14–17 Oct 2019
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Speakers

Hear from innovative researchers, talented CxOs, 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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Alex Adam is a data scientist at Faculty. He’s particularly interested in generative neural networks and their applications both in natural language processing (text generation) and computer vision (video generation). Previously, he’s worked on many projects across sectors including retail, marketing, civil engineering, and private equity. The highlights of his career include his work being showcased at the Copenhagen Democracy Summit, presenting at O’Reilly conferences, BBC events, and being featured on BBC Newswround. Alex holds a PhD in theoretical physics from Imperial College London.

Presentations

The era of deepfake defense Session

In just a few years, it'll be possible to create synthetic videos indistinguishable to both eye and ear from reality. While the debate around "fake news" often leads us to question the meaning of truth, this deepfake technology is starting to pose a distinct and even more dramatic risk in the form of a new kind of political disinformation. Alex Adam dives into how to tackle this.

Sridhar Alla is a cofounder and CTO at BlueWhale, which brings together the worlds of big data and artificial intelligence to provide comprehensive solutions to meet the business needs of organizations of all sizes. He and his team are cloud and tool agnostic and strive to embed themselves into the work stream to provide strategic and technical assistance. Sridhar is also an avid speaker, author, and coach. He lives in southern New Jersey with his wife and daughter.

Presentations

Anomaly detection using deep learning to measure the quality of large datasets Session

Any business, big or small, depends on analytics, whether the goal is revenue generation, churn reduction, or sales or marketing purposes. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. Sridhar Alla examines some techniques used to evaluate the quality of data and the means to detect the anomalies in the data.

Alasdair Allan is a director at Babilim Light Industries and a scientist, author, hacker, maker, and journalist. An expert on the internet of things and sensor systems, he’s famous for hacking hotel radios, deploying mesh networked sensors through the Moscone Center during Google I/O, and for being behind one of the first big mobile privacy scandals when, back in 2011, he revealed that Apple’s iPhone was tracking user location constantly. He has written eight books, and writes regularly for Hackster.io, Hackaday, and other outlets. A former astronomer, he also built a peer-to-peer autonomous telescope network that detected what was, at the time, the most distant object ever discovered.

Presentations

Measuring embedded machine learning Session

The future of machine learning is on the edge and on small, embedded devices that can run for a year or more on a single coin-cell battery. Alasdair Allan dives deep into how using deep learning can be very energy efficient and allows you to make sense of sensor data in real time.

Krishna Anumalasetty is a principal program manager for Azure Machine Learning at Microsoft. He’s worked as a program and product manager at Azure cloud services and AI org for the last eight years, enabling enterprise customers with on-premises and cloud hybrid scenarios, helping them scale up and out in the cloud and implement security protections and easy to deploy ML models in the cloud. Krishna is a founding member of Microsoft’s AutoML team. He holds a master’s degree in computer science from Arizona State University.

Presentations

Time series forecasting: Build and deploy your ML models to forecast the future 2-Day Training

Francesca Lazzeri, Wee Hyong Tok, Krishna Anumalasetty, and Aashish Bhateja walk you through the core steps of training your machine learning time series forecasting models using Python and Azure Machine Learning both locally and on remote compute resources.

Sacha Arnoud is a senior director of engineering at Waymo, a self-driving technology company with a mission to make it safe and easy for people and things to move around. Sacha oversees the company’s perception efforts from multisensor configuration to signal processing, advanced and industrialized machine learning to complex scene semantics understanding. Sacha has twenty years of industry experience in Silicon Valley, from leading development on Sun Microsystems first distributed large-scale archival storage system to pioneering the use of deep learning-based computer vision at Google Maps using Street View imagery. Sacha earned a bachelor’s degree from the Ecole Polytechnique, France, and a master’s in telecommunication systems and computer science from Telecom ParisTech, France.

Presentations

The rise of machine learning for autonomous vehicles Session

To navigate city streets, self-driving vehicles need a deep semantic understanding of the world around us. Sacha Arnoud explores how Waymo uses deep learning to unlock new capabilities and build safe autonomous vehicles and provides an overview of how Waymo is thinking about developing machine learning at scale as it expands to new cities and geographies.

Zahra Ashktorab is a research staff member at IBM Thomas J. Watson Center. At IBM Research, she studies social technologies, AI systems, and their influence on user behavior and interaction. Her interests and prior work lie at the intersection of machine learning, human-computer interaction (HCI), and design. She uses a mix of quantitative and qualitative methods in her research to address HCI-related questions and interaction design. She has published her work at the ACM Conference on Human Factors and Computing Systems (CHI), the ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW), and other reputable HCI and information systems conferences. She received her PhD on human-computer interaction at the University of Maryland, College Park.

Presentations

The intersection of AI and HCI: Gamifying the latest artificial intelligence research Session

Casey Dugan and Zahra Ashktorab challenge you to guess the backdoor of a hacked classifier. Join them to learn more about novel AI technologies through the design and development of engaging games. Take a look at their latest research around improving the interactions between humans and AI systems from empathy building to feedback design.

Ricardo Baeza-Yates is CTO at NTENT, where he oversees the technical vision of the company. Previously, he was vice president and chief research scientist at Yahoo; was a professor, founded and acted as director of the Center for Web Research, and twice served as Computer Science Department chair at Universidad de Chile; and was a professor and founded and acted as director of the Web Research Group at Universitat Pompeu Fabra. Ricardo is an ACM and IEEE Fellow with over 500 publications, tens of thousands of citations, multiple awards, and several patents. He’s coauthored several books including Modern Information Retrieval, the most widely used textbook on search. He earned bachelor’s and master’s degrees in computer science and electrical engineering from the University of Chile and a PhD in computer science from the University of Waterloo.

Presentations

Executive Briefing: How the growth of voice-based AI stands to blur the lines of big data Session

Voiced-based AI continue to gain popularity among customers, businesses, and brands but it’s important to understand that, while it presents a slew of new data at our disposal, the technology is still in its infancy. Ricardo Baeza-Yates examines three ways voice assistants will make big data analytics more complex and the various steps you can take to manage this in your company.

Bahman Bahmani is the vice president of data science and engineering at Rakuten (the seventh-largest internet company in the world), managing an AI organization with engineering and data science managers, data scientists, machine learning engineers, and data engineers globally distributed across three continents, and he’s in charge of the end-to-end AI systems behind the Rakuten Intelligence suite of products. Previously, Bahman built and managed engineering and data science teams across industry, academia, and the public sector in areas including digital advertising, consumer web, cybersecurity, and nonprofit fundraising, where he consistently delivered substantial business value. He also designed and taught courses, led an interdisciplinary research lab, and advised theses in the Computer Science Department at Stanford University, where he also did his own PhD focused on large-scale algorithms and machine learning, topics on which he’s a published author.

Presentations

Executive Briefing: Business at the speed of AI Session

Amid fears of sentient killing robots and a freezing AI winter, AI has a true potential to transform the enterprise. Actualizing this potential requires a well-informed organizational strategy and consistent execution of best practices regarding people, processes, and platforms. Bahman Bahmani examines these strategies and best practices and provides insights into their successful execution.

