processing. The company was named one of the “50 most innovative companies” by Technology Review and was featured in the Wall Street Journal, New York Times, EE Times, Scientific American, Wired, and other media. Lyric was successfully acquired by Analog Devices, and Lyric’s products and technology are being deployed in leading smartphones and consumer electronics, medical devices, wireless base stations, and automobiles. Ben also cofounded Design That Matters, a not-for-profit that for the past decade has helped solve engineering and design problems in underserved communities and has saved thousands of infant lives by developing low-cost, easy-to-use medical technology such as infant incubators, UV therapy, pulse oximeters, and IV drip systems that have been fielded in 20 countries. He has won entrepreneurship competitions at MIT and Harvard and fellowships from Intel and the Kavli Foundation/National Academy of Sciences and has held research appointments at MIT, HP, Mitsubishi, and the Santa Fe Institute. Ben has authored over 120 patents and academic publications. He currently serves on the DARPA Information Science and Technology (ISAT) steering committee. Ben holds a PhD from MIT, where he developed circuits for implementing machine learning algorithms natively in hardware.

Presentations

AI, customers, and ideas: Customer feedback management using next-generation AI (sponsored by Gamalon) Session

Ben Vigoda discusses idea learning, a new approach to deep learning. Idea learning can turn the customer journey into a personalized natural language conversation using deep, probabilistic, Turing-complete models that learn more from business people providing ideas and much less from supervised (labeled) data.

Executive Briefing: Is there a Moore’s law (not hardware related) for artificial intelligence Session

For AI to serve each individual customer, we need much more complex natural language understanding, ideas, and behaviors. Will compositional deep learning put us on a new curve?

Mary Wahl is a data scientist on Microsoft’s AI for Earth team, which helps NGOs apply deep learning to problems in conservation biology and environmental science. Mary has also worked on computer vision and genomics projects as a member of Microsoft’s algorithms and data science solutions team in Boston. Previously, Mary studied recent human migration, disease risk estimation, and forensic reidentification using crowdsourced genomic and genealogical data as a Harvard College Fellow.

Presentations

Distributed deep learning in the cloud: Build an end-to-end application involving computer vision and geospatial data Tutorial

High-resolution land cover maps help quantify long-term trends like deforestation and urbanization but are prohibitively costly and time intensive to produce. Mary Wahl and Banibrata De demonstrate how to use Microsoft’s Cognitive Toolkit and Azure cloud resources to produce land cover maps from aerial imagery by training a semantic segmentation DNN—both on single VMs and at scale on GPU clusters.

Yi-Chia Wang is a data scientist on the applied machine learning team at Uber. Yi-Chia’s research interests and skills combine language processing technologies, machine learning methodologies, and social science theories to analyze large-scale data and understand social behavior in online environments. Previously, she worked on question answering and information retrieval. Yi-Chia holds a PhD from the Language Technologies Institute, part of the School of Computer Science at Carnegie Mellon University. Her thesis developed a new machine learning model to measure self-disclosure in social networking site communication at scale and used it to better understand the contexts in which users disclose more or less about themselves.

Presentations

Improving customer support with natural language processing and deep learning Session

Uber has implemented an ML and NLP system that suggests the most likely solutions to a ticket to its customer support representatives, making them faster and more accurate while providing a better user experience. Piero Molino, Huaixiu Zheng, and Yi-Chia Wang describe how Uber built the system with traditional and deep learning models and share the lessons learned along the way.

Richard Wei is a software engineer on the Google Brain team at Google, working on APIs and automatic differentiation for Swift for TensorFlow. Previously, he worked on Siri at Apple. Richard studied computer science and linguistics at the University of Illinois at Urbana-Champaign, where he developed compiler technologies for machine learning.

Presentations

Swift for TensorFlow and TensorFlow Lite (sponsored by Google) Session

Richard Wei and Andrew Selle discuss Swift for TensorFlow and TensorFlow Lite, covering the current status of development and the latest developments. They then teach you how to prepare your model for mobile and how to write code that executes it on a variety of different platforms.

Michael Weinberg is the chief investment officer and a senior managing director of MOV37 and Protégé Partners. Michael has 25 years of experience investing directly at the security level and indirectly as an asset allocator in traditional and alternative assets. His portfolio management experience includes Soros Fund Management LLC, Credit Suisse First Boston, and Financial Risk Management (FRM). Previously, he was a research analyst at Dean Witter (now part of Morgan Stanley). Michael is a board member of AIMA and a member of the Economic Club of New York, serves on the management advisory council for the Michael Price Student Investment Fund, and was formerly the chair of value investing at CFANY, where he received multiple awards. He is a frequent keynote speaker at conferences and universities and a published author. He has been interviewed by top financial newspapers. Michael holds an MBA from Columbia Business School, where he is now an adjunct professor of finance and economics, and a BS in economics from New York University.

Presentations

Machine learning in investment management

There are immense opportunities to apply machine learning to investment management if you know where to look. Unlike many Silicon Valley challenges, it's not simply a matter of throwing capital and PhDs at the financial markets. Michael Weinberg explains why you must exploit domain expertise to achieve disruptive success.

Daniel Whitenack is a PhD-trained data scientist and engineer at Pachyderm. His industry experience includes developing data science applications, such as predictive models, dashboards, recommendation engines, and more, for large and small companies. Daniel has spoken at conferences around the world, including Applied ML Days, Spark Summit, PyCon, ODSC, and GopherCon. He maintains the Go kernel for Jupyter and is actively helping to organize contributions to various open source data science projects.

Presentations

AI on Kubernetes Tutorial

Kubernetes—the container orchestration engine used by all of the top technology companies—was built from the ground up to run and manage highly distributed workloads on huge clusters. Thus, it provides a solid foundation for model development. Daniel Whitenack demonstrates how to easily deploy and scale AI/ML workflows on any infrastructure using Kubernetes.

Meredith Whittaker is a distinguished research scientist at New York University and the founder of Google’s Open Research Group. Meredith is also the cofounder and codirector of the AI Now Institute at NYU, dedicated to researching the social implications of artificial intelligence and related technologies in an interdisciplinary context. She has over a decade of experience working in industry, leading product and engineering teams. Meredith cofounded M-Lab, a globally distributed network measurement system that provides the world’s largest source of open data on internet performance. M-Lab is one of the primary resources for policymakers and researchers investigating issues of net neutrality and network performance. She has worked extensively on issues of privacy and security, advising on both policy direction and technical implementation. She cofounded Simply Secure, helped build and currently advises the Open Technology Fund, and led work to strengthen the security of critical internet infrastructure. She has advised the White House, the FCC, the City of New York, the European Parliament, and many other governments and civil society organizations on artificial intelligence, internet policy, measurement, privacy, and security.

Presentations

AI, Neuroscience, and the Ethics of Automating ‘Normal’ Keynote

Keynote by Meredith Whittaker

Casimir Wierzynski is a senior director in the Artificial Intelligence Product Group at Intel, where he leads research efforts to identify, synthesize, and incubate emerging technologies that will enable the next generation of AI systems. Previously, Cas led research teams in neuromorphic computing, learning and AI planning, and autonomous robotics at Qualcomm and was a vice president at Goldman Sachs, where he traded fixed income and credit derivatives. Driven by his passion for AI and the brain, Cas left finance to get a PhD in computation and neural systems from Caltech, where he used large-scale neural recordings to study the relationship between memory consolidation and sleep. Cas also holds a BS and MS in electrical engineering from MIT (he completed his master’s thesis at AT&T Bell Labs) and a BA in mathematics from Cambridge University, where he was a British Marshall Scholar.

Presentations

Trends in AI systems Session

One of the biggest challenges in AI is how to translate advances in the lab into large-scale applications. Casimir Wierzynski reviews current trends in the field and shares case studies to illustrate why codesigning these components in concert will be critical for building the AI systems of the future.