Antje is a technical evangelist for AI and machine learning at AWS and is based in Düsseldorf, Germany. Besides AI/ML, Antje is passionate about showing developers how to leverage and integrate with (big) data, containers, Kubernetes, and cloud-native technologies. Prior to joining AWS, Antje worked in technical evangelist and solutions engineering roles at MapR and Cisco. She frequently speaks at conferences and meetups around the world. Antje is a co-founder of the Düsseldorf chapter of Women in Big Data.

Presentations

Containerized architectures for deep learning Session

Container and cloud native technologies around Kubernetes have become the de facto standard in modern ML/AI application development. Antje Barth examines common architecture blueprints and popular technologies used to integrate AI into existing infrastructures and how you can build a production-ready containerized platform for deep learning.

Martin Benson is the head of AI consulting with Jaywing, where he invented the algorithm that underpins Jaywing’s new patent-pending AI product—Archetype, which enables lenders to produces interpretable AI credit-scoring models—and is recognized for driving business value from data and developing relevant technology that directly grows business opportunities. He’s led a number of game-changing data science products to discover new business opportunities for Jaywing and its clients, spearheading commercial applications of machine learning, statistical analysis, data mining, and modeling for clients like Avios and Swinton. Martin has a passion for turning data into products, actionable insights, and meaningful stories and is recognized both internally and externally as an expert to solve challenging data problems. He thrives on sharing his knowledge with others and has recently been invited to be an ambassador for the leading deep learning MOOC by Coursera, assisting people in learning about AI. Martin was also a DataIQ Talent Awards finalist for the data science leader category. A trained mathematician with a master’s degree and PhD in mathematics, and leading expert in driving business value from data, Martin is at the forefront of AI and data science.

Presentations

Fairness in AI: Applying deep learning to credit scoring Session

Machine learning has been used in credit scoring for three decades. Martin Benson explores the history of machine learning in credit scoring and the need for explainable and justified decisions made by machine learning systems. Come find out if it's possible to overcome the black box problem and more about how machine learning systems are evolving and how to bypass the challenges to adoption.

Aashish Bhateja is a senior program manager working on Microsoft Azure Machine Learning—building an exciting machine learning service that makes it easy for all data scientists and ML engineers to create and deploy robust, scalable, and highly available machine learning web services in the cloud.

Presentations

Time series forecasting: Build and deploy your ML models to forecast the future 2-Day Training

Francesca Lazzeri, Wee Hyong Tok, Krishna Anumalasetty, and Aashish Bhateja walk you through the core steps of training your machine learning time series forecasting models using Python and Azure Machine Learning both locally and on remote compute resources.

Rajib Biswas is a lead data scientist with Ericsson (global AI accelerator). He has 10 years of industry experience into AI- and ML-based product development and research and has applied AI and ML to solve problems related to domains like finance, telecom, and consumer electronics. He earned his master’s in computer science from the BITS-Pilani campus.

Presentations

Adversarial network for natural language synthesis Session

Rajib Biswas outlines the application of AI algorithms like generative adversarial networks (GANs) to solve natural language synthesis tasks. Join in to learn how AI can accomplish complex tasks like machine translation, write poetry with style, read a novel, and answer your questions.

Paris Buttfield-Addison is a 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, the BAFTA- and IGF-winning Night in the Woods, the Qantas airlines Joey Playbox games, and the Yarn Spinner narrative game framework. Previously, Paris was a mobile product manager for Meebo (acquired by Google). Paris particularly enjoys game design, statistics, blockchain, machine learning, and human-centered technology. He researches and writes technical books on mobile and game development (more than 20 so far) for O’Reilly and is writing Practical AI with Swift and Head First Swift. He holds a degree in medieval history and a PhD in computing. You can find him on Twitter as @parisba.

Presentations

Building, teaching, and training simulations for machine learning with a game engine Session

You're building a high-volume, expensive, robot-driven warehouse. Your robots need to get to the right place quickly, find the right item, and sort it to the right place without colliding with each other, the shelves, or people. But you don't have any robots, and you need to start writing the logic and training them. Paris Buttfield-Addison and Tim Nugent outline how to use a simulation to do it.

Practical on-device AI and ML using Swift Session

On-device ML and AI is the future for privacy-conscious, cloud-averse users of modern smartphones. Paris Buttfield-Addison and Tim Nugent explore what's possible using CoreML, Swift, and associated frameworks, and the powerful ML-tuned silicon in modern Apple iOS hardware. They demonstrate and create ML and AI features with Swift to show how much you can do without touching the cloud.

Umit Mert Cakmak is a manager and senior data scientist on the data science elite team at IBM. Umit excels at helping clients solve complex data science problems from inception to the delivery of deployable machine learning and AI pipelines. His research spans across multiple disciplines, and he enjoys sharing his insights at conferences, universities, and meetups.

Presentations

Executive Briefing: Why your AI initiative will fail Session

In every AI initiative, there’s a demand from businesses to protect or increase market share or decrease operational costs. Your competitors are a growing threat, seemingly adopting new technologies better than you. Umit Cakmak examines critical steps from countless client engagements on how to consistently deliver successful AI projects.

Douglas Calegari is a director of architecture and strategic development at a fortune 500 insurance company . In a career that has spanned several decades, Doug has worked for multiple startup companies, insurance, and financial services institutions.

Presentations

Service center automation using the state-of-the-art NLP Session

Douglas Calegari details a solution that classifies and routes emails coming into a busy insurance service center. Join in to discover how his team evaluated NLP models, leveraged various techniques to increase classification and entity recognition accuracy, designed a scalable end-to-end machine learning data pipeline, and integrated them into an existing transactional system.

Roger Chen is cofounder and CEO of Computable 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 realm 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

Thursday Opening Welcome Keynote

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

Wednesday Opening Welcome Keynote

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

Ira Cohen is a cofounder and chief data scientist at Anodot, where he’s 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

Herding cats: Product management in the machine learning era Tutorial

While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores the cycle of developing ML-based capabilities (or entire products) and the role of the (product) manager in each step of the cycle.

Sequence to sequence (S2S) modeling for time series forecasting Session

Sequence to sequence (S2S) modeling using neural networks has become increasingly mainstream in recent years. In particular, it's been leveraged for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for these use cases, visualization, real-time anomaly detection, and forecasting.

A data scientist and TensorFlow addict, Robert has a passion for helping developers quickly learn what they need to be productive. He’s used TensorFlow since the very early days and is excited about how it’s evolving quickly to become even better than it already is. Before moving to data science Robert led software engineering teams for both large and small companies, always focusing on clean, elegant solutions to well-defined needs. In his spare time Robert sails, surfs occasionally, and raises a family.

Presentations

TFX: Production ML pipelines with TensorFlow Tutorial

Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe explores Google's open source community TensorFlow Extended (TFX), an open source version of the tools and libraries that Google uses internally, made using its years of experience in developing production ML pipelines.

Alexis Crowell is senior director of artificial intelligence product marketing at Intel, where she and her team are responsible for technical positioning and messaging as well as outbound content and campaigns for Intel AI products. Alexis and her team partner with AI adopters across the industry from small device implementations to HPC clusters to launch products, showcase innovative use cases, and help other companies find their own AI path. She has an unyielding passion to deliver technology solutions that help businesses thrive. Over her rich career, she has run a cloud software engineering team focused on distributed computing and microservices integration, led the open source marketing efforts from Intel, and worked with many of the Fortune 100 companies to help incubate service offerings and deliver innovative products.

Presentations

Keynote by Alexis Crowell Keynote

Details to come.

Thursday Opening Welcome Keynote

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

Wednesday Opening Welcome Keynote

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

Michael Cullan holds a Masters in Statistics and has 4 years of research experience spanning topics in nonparametric statistics, applied mathematics, and artificial intelligence. He has 3 years of teaching experience in academic and professional settings. He combines a passion for teaching and statistical programming as a Data Scientist in Residence at The Data Incubator.

Presentations

Deep Learning with TensorFlow 2-Day Training

The TensorFlow library provides computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Michael Cullan walks you through TensorFlow's capabilities in Python from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.

Tim Daines is a principle designer with QuantumBlack. He’s also a proactive designer, telling the stories of human-centered explainable AI, ML, an IoT experiences across a variety of industries, including digital health, energy, elite sports, and learning. He has over ten years’ experience working closely with humans to design and bring to market digital products and services, and he’s an innovative person who enjoys developing products and services across the entire end-to-end human experience, creating lasting experiences, and determining the best solutions to problems through insight discovery and journey mapping. His passion for creating better experiences for people drives all aspects of his designs and naturally aligns with companies who want to understand how their customers and employees interact with their product and services. Tim enjoys turning research gained through stakeholder workshops and meetings into designs that deliver significant value to humans to develop trust and loyalty. He holds master’s degrees in user experience design, and social science and research practices, and has worked with a range of companies from startups to global blue-chip companies across the US, UK, Europe, and Asia.

Presentations

Executive Briefing: Fusing data and design Session

UX and DS can collaborate effectively when built with advanced analytics. Tim Daines and Daniel First detail best practices (via a case study for building an optimization algorithm for natural resource production) of how data science and design work in tandem to create adoptable data-driven products that feel intuitive to users, as well as deliver powerful insights into business operations.

Danielle Dean is the technical director of machine learning at iRobot. Previously, she was a principal data science lead at Microsoft. She holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill.

Presentations

Azure AI reference architectures Session

Dive into the the newly released GitHub repository for recommended ways to train and deploy models on Azure with Danielle Dean, Wee Hyong Tok, and Mathew Salvaris. The repository ranges from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes.

Training and deploying Python models on Azure Tutorial

Danielle Dean, Mathew Salvaris, and Wee Hyong Tok outline the recommended ways to train and deploy Python models on Azure, ranging from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes.

Danielle Deibler is the cofounder and CEO of MarvelousAI, an early stage startup focused on building natural language technology to discover and expose propaganda, disinformation, and bias to enable advocates and policymakers to devise countermeasures and immunities. She has over 25 years in the internet infrastructure, security, networking, interactive technology, machine learning, and AI technologies. Her primary area of focus in the last 20 years has been building scalable real-time interactive platforms. Previously, she was CEO and cofounder of leading-edge regulatory technology startup Compliance.ai; founder of Apps54 and Ignited Artists; an entrepreneur in residence at Trinity Ventures; and has held senior leadership positions in software development, engineering, business development, and product management for KIXEYE, Adobe, DIGEX, and UltraDNS.

Presentations

To arms: The battle against misinformation Session

Danielle Deibler examines an approach to detecting bias, fine-grained emotional sentiment, and misinformation through the detection of political narratives in online media. As building blocks, the methodology utilizes human-in-the-loop alongside other natural language processing and computational linguistics techniques, with examples focused on the 2020 US presidential election.

Jim Dowling is the CEO of Logical Clocks, an associate professor at KTH Royal Institute of Technology in Stockholm, and lead architect of Hopsworks, an open source data and AI platform. He’s a regular speaker at big data industry conferences. He holds a PhD in distributed systems from Trinity College Dublin.

Presentations

ROCm and Hopsworks for end-to-end deep learning pipelines Session

The Radeon open ecosystem (ROCm) is an open source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning. Jim Dowling outlines how the open source Hopsworks framework enables the construction of horizontally scalable end-to-end machine learning pipelines on ROCm-enabled GPUs.

Casey Dugan is the manager of the AI Experience Lab at IBM Research in Cambridge. Her group is an interdiscipinary team made up of designers, engineers, and HCI researchers. They design, build, and study systems at the intersection of HCI & AI, especially human-AI interaction. She has worked in the research areas of social media, analytics and visualization dashboards, human computation and crowdsourcing, and recommender systems since joining IBM. Her projects have ranged from designing meeting rooms of the future to studying #selfiestations, or kiosks for taking selfies at IBM labs around the world. She earned a couple of degrees from MIT and spent two summers interning with the IBM lab. Outside of work, she’s taught chocolate sculpture to teenagers, drinks a lot of Starbucks, and has a big fluffy dog named Lincoln.

Presentations

The intersection of AI and HCI: Gamifying the latest artificial intelligence research Session

Casey Dugan and Zahra Ashktorab challenge you to guess the backdoor of a hacked classifier. Join them to learn more about novel AI technologies through the design and development of engaging games. Take a look at their latest research around improving the interactions between humans and AI systems from empathy building to feedback design.

Ted Dunning is the chief technology officer at MapR. He’s also a board member for the Apache Software Foundation; a PMC member and committer of the Apache Mahout, Apache Zookeeper, and Apache Drill projects; and a mentor for various incubator projects. Ted has years of experience with machine learning and other big data solutions across a range of sectors. He’s contributed to clustering, classification, and matrix decomposition algorithms in Mahout and to the new Mahout Math library and designed the t-digest algorithm used in several open source projects and by a variety of companies. Previously, Ted was chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems and built fraud-detection systems for ID Analytics (LifeLock). Ted has coauthored a number of books on big data topics, including several published by O’Reilly related to machine learning, and has 24 issued patents to date plus a dozen pending. He holds a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting.

Presentations

Online evaluation of machine learning models Session

Evaluating machine learning models is surprisingly hard, but it gets even harder because these systems interact in very subtle ways. Ted Dunning breaks the problem apart into operational and function and shows you how each can be done without unnecessary pain and suffering. You'll also get to see some exciting visualization techniques to help make the differences strikingly apparent.

Raffaello D’Andrea is founder, CEO, and chairman of the board of Verity, the world’s leading autonomous indoor drone system provider. He is also co-founder of ROBO Global, creator of the world’s first robotics exchange traded fund, and a professor of dynamic systems and control at ETH Zurich (on leave).

He co-founded Kiva Systems – now Amazon Robotics – in 2003. He was the system architect and faculty advisor of the 4-time world champion Cornell RoboCup team, from 1999 to 2003. In addition, he is a new media artist with exhibitions at various international venues, including the Venice Biennale, the FRAC Centre and the National Gallery of Canada. Other creations and projects include the Flying Machine Arena, the Distributed Flight Array, the Balancing Cube, Cubli, Flight Assembled Architecture, the Blind Juggler, the Robotic Chair, and RoboEarth.