Edd Wilder-James is a strategist at Google, where he is helping build a strong and vital open source community around TensorFlow. A technology analyst, writer, and entrepreneur based in California, Edd previously helped transform businesses with data as vice president of strategy for Silicon Valley Data Science. Formerly Edd Dumbill, Edd was the founding program chair for the O’Reilly Strata conferences and chaired the Open Source Convention for six years. He was also the founding editor of the peer-reviewed journal Big Data. A startup veteran, Edd was the founder and creator of the Expectnation conference-management system and a cofounder of the Pharmalicensing.com online intellectual-property exchange. An advocate and contributor to open source software, Edd has contributed to various projects such as Debian and GNOME and created the DOAP vocabulary for describing software projects. Edd has written four books, including Learning Rails from O’Reilly.

Presentations

Building AI with TensorFlow: An Overview (sponsored by Google) Session

TensorFlow is one of the world’s biggest open source projects, and it continues to grow in adoption and functionality. Laurence Moroney, Edd Wilder-James, and Sandeep Gupta share major recent developments and highlight some future directions. Join in to learn how you can become more involved in the TensorFlow community.

Lucas A. “Luke” Wilson is a data scientist and artificial intelligence researcher in Dell EMC’s HPC and AI Engineering Group, focusing on developing hardware configurations and software solutions for deep learning problems. Previously, he was the director of training and professional development at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin and a member of TACC’s High-Performance Computing Group working on performance profiling and optimization, including early performance optimization work on both the first- and second-generation Intel Xeon Phi processors. Luke has been involved in research and development related to parallel and distributed nature-inspired algorithms for more than 15 years, including using genetic algorithms, artificial immune systems, and artificial neural networks for efficiently solving complex scheduling, categorization, prediction, and design problems. Luke holds a BS and MS in computer science from Texas A&M University-Corpus Christi and a PhD in computer science from the University of Texas at San Antonio.

Presentations

Efficient neural network training on Intel Xeon-based supercomputers Session

Vikram Saletore and Luke Wilson discuss a collaboration between SURFSara and Intel to advance the state of large-scale neural network training on Intel Xeon CPU-based servers, highlighting improved time to solution on extended training of pretrained models and exploring how various storage and interconnect options lead to more efficient scaling.

Alexander Wong is chief scientist at DarwinAI, a Waterloo-based startup that enables deep learning optimization and explainability by way of its patented Generative Synthesis technology, as well as the Canada Research Chair in Artificial Intelligence and Medical Imaging, a founding member of the Waterloo Artificial Intelligence Institute, and an associate professor in the Department of Systems Design Engineering at the University of Waterloo. Alex has published over 450 refereed journal and conference papers and holds patents in various fields such as computational imaging and artificial intelligence. He has received numerous awards for his work in artificial intelligence, including best paper awards at the prestigious NIPS conference in 2017 and 2016 on transparent and interpretable machine learning and efficient methods for deep neural networks, respectively.

Presentations

Slaying the beasts of scalability and explainability Session

Alex Wong discusses some of the operational challenges associated with scalability and explainability in deep learning for real-world operational scenarios and explains how these are being tackled to enable more seamless, accessible, deployable, and transparent deep learning design and development through advancements made in the respective areas.

Candace Worley is vice president and chief technical strategist at McAfee, where she manages a worldwide team of technical strategists responsible for driving thought leadership and advancing technical innovation in McAfee security solutions. During her time at McAfee, Candace has held a number of technology leadership positions, including five and a half years as the senior vice president and general manager of the Enterprise Endpoint Security business. Previously, Candace was vice president for enterprise solutions for Intel’s Security Group, where she had worldwide responsibility for all facets of product and vertical marketing for the complete corporate products solutions set, and spent seven years with Mentor Graphics, where she led a team of product managers responsible for electronic design automation and electronic component software. She holds a bachelor’s degree in management from Oregon State University and an MBA from Marylhurst University.

Presentations

Human-machine teaming: Why the human element will always be indispensable in cybersecurity Session

The future isn’t about AI or machine learning; it’s about human-machine teaming. Candace Worley explains why, as long as there are human adversaries behind cybercrime and cyberwarfare, there will always be a critical need for the human beings teamed with machines in cybersecurity.

Cathy Wu works at the intersection of machine learning, optimization, and large-scale societal systems. Her recent research focuses on mixed autonomy systems in mobility, which studies the complex integration of automation such as self-driving cars into existing urban systems. She is interested in developing computational tools for reliable and complex decision making in critical societal systems. Cathy will be joining MIT as an assistant professor in 2019. Cathy holds a PhD in EECS from UC Berkeley, where she was part of the Berkeley AI Research lab, DeepDrive, California PATH, and RISELab, and a BS and MEng in EECS from MIT.