His 2 TED talks, with almost 20 million views, have inspired a generation to pursue engineering, robotics, and computer science.

Presentations

Keynote by Raffaello D’Andrea Keynote

Details to come.

Sergey Ermolin is a principal solutions architect (ML/DL/AI) for Amazon Web Services. Previously, he was a software solutions architect for deep learning, Spark analytics, and big data technologies at Intel. A Silicon Valley veteran with a passion for machine learning and artificial intelligence, Sergey has been interested in neural networks since 1996 when he used them to predict aging behavior of quartz crystals and cesium atomic clocks made by Hewlett-Packard. Sergey holds an MSEE and a certificate in mining massive datasets from Stanford and BS degrees in both physics and mechanical engineering from California State University, Sacramento.

Presentations

Using reinforcement learning to build recommendation systems with AWS SageMaker RL Tutorial

Sergey Ermolin and Vineet Khare provide a step-by-step overview on how to implement, train, and deploy a reinforcement learning (RL)-based recommender system with real-time multivariate optimization. They show you how leverage RL to implement a recommender system that optimizes an advertisement message that promotes adoption of merchant's services.

Ilya Feige is the head of data science and the head of research at Faculty. He ensures that cutting-edge machine learning is used across all Faculty’s commercial data science projects. Previously, Ilya worked at McKinsey & Company, helping to deploy artificial intelligence for some of the world’s largest brands. He’s also an honorary senior research fellow in artificial intelligence at UCL. Ilya was awarded the Goldhaber prize for the best PhD in theoretical physics from Harvard University, and the Governor General’s award for the single highest ranked undergraduate student at McGill University.

Presentations

Concepts and tools for fairness, explainability, and robustness in machine learning Session

Ilya Feige explores AI safety concerns—explainability, fairness, and robustness—relevant for machine learning (ML) models in use today. You'll see Ilya demonstrate tools developed at Faculty to ensure black box algorithms make interpretable decisions, do not discriminate unfairly, and are robust to perturbed data with a focus on concepts and examples.

James Fletcher is a principle researcher with Grakn, investigating approaches to advance cognition and leveraging machine learning, automated reasoning, and a knowledge base.

Presentations

Why biotech needs knowledge graph convolutional networks for discovery Session

Statistical approaches alone are not sufficient to tackle the complexity of AI challenges today. Being smarter with the data we already have is critical to achieving machine understanding of any complex domain. James Fletcher explains how knowledge graph convolutional networks (KGCNs) demonstrate the usefulness of combining a connectionist deep learning approach with a symbolic approach.

Steve Flinter is an artificial intelligence practice lead at Mastercard Labs. He’s an IT professional with more than 20 years’ experience in industry, government, and academia. Previously, Steve was with the global Mastercard start path team, Mastercard’s startup engagement activity, where he supported fintech startup companies by connecting them to Mastercard and its global network of customers; managed an investment portfolio of approximately €120m in the software and computer science areas at Science Foundation Ireland (SFI), the Irish Government agency investing in academic research; and worked in various senior software development roles in a variety of industry verticals. Steve holds a BSc in computer applications from Dublin City University and a PhD in computer science specializing in artificial intelligence from Trinity College Dublin.

Presentations

Developing a modern, open source machine learning pipeline with Kubeflow Session

Steve Flinter and Ahmed Menshaw explore work that Mastercard Labs undertook to build an end-to-end machine learning pipeline, suitable for both R&D and production, using Kubernetes and Kubeflow. They demonstrate how the pipeline can be defined, configured, connected to a data streaming service, and used to train and deploy a model, which can be exposed for inference via an API.

Ariadna Font Llitjós is the director of engineering of Cortex Machine Learning (ML) Platform, focusing on enabling teams and increase their productivity, with the end goal to help facilitate a healthy public conversation and reduce miss information.
Additionally, she is also the Engineering Site Executive for the New York City office, responsible for growing the engineering and design teams, as well as making Twitter NYC the best place to work by instilling a culture of inclusiveness and growth.

Ari is passionate about bringing innovation to market and makes intelligent systems easy to use, always putting people at the center. Previously, Ari was director of product development and design principal at IBM Watson (Data and AI), where she led a large global team of engineers focused on the Watson discovery portfolio. With her teams, she worked to infuse Watson solutions and applications with knowledge and natural language understanding, leveraging machine learning (ML), neural networks (NN), and natural language processing (NLP) techniques, turning unstructured data into knowledge in a way that improves both the offerings and the user experience.

Leveraging agile, lean, design thinking and lean UX best practices, Ari has been leading teams of developers, designers, and researchers for the last eight years. She earned a PhD in language and information technologies in the School of Computer Science at Carnegie Mellon University. Her PhD research focused on improving machine translation quality and accuracy by developing a largely automated approach that used online post-editing feedback to refine translation rules.

Presentations

Executive Briefing: Designing and building responsible AI for you Session

In the rapidly changing world of AI, adopting the right design principles is key. Ariadna Font Llitjós examines how at IBM Watson, ethical AI and user-centered design principles are applied from the beginning and leveraged throughout the product development cycle. From data scientists and business users to client end users, IBM Watson always seeks to augment their capabilities.

Michael Friedrich is a Senior Computer Scientist for the Adobe Cloud Platform at Adobe. His team brings cloud operations to a new level, using machine learning to automate complex development and delivery processes, including by implementing automated canary analysis for deployments or researching new automated scaling solutions.

Prior to Adobe he was the chief software engineer for Hamburg Süd (now part of Maersk) and was working on their container routing software.

Presentations

About Space Invaders and automated scaling Session

Michael Friedrich and Stefanie Grunwald explore how an algorithm capable of playing Space Invaders can also improve your cloud service's automated scaling mechanism.

Siddha Ganju is a self-driving solutions architect at NVIDIA and was featured by Forbes on their 30 under 30 list. Previously, she developed deep learning models for resource constraint edge devices at Deep Vision. A graduate from Carnegie Mellon University, her prior work ranges from visual question answering to generative adversarial networks to gathering insights from CERN’s petabyte-scale data, and she’s been published at top-tier conferences including CVPR and NeurIPS. Serving as an AI domain expert, she’s also been guiding teams at NASA as well as featured as a jury member in several international tech competitions.

Presentations

Deep learning on mobile Session

Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would benefit from the new opportunities enabled by deep learning techniques. Siddha Ganju and Meher Kasam walk you through optimizing deep neural nets to run efficiently on mobile devices.

Biraja Ghoshal is a computer consultant with Tata Consultancy Service. He has 21 years of software development, architecture, and systems engineering expertise in information management and mining massive datasets technologies. Biraja assists clients to apply analytic capabilities using big data platforms to improve performance and optimize decision making with high-quality, actionable insights. Biraja is also interested in machine learning, cognitive computing, and artificial intelligence topics.

Presentations

Deep learning with TensorFlow probability in cancer prediction with reporting confidence Session

Deep learning, which involves powerful black box predictors, has achieved state-of-the-art performance in medical imaging analysis, such as segmentation and classification for diagnosis, but knowing how much confidence there is in a prediction is essential for gaining clinicians' trust. Biraja Ghoshal explores probabilistic modeling with TensorFlow probability in cancer prediction.

Martin Goodson is the chief scientist and CEO of Evolution AI, where he specializes in large-scale natural language processing. Martin has designed data science products that are in use at companies like Dun & Bradstreet, Time Inc., John Lewis, and Condé Nast. Previously, Martin was a statistician at the University of Oxford, where he conducted research on statistical matching problems for DNA sequences. He runs the largest community of machine learning practitioners in Europe, Machine Learning London, and convenes the CBI/Royal Statistical Society roundtable, AI in Financial Services. Martin’s work has been covered by publications such as the Economist, Quartz, Business Insider, TechCrunch, and others.

Presentations

The dangers of data leakage in production machine learning systems Session

Data leakage occurs when the model gains access to data that it shouldn't have. AI systems can fail catastrophically in production if leakage is not dealt with properly. Martin Goodson details the four main manifestations of data leakage and how to recognize the warning signs. By mastering several key scientific principles, you can mitigate the risk of failure.

Vignesh Gopakumar is a machine learning engineer specializing in fusion research with the United Kingdom Atomic Energy Authority. He spends his time building machine learning algorithms to model physics systems that help gain more understanding of the underlying phenomenons. He designs algorithms that help discover anomalies as well as predict malfunction of engineering systems. He’s working on building a model that can be augmented in real time when exposed to different physics principles.

Presentations

Data-driven approach to model the physics of superheated gas hitting a wall Session

Vignesh Gopakumar explores image mapping of the temporal evolution of physics parameters as plasma interacts with the reactor wall using a data-inferred approach. The model captures how parameters such as temperature and density evolve across space and time. By analyzing the patterns found in simulation data, the model learns the existing physics relations implicitly defined within the data.

Stefanie Grunwald is a senior data and platform engineer with Adobe Experience Cloud. As a trained software architect, she’s been working in the field of data science and data engineering since 2011, applying the best practices from software engineering to building intelligent data platforms. With her heart devoted to DataOps and the OSS community, she and her team help others at Adobe become data-driven through automation and real-time insights.

Presentations

About Space Invaders and automated scaling Session

Michael Friedrich and Stefanie Grunwald explore how an algorithm capable of playing Space Invaders can also improve your cloud service's automated scaling mechanism.

Adam Grzywaczewski is a deep learning solution architect at NVIDIA, where his primary responsibility is to support a wide range of customers in delivery of their deep learning solutions. Adam is an applied research scientist specializing in machine learning with a background in deep learning and system architecture. Previously, he was responsible for building up the UK government’s machine-learning capabilities while at Capgemini and worked in the Jaguar Land Rover Research Centre, where he was responsible for a variety of internal and external projects and contributed to the self-learning car portfolio.

Presentations

Developing perception algorithms for autonomous vehicles Session

Developing perception algorithms for autonomous vehicles is incredibly difficult, as they need to operate in thousands of driving conditions and locations. Adam Grzywaczewski explores the challenges involved in data collection, processing, and management, as well as model development and validation. He also provides an overview of the necessary hardware and software infrastructure.

Rebecca Gu is a Senior Consultant at Baringa Partners. On the human side, she’s brought forward economic evidence in the first UK High Court case to go to trial for damages against a cartelist. On the machine side, her interest lies in how machine learning and AI are changing what we might consider cartels. Machine learning is also broadly changing the way we shop, the way firms price, and how governments and industry should work together to face these challenges. Rebecca has written a discussion paper on the topic of ethics and AI pricing.

Presentations

Executive Briefing: A look at the future of online pricing and algorithm-led collusion Session

In a future of widespread algorithmic pricing, cooperation between algorithms is easier than ever, resulting in coordinated price rises. Rebecca Gu and Cris Lowery explore how a Q-learner algorithm can inadvertently reach a collusive outcome in a virtual marketplace, which industries are likely to be subject to greater restrictions or scrutiny, and what future digital regulation might look like.

Charlotte Han is a deep learning marketing manager with NVIDIA, where she sees firsthand how AI is transforming industries. She processes data and computes brand and digital strategies for a living. Thanks to growing up in Asia, becoming American in Silicon Valley, and now living in Europe, she’s learned not take things for granted and to make connections where they may not seem apparent. She’s highly interested in all things tech, especially how technologies can advance human lives, and enjoys networking with the misfits, the rebels, and the troublemakers who aren’t afraid to shake things up and push the boundaries of what is possible. Connect with her on Twitter as @sunsiren or on LinkedIn.

Presentations

Executive Briefing: Will you learn Chinese to advance in AI? Session

According to research by AI2, China is poised to overtake the US in the most-cited 1% of AI research papers by 2025. The view that China is a copycat but not an innovator may no longer be true. Charlotte Han explores the what implications of China's government funding, culture, and access to massive data pools mean to AI development and how the world could benefit from such advancement.

Kristian is currently a visiting scholar in the Robotics and AI Lab (RAIL) at UC Berkeley working with Sergey Levine and Tuomas Haarnoja, and will begin his PhD studies at the University of Oxford with Simon Whiteson in fall 2019. He’s research focus is on the development of model-free deep reinforcement learning algorithms for robotic control. He’s also working on Ray RLlib, a scalable reinforcement learning library; Ray Tune, a distributed framework for model training; and is also the author and maintainer of Softlearning, the official Soft-Actor Critic project. Prior to coming to Berkeley, he spent several years working as a software engineer on statistical analysis and machine learning products in the industry at Statwing and Qualtrics.

Presentations

Scalable AI and Reinforcement Learning with Ray Tutorial

This tutorial will provide a deep dive into Ray, a new distributed execution framework for distributed AI applications developed by machine learning and systems researchers at UC Berkeley’s RISELab, walking you through Ray’s API and system architecture and sharing application examples, including several state-of-the-art distributed training, hyperparameter search, and RL algorithms.

Kim Hazelwood is a senior engineering manager leading the AI Infrastructure Foundation and AI Infrastructure Research efforts at Facebook, where the focus is designing and optimizing efficiency hardware and software systems for Facebook’s many applied machine learning-based products and services. Previously, Kim was a tenured associate professor at the University of Virginia, a software engineer at Google, and director of systems research at Yahoo Labs. She’s been recognized with an NSF CAREER Award, the Anita Borg Early Career Award, the MIT Technology Review Top 35 Innovators under 35 Award, and the ACM SIGPLAN 10-Year Test of Time Award. She serves on the Board of Directors of CRA and has authored over 50 conference papers and one book. She holds a PhD in computer science from Harvard University.

Presentations

Large-scale machine learning at Facebook: Implications of platform design on developer productivity Keynote

AI plays a key role in achieving the Facebook mission of connecting people and building communities. Nearly every visible product is powered by machine learning algorithms at its core from delivering relevant content to making the platform safe. Kim Hazelwood takes an end-to-end look at how applied ML has continued to change the landscape of the platforms and infrastructure at Facebook.

Adithya Hrushikesh is an operational intelligence lead, data science, with Vodafone, where he leads a team of data scientists and data engineers to build data products.