Presentations

Reinforcement learning for mixed autonomy mobility Session

Using novel techniques in model-free deep reinforcement learning and control theory, Cathy Wu explores and quantifies the potential impact of a small fraction of automated vehicles on low-level traffic flow dynamics, such as congestion on a variety of important traffic contexts.

Jian Wu is a full stack engineer at NIO US. Before joining NIO, he was a data analytics developer at Samsung SDSRA’s AI and Machine Learning Lab working on machine learning projects using Kubernetes, Python, and TensorFlow, he also developed Web UI and Dashboard using JavaScript with AngularJS, D3.js, and Bootstrap. Jian has been a software developer in the San Francisco Bay Area for 20+ years, he developed a device gateway and a REST-style WS server when working at Netflix, developed a payment gateway when working at eBay/PayPal, and worked at Oracle for 8+ years developing Java middle-tier server and applications.

Presentations

Evaluate deep Q-learning for sequential targeted marketing with 10-fold cross-validation Session

Jian Wu discusses an end-to-end engineering project to train and evaluate deep Q-learning models for targeting sequential marketing campaigns using the 10-fold cross-validation method. Jian also explains how to evaluate trained DQN models with neural network-based baseline models and shows that trained deep Q-learning models generally produce better-optimized long-term rewards.

Kevin Wu is an engineer at Cardiogram, a startup that applies deep learning to wearable data. Previously, Kevin worked at Two Sigma and Credit Suisse. He holds a BS and MS in computer science from Stanford University.

Presentations

Debuggable deep learning Session

Deep learning is often called a black box, so how do you diagnose and fix problems in a deep neural network (DNN)? Avesh Singh and Kevin Wu explain how they systematically debugged DeepHeart, a DNN that detects cardiovascular disease from heart rate data. You'll leave with an arsenal of tools for debugging DNNs, including Jacobian analysis, TensorBoard, and DNN unit tests.

Zhou Xing is the director of artificial intelligence for autonomous driving at Borgward R&D Silicon Valley, where he is in charge of the R&D on artificial intelligence for self-driving car technologies. Zhou is an exceptional scientist, and AI researcher and an experimentalist who always believe in data and experimental results. Previously, he was an engineering physicist and staff scientist at Stanford, where he worked at the SLAC National Accelerator Laboratory developing and managing an in-house software system for data analysis, machine learning, sensor and detector read-out, and big data, and a staff data scientist at NIO USA, where he focused on developing state-of-the-art deep learning and reinforcement learning algorithms for autonomous driving vehicles. An innovative entrepreneur, Zhou founded his own startup company Athena Robotics and served as the CTO in charge of full stack of self-driving car software technologies, including perception, prediction, simulation, and planning. Athena Robotics was selected by Y Combinator for the summer batch of 2018. Zhou’s outstanding publication record includes more than 15,000 Google Scholar citations in top-notch science and AI journals and conferences. He is also a frequent invited speaker on the applications of deep neural networks. Zhou holds a BS in physics from the University of Science and Technology of China (USTC), which has the most prestigious physics department in China, and a PhD in experimental particle physics at CERN, France and Switzerland, affiliated with Syracuse University, New York, where his thesis was focusing on using neural network to solve leading-edge scientific problems, including CP violation after the Big Bang.

Presentations

Predicting short-term driving intention using recurrent neural network on sequential data Session

Predicting driver intention and behavior is of great importance for the planning and decision-making processes of autonomous driving vehicles. Zhou Xing shares a methodology that can be used to build and train a predictive driver system, helping to learn on-road drivers' intentions, behaviors, associated risks, etc.

Torry Yang is an engineer at Google, where he works to improve the experience of machine learning developers on Google Cloud. Recently, he has worked on Cloud AutoML, BigQuery ML, and Cloud ML Engine.