Presentations

Automating customer complaints classification in German Session

Every day, millions of Vodafone Germany customers reach out through various social media channels about issues related to mobile, internet, signal issues, etc. Adithya Hrushikesh details how to build and deploy an ensemble model to classify 26 (originally 56) complaint classes using machine learning over deep learning. He also touches on business case, data product development, and GDPR.

Congrui Huang is a senior data scientist with the AI platform team of Microsoft Cloud and AI Division.

Presentations

Introducing a new anomaly-detection algorithm (SR-CNN) inspired by computer vision Session

Anomaly detection may sound old fashioned, yet it's super important in many industry applications. Tony Xing, Bixiong Xu, Congrui Huang, and Qun Ying detail a novel anomaly-detection algorithm based on spectral residual (SR) and convolutional neural network (CNN) and how this method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention.

Ihab Ilyas is a professor in the Cheriton School of Computer Science at the University of Waterloo, where his research focuses on the areas of big data and database systems, with special interest in data quality and integration, managing uncertain data, rank-aware query processing, and information extraction. Ihab is also a cofounder of Tamr, a startup focusing on large-scale data integration and cleaning. He’s a recipient of the Ontario Early Researcher Award (2009), a Cheriton faculty fellowship (2013), an NSERC Discovery Accelerator Award (2014), and a Google Faculty Award (2014), and he’s an ACM Distinguished Scientist. Ihab is an elected member of the VLDB Endowment board of trustees and an associate editor of ACM Transactions of Database Systems (TODS). He holds a PhD in computer science from Purdue University, West Lafayette.

Presentations

The quest for high-quality data Keynote

This keynote highlights this data quality problem and describes the Holoclean framework, a state-of-the-art prediction engine for structured data, with direct applications in detecting and repairing data errors, as well as imputing missing labels and values.

Alex Ingerman is a product manager at Google AI, focusing on federated learning and other privacy-preserving technologies. His mission is to enable all ML practitioners to protect their users’ privacy by default. Previously, Alex worked on ML-as-a-service platforms for developers, web-scale search, content recommendation systems, and immersive data exploration and visualization. Alex lives in Seattle, where as a frequent bike and occasional kayak commuter, he has fully embraced the rain. Alex holds a BS in computer science and an MS in medical engineering.

Presentations

Federated learning introduction and examples with TensorFlow Federated Session

Federated learning is the approach of training ML models across many devices without collecting the data in a central location. Alex Ingerman explores learning concepts and the use cases for decentralized machine learning, drawing on Google's real-world deployments. You'll learn how to build your first federated models with the open source TensorFlow Federated.

Jewel James is a data scientist at Gojek.

Presentations

Using ML for personalizing food search at Gojek Session

GoFood, the food delivery product of Gojek is one of the largest of its kind in the world. Jewel James and Mudit Maheshwari walk you through the story of how they prototyped the search framework that personalizes the restaurant search results by using ML to learn what constitutes a relevant restaurant given a user's purchasing history.

Katharine Jarmul is a cofounder of KIProtect and is a passionate and internationally recognized data scientist, programmer, and lecturer. Her work and research focuses on securing data for data science workflows. Previously, she held numerous roles at large companies and startups in the US and Germany, implementing data processing and machine learning systems with a focus on reliability, testability, and security. She’s an author for O‘Reilly and frequent keynote speaker at international software conferences.

Presentations

Executive Briefing: Advances in privacy for machine learning systems Session

Katharine Jarmul sates your curiosity about how far we've come in implementing privacy within machine learning systems. She dives into recent advances in privacy measurements and how this changed the approach of privacy in machine learning. You'll discover new techniques including differentially private data collection, federated learning, and homomorphic techniques.

Jeff Jonas is the founder and CEO of Senzing and is an acclaimed data scientist and the leading creator of entity resolution systems. Jonas founded Senzing with the goal of making entity resolution technology available for everyone everywhere. For more than three decades, he’s been at the forefront of solving complex big data problems for companies and governments. Jonas is a three-time entrepreneur and sold his last company to IBM in 2005. Previously, Jonas was an IBM Fellow and chief scientist of context computing at IBM, where he led a team focused on creating next-generation AI for entity resolution technology.

Presentations

Real-time AI for entity resolution Keynote

Entity resolution—determining “who is who” and “who is related to who”—is essential to almost every industry, including banking, insurance, healthcare, marketing, telecommunications, social services, and more. Jeff Jonas details how you can use a purpose-built real-time AI, created for general-purpose entity resolution, to gain new insights and make better decisions faster.

Ahmed Kamal is the machine learning platform lead at Careem, where he’s working on developing machine learning services and data infrastructure. Previously, he worked on building the data science platform at Seeloz. He’s passionate about building data products that change people’s lives.

Presentations

Scaling machine learning at Careem Session

Every day Careem’s platform relies on machine learning (ML) in production to enable the movement of millions of its users. Ahmed Kamal outlines the challenges Careem faced while productionizing ML on scale and how to build an in-house ML platform that facilitates development and fast deployment of scalable ML services and accelerates the impact of ML everywhere.

Manas Ranjan Kar is a senior manager at US healthcare company Episource, where he leads the NLP and data science practice, works on semantic technologies and computational linguistics (NLP), builds algorithms and machine learning models, researches data science journals, and architects secure product backends in the cloud. He’s architected multiple commercial NLP solutions in the area of healthcare, food and beverages, finance, and retail. Manas is deeply involved in functionally architecting large-scale business process automation and deep insights from structured and unstructured data using NLP and ML. He’s contributed to NLP libraries including gensim and Conceptnet 5 and blogs regularly about NLP on forums like Data Science Central, LinkedIn, and his blog Unlock Text. Manas speaks regularly about NLP and text analytics at conferences and meetups, such as PyCon India and PyData, has taught hands-on sessions at IIM Lucknow and MDI Gurgaon, and has mentored students from schools including ISB Hyderabad, BITS Pilani, and the Madras School of Economics. When bored, he falls back on Asimov to lead him into an alternate reality.

Presentations

NLP for healthcare: Feature engineering and model diagnostics Session

Natural language processing (NLP) is hard, especially for clinical text. Manas Ranjan Kar explains the multiple challenges of NLP for clinical text and why it's so important that we invest a fair amount of time on domain-specific feature engineering. It’s also crucial to understand to diagnose an NLP model performance and identify possible gaps.

Meher Kasam is an iOS software engineer at Square and is a seasoned software developer with apps used by tens of millions of users every day. He’s shipped features for a range of apps from Square’s point of sale to the Bing app. Previously, he worked at Microsoft, where he was the mobile development lead for the Seeing AI app, which has received widespread recognition and awards from Mobile World Congress, CES, FCC, and the American Council of the Blind, to name a few. A hacker at heart with a flair for fast prototyping, he’s won close to two dozen hackathons and converted them to features shipped in widely used products. He also serves as a judge of international competitions including Global Mobile Awards, Edison Awards.

Presentations

Deep learning on mobile Session

Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would benefit from the new opportunities enabled by deep learning techniques. Siddha Ganju and Meher Kasam walk you through optimizing deep neural nets to run efficiently on mobile devices.