Presentations

Cloud AutoML: Customize machine learning models with your own data (sponsored by Google) Session

Cloud AutoML enables developers with limited machine learning expertise to train high-quality models specific to their business needs, by leveraging Google’s state-of-the-art transfer learning and neural architecture search technology. Torry Yang explores the AutoML Vision, Translate, and Natural Language services and APIs and demonstrates how powerful and easy they are to use.

Mariya Yao is chief technology and product officer at Metamaven, a company that intelligently automates revenue growth for global companies like Paypal, LinkedIn, L’Oréal, LVMH, and WPP. She’s also editor-in-chief of TOPBOTS, the largest publication and community for business leaders applying AI to their enterprises, a Forbes writer covering the interplay of human and machine intelligence, and coauthor of Applied AI: A Handbook for Business Leaders, which she launched onstage at CES 2018.

Presentations

Executive Briefing: Organizational design for effective AI Session

Executives are often asked to "innovate with AI," but barriers to successful adoption for most enterprises are organizational, not technical. Mariya Yao explains why effective AI requires not only technical talent but extended interdisciplinary coordination between teams, investments in retraining your workforces at all levels, and cultivation of an experimental, data-driven culture.

Ting-Fang Yen is director of research at DataVisor, the leading fraud, crime, and abuse detection solution utilizing unsupervised machine learning to detect fraudulent and malicious activity such as fake account registrations, fraudulent transactions, spam, account takeovers, and more. She has over 10 years of experience in applying big data analytics and machine learning to tackle problems in cybersecurity. Ting-Fang holds a PhD in electrical and computer engineering from Carnegie Mellon University.

Presentations

Deep learning for large-scale online fraud detection Session

Online fraud is often orchestrated by organized crime rings, who use malicious user accounts to actively target modern online services for financial gain. Ting-Fang Yen shares a real-time, scalable fraud detection solution backed by deep learning and built on Spark and TensorFlow and demonstrates how the system outperforms traditional solutions such as blacklists and machine learning.

Ping Yu is a senior engineer on the Google Brain team and a core team member of TensorFlow.js at Google. Previously, he was a TL for Google Attribution.

Presentations

TensorFlow for JavaScript (sponsored by Google) Session

TensorFlow.js is the recently released JavaScript version of TensorFlow that runs in the browser and Node.js. Nick Kreeger and Ping Yu offer an overview of the TensorFlow.js ML framework and demonstrate how to perform the complete machine learning workflow, including training, client-side deployment, and transfer learning.

Wojciech Zaremba is a cofounder of OpenAI, where he leads the robotics team, which is working on developing general-purpose robots via new approaches to transfer learning and teach robots unprecedentedly complex behaviors. Previously, he spent a year at Facebook AI Research and a year at Google Brain. He holds an MS from Warsaw University/École Polytechnique and a PhD from New York University, completed under Rob Fergus and Yann LeCun. Wojciech’s research contributions include using neural network techniques to get computers to learn sophisticated algorithms from raw data—specifically, applying translation models to computer programs and building the first neural Turing machines with discrete actions. He has also worked on the discovery of adversarial examples, improved training of GANs, and the development of OpenAI gym.

Presentations

Deep reinforcement learning for robotics Session

Woj Zaremba discusses deep reinforcement learning for robotics.

Mazen Zawaideh is Chief Radiology Resident at the University of Washington. He is also co-founder and co-instructor of imagedeep.io, an intensive course for radiology residents designed to bridge the gap between medical imaging and AI education. He received his BS and MD from UCSD.

Presentations

Building intelligent mobile applications in healthcare Tutorial

Xiaoyong Zhu, Gheorghe Iordanescu, Wilson Lee, and Ivan Tarapov walk you through building a deep learning model and intelligent applications on edge devices running iOS, Android, and Windows, using a working example that helps clinicians in areas with less access to radiologists identify possible lung diseases.

Huaixiu Zheng is a data scientist at Uber, where he’s a major contributor to several ongoing efforts at Uber using deep learning-based NLP, ML, and AI technologies to empower the intelligent business operations. Huaixiu has made significant contributions in the fields of quantum waveguide QED, quantum phase transition in dissipative environments, and photonic quantum computation. Previously, he was a postdoctoral researcher at Yale University, where he worked on quantum error corrections and topological quantum computation. He has published more than 25 journal and conference papers in prestigious journals such as Nature, Nature Physics, and Physical Review Letters, and has more than 1,000 citations. He received prestigious academic and industrial awards, including the Chinese Government Award for Outstanding Self-Financed Students Abroad, the John T. Chambers Scholarship, a second-place award from the SPIE-AAPM-NCI Prostate MR Classification Challenge, a second-place award for the SPIE-AAPM-NCI Prostate MR Gleason Grade Group Challenge, and the second prize (as part of team Future Lifecare) of the 8th Intelligent System Summit & TEEC Cup Startup Contest. He holds a PhD in quantum physics and quantum computation from Duke University.

Presentations

Improving customer support with natural language processing and deep learning Session

Uber has implemented an ML and NLP system that suggests the most likely solutions to a ticket to its customer support representatives, making them faster and more accurate while providing a better user experience. Piero Molino, Huaixiu Zheng, and Yi-Chia Wang describe how Uber built the system with traditional and deep learning models and share the lessons learned along the way.

Xiaoyong Zhu is a senior data scientist at Microsoft, where he focuses on distributed machine learning and its applications.

Presentations

Building intelligent mobile applications in healthcare Tutorial

Xiaoyong Zhu, Gheorghe Iordanescu, Wilson Lee, and Ivan Tarapov walk you through building a deep learning model and intelligent applications on edge devices running iOS, Android, and Windows, using a working example that helps clinicians in areas with less access to radiologists identify possible lung diseases.

Shelley Zhuang is founder and managing partner at 11.2 Capital. Shelley has over 15 years of experience in technology as a software engineer, research scientist, business executive, and venture capitalist. Previously, Shelley was EVP of business development at Ecoplast Technologies, where she oversaw business development and sales efforts in North America, and a principal at DFJ, where she was actively involved in a number of investments, including Ecoplast Technologies, FeedBurner (acquired by Google for $100M), Flurry (acquired by Yahoo for $240M), PPLive (acquired by Suning for $420M), TicketsNow (acquired by Ticketmaster for $265M), Xfire (acquired by Viacom for $102M), YeePay. Shelley is a techie at heart. She is currently an advisor at Skydeck and ML7 associate at Creative Destruction Lab. She also served on Enigma 2016’s program committee. Shelley holds a BS in computer science and computer engineering from the University of Missouri and a PhD in computer science from the University of California, Berkeley.

Presentations

Data-driven healthcare Session

Given the revolution in data and healthcare, Shelley Zhuang predicts how certain sectors may unfold and shares opportunities for innovation. Along the way, Shelley discusses innovations that advance precision medicine by bringing together interdisciplinary fields across biology, engineering, data science, and clinical care.

Neta Zmora is a deep learning research engineer at the Intel AI Lab, where he wrote Distiller, an open source Python package for neural network compression research. Previously, Neta was the lead software architect of Intel’s Computer Vision Group DL software stack.

Presentations

Neural Network Distiller: A PyTorch environment for neural network compression Session

Neta Zmora offers an overview of Distiller, an open source Python package for neural network compression research. Neta discusses the motivation for compressing DNNs, outlines compression approaches, and explores Distiller's design and tools, supported algorithms, and code and documentation. Neta concludes with an example implementation of a compression research paper.

Liran Zvibel is cofounder and CEO at WekaIO, where he guides the company’s long-range technical strategies. Previously, Liran ran engineering at social startup and Fortune 100 organizations including Fusic, where he managed product definition, design, and development for a portfolio of rich social media applications and was responsible for the principal architecture of the hardware platform, clustering infrastructure, and overall systems integration for XIV Storage System (acquired by IBM in 2007). He holds a BSc in mathematics and computer science from Tel Aviv University.

Presentations

A next-generation NVMe-native parallel filesystem for accelerating AI workloads (sponsored by WekaIO) Session

Artificial intelligence requires a low-latency, high-throughput storage system to keep the compute layer fully saturated with data. Liran Zvibel demonstrates why NVMe-optimized, distributed filesystems are ideal storage solutions to support AI applications and introduces a next-gen massively parallel shared filesystem that's NAND flash and NVMe optimized, built to solve the I/O starvation problem.