Ujwal Kayande is the associate dean (academic) and professor of marketing at Melbourne Business School, an area editor for the International Journal of Research in Marketing, and serves on the editorial boards of the Journal of Marketing Research and the Journal of Service Research. He’s also the founding director of the Centre for Business Analytics. Ujwal teaches marketing strategy and analytics on the MBA, master of business analytics, and executive education programs. He’s the codirector of the digital marketing and analytics executive education program jointly delivered by the University of Oxford and Melbourne Business School. Previously, Ujwal held faculty positions at Penn State, the Australian National University, UNSW Sydney, University of Pennsylvania, and others. He’s received numerous accolades for teaching excellence, and his research has been awarded by the American Marketing Association (Lehmann Award), European Marketing Academy (IJRM Best Paper), and the Australia-NZ Marketing Academy (Distinguished Researcher). A frequent media commentator in The Australian, Australian Financial Review, Daily Telegraph, and other publications, Ujwal consults globally on marketing strategy and analytics.

Presentations

Executive Briefing: From laggard to leader—Winning the AI race Session

The Analytics Impact Index gives organizations an understanding of the value potential of analytics as well as the capabilities required to capture the most value. Ujwal Kayande, Anastasia Kouvela, and Bharath Thota walk you through the 2019 results and the analytics journey of leading global organizations and empower companies to develop a case for change.

Arun Kejariwal is an independent lead engineer. Previously, he was he 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, and his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection; and he developed and open-sourced techniques for anomaly detection and breakout detection at Twitter. 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

Sequence to sequence (S2S) modeling for time series forecasting Session

Sequence to sequence (S2S) modeling using neural networks has become increasingly mainstream in recent years. In particular, it's been leveraged for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for these use cases, visualization, real-time anomaly detection, and forecasting.

Ganes Kesari is a cofounder and head of analytics at Gramener, where he leads analytics and innovation in data science, advising enterprises on deriving value from data science initiatives and leading applied research in deep learning at Gramener AI Labs. He’s passionate about the confluence of machine learning, information design, and data-driven business leadership and strives to simplify and demystify data science.

Presentations

Predicting the quality of life from satellite imagery Session

In many countries, policy decisions are disconnected from data and very few avenues exist to understand deeper demographic and socioeconomic insights. Ganes Kesari and Soumya Ranjan explain how satellite imagery can be a powerful aid when viewed through the lens of deep learning. When combined with conventional data, it can help answer important questions and show inconsistencies in survey data.

Vineet Khare is a manager of applied science at AWS and has led the research and development efforts for multiple AWS products including SageMaker built-in algorithms, SageMaker RL, AWS DeepRacer, and AWS Ground Truth. He’s presented his research at various international conferences including PPSN, EMO, GECCO, and SEAL. He’s also conducted SageMaker workshops and tutorials at various AWS events such as the annual conference, re:Invent.

Presentations

Using reinforcement learning to build recommendation systems with AWS SageMaker RL Tutorial

Sergey Ermolin and Vineet Khare provide a step-by-step overview on how to implement, train, and deploy a reinforcement learning (RL)-based recommender system with real-time multivariate optimization. They show you how leverage RL to implement a recommender system that optimizes an advertisement message that promotes adoption of merchant's services.

Anastasia Kouvela is a principal at A.T. Kearney with more than 10 years in the advisory space. She leads international large-scale operations transformations across industries and is well known for delivering high-impact transformation programs that address post-merger operations integrations, cost optimization, complexity reduction, and supply chain and logistics optimization. Anastasia is particularly passionate about analytics and AI.

Presentations

Executive Briefing: From laggard to leader—Winning the AI race Session

The Analytics Impact Index gives organizations an understanding of the value potential of analytics as well as the capabilities required to capture the most value. Ujwal Kayande, Anastasia Kouvela, and Bharath Thota walk you through the 2019 results and the analytics journey of leading global organizations and empower companies to develop a case for change.

Akshay Kulkarni is a senior data scientist with SapientRazorfish’s core AI and data science team, where he’s part of strategy and transformation interventions through AI, manages high priority growth initiatives around data science and works on various machine learning, deep learning, natural language processing, and artificial intelligence engagements by applying state-of-the-art techniques, as well as a renowned AI and machine learning evangelist, an author, and a speaker. He was recently recognized as one of the “top 40 under 40 data scientists” in India by Analytics India Magazine. He’s consulted with several Fortune 500 and global enterprises in driving AI and data science-led strategic transformations. Akshay has a rich experience of building and scaling AI and machine learning businesses and creating significant client impact. He’s actively involved in next gen AI research and is also a part of next gen AI community. Previously, he was part of Gartner and Accenture, where he scaled the AI and data science business. He’s a regular speaker at major data science conferences recently gave a talk on “Sequence Embeddings for Prediction Using Deep Learning” at GIDS. He’s the author of a book on NLP with Apress and currently authoring couple more books with Packt on deep learning and next gen NLP. He is also a visiting faculty (industry expert) at few of the top universities in India. In his spare time, he likes to read, write, code, and help aspiring data scientists.

Presentations

Text analytics 101: Deep learning and attention networks all the way to production Tutorial

An estimated 80% of data generated is an unstructured format, such as text, image, audio, or video. Vijay Srinivas Agneeswaran, Pramod Singh, and Akshay Kulkarni explore how to create a language model that generates natural language text by implementing and forming a recurrent neural network and attention networks built on top of TensorFlow 2.0.

Abhishek Kumar is a senior manager of data science in Publicis Sapient’s India office, where he looks after scaling up the data science practice by applying machine learning and deep learning techniques to domains such as retail, ecommerce, marketing, and operations. Abhishek is an experienced data science professional and technical team lead specializing in building and managing data products from conceptualization to the deployment phase and interested in solving challenging machine learning problems. Previously, he worked in the R&D center for the largest power-generation company in India on various machine learning projects involving predictive modeling, forecasting, optimization, and anomaly detection and led the center’s data science team in the development and deployment of data science-related projects in several thermal and solar power plant sites. Abhishek is a technical writer and blogger as well as a Pluralsight author and has created several data science courses. He’s also a regular speaker at various national and international conferences and universities. Abhishek holds a master’s degree in information and data science from the University of California, Berkeley. Abhishek has spoken at past O’Reilly conferences, Strata 2019, Strata 2018, and AI 2019.

Presentations

Industrialized capsule networks for text analytics Session

Vijay Srinivas Agneeswaran and Abhishek Kumar outline how to industrialize capsule networks by detailing capsule networks and how capsule networks help handle spatial relationships between objects in an image and how to apply them to text analytics and tasks such as NLU or summarization. Join in to see a scalable, productionizable implementation of capsule networks over KubeFlow.

Marta Kwiatkowska is a professor of computing systems and fellow of Trinity College, University of Oxford. She’s known for fundamental contributions to the theory and practice of model checking for probabilistic systems. She led the development of the “PRISM model checker”http://www.prismmodelchecker.org, the leading software tool in the area. Probabilistic model checking has been adopted in diverse fields, including distributed computing, wireless networks, security, robotics, healthcare, systems biology, DNA computing, and nanotechnology, with genuine flaws found and corrected in real-world protocols. Marta was awarded two ERC Advanced Grants, VERIWARE and FUN2MODEL, and is a coinvestigator of the EPSRC Programme Grant on Mobile Autonomy. She was honored with the Royal Society Milner Award in 2018 and the Lovelace Medal in 2019 and is a Fellow of the Royal Society, ACM and BCS, and Member of Academia Europea.

Presentations

When to trust AI Keynote

Machine learning solutions are revolutionizing AI, but Marta Kwiatkowska explores their instability against adversarial examples—small perturbations to inputs that can catastrophically affect the output—which raises concerns about the readiness of this technology for widespread deployment.

Holger Kyas is Open Group Board Member for OpenCA Architecture Certifications, Enterprise Architect at Helvetia Insurances and Adjunct Professor at the University of Applied Sciences Bern in Switzerland. He has presented at international conferences like “IBM World of Watson” or “Insurance AI and Analytics Europe.”

Presentations

Implementing an AI multicloud broker Session

Holger Kyas details the AI multicloud broker, which is triggered by Amazon Alexa and mediates between AWS Comprehend (Amazon), Azure Text Analytics (Microsoft), GCP Natural Language (Google), and Watson Tone Analyzer (IBM) to compare and analyze sentiment. The extended AI part generates new sentences (e.g., marketing slogans) with a recurrent neural network (RNN).

Francesca Lazzeri is a senior machine learning scientist at Microsoft on the cloud advocacy team and an expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries—energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a research fellow in business economics at Harvard Business School, where she performed statistical and econometric analysis within the technology and operations management unit. At Harvard, she worked on multiple patent, publication and social network data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. Francesca periodically teaches applied analytics and machine learning classes at universities and research institutions around the world. She’s a data science mentor for PhD and postdoc students at the Massachusetts Institute of Technology and speaker at academic and industry conferences—where she shares her knowledge and passion for AI, machine learning, and coding.

Presentations

Time series forecasting: Build and deploy your ML models to forecast the future 2-Day Training

Francesca Lazzeri, Wee Hyong Tok, Krishna Anumalasetty, and Aashish Bhateja walk you through the core steps of training your machine learning time series forecasting models using Python and Azure Machine Learning both locally and on remote compute resources.

Chang Liu is an applied research scientist at Georgian Partners and a member of the Georgian impact team, where she draws on her in-depth knowledge of mathematical and combinatorial optimization to help Georgian’s portfolio companies. Previously, Chang was a risk analyst at Manulife Bank, where she built models to assess the bank’s risk exposure based on extensive market research, including evaluating and predicting the impact of the oil price drop to the mortgage lending risks in Alberta in 2014. Chang holds a master’s of applied science in operations research from the University of Toronto, where she specialized in combinatorial optimization, and a bachelor’s degree in mathematics from the University of Waterloo.

Presentations

Building differentially private machine learning models using TensorFlow Session

The world is increasingly data driven, and people have developed an awareness and concern for their data. Chang Liu and Ji Chao Zhang examine differential privacy and its use cases, the component of the TensorFlow privacy library that allows users to train differentially private logistic regression and support vector machines, and real-world scenarios and demonstrations for how to apply the tools.

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

Thursday Opening Welcome Keynote

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

Wednesday Opening Welcome Keynote

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

Cristobal Lowery is an artificial intelligence specialist leading Baringa’s modeling and machine learning center of excellence of 30 individuals. I have supported airlines in their pricing strategy, run Artificial Intelligence due diligence and built many machine learning solutions for clients.

Presentations

Executive Briefing: A look at the future of online pricing and algorithm-led collusion Session

In a future of widespread algorithmic pricing, cooperation between algorithms is easier than ever, resulting in coordinated price rises. Rebecca Gu and Cris Lowery explore how a Q-learner algorithm can inadvertently reach a collusive outcome in a virtual marketplace, which industries are likely to be subject to greater restrictions or scrutiny, and what future digital regulation might look like.

Angie Ma is cofounder and COO of Faculty, a London-based AI tech startup that provides strategy, software, and skills. Faculty has delivered more than 300 commercial data science projects across 23 sectors and 8 countries. Angie is passionate about real-world applications of machine learning that generate business value for companies and organizations and has experience delivering complex projects from prototyping to implementation. A physicist by training, Angie was previously a researcher in nanotechnology working on developing optical detection for medical diagnostics.

Presentations

AI for executives 2-Day Training

Angie Ma offers a condensed introduction to key AI and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization. You’ll learn a language and framework to talk to both technical experts and executives in order to better oversee the practical application of data science in your organization.

Mark Madsen is a fellow at Teradata, where he’s responsible for understanding, forecasting, and defining the analytics ecosystem and architecture. Previously, he was CEO of Third Nature, where he advised companies on data strategy and technology planning and vendors on product management. Mark has designed analysis, machine learning, data collection, and data management infrastructure for companies worldwide.

Presentations

Executive Briefing: The black box—Interpretability, reproducibility, and data management Session

The growing complexity of data science leads to black box solutions that few people in an organization understand. Mark Madsen explains why reproducibility—the ability to get the same results given the same information—is a key element to build trust and grow data science use. And one of the foundational elements of reproducibility (and successful ML projects) is data management.

Mudit Maheshwari is a product engineer at Gojek working with the GoFood search team focused on providing relevant results to the user. Previously, he’s worked on developing and designing scalable, reliable, and fault-tolerant systems for one of the biggest food delivery business.

Presentations

Using ML for personalizing food search at Gojek Session

GoFood, the food delivery product of Gojek is one of the largest of its kind in the world. Jewel James and Mudit Maheshwari walk you through the story of how they prototyped the search framework that personalizes the restaurant search results by using ML to learn what constitutes a relevant restaurant given a user's purchasing history.

Michael W. Mahoney is a professor in the Department of Statistics and the International Computer Science Institute (ICSI) at the University of California, Berkeley. He works on the algorithmic and statistical aspects of modern large-scale data analysis. He’s also the director of the NSF/TRIPODS-funded Foundations of Data Analysis (FODA) Institute at UC Berkeley. Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, computational methods for neural network analysis, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. Previously, he worked and taught at Yale University in the mathematics department, at Yahoo Research, and at Stanford University in the mathematics department. Among other things, he’s on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), he was on the National Research Council’s Committee on the Analysis of Massive Data, he co-organized the Simons Institute’s fall 2013 and 2018 programs on the foundations of data science, and he runs the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets. He earned his PhD from Yale University with a dissertation in computational statistical mechanics. More information is available at https://www.stat.berkeley.edu/~mmahoney/.

Presentations

Principled tools for analyzing weight matrices of production-scale deep neural networks Session

An important practical challenge is developing theoretically principled tools to guide the use of production-scale neural networks. Michael Mahoney explores recent work from scientific computing and statistical mechanics to develop such tools, covering basic ideas and their use for analyzing production-scale neural networks in computer vision, natural language processing, and related tasks.

Ted Malaska is a director of enterprise architecture at Capital One. Previously, he was the director of engineering in the Global Insight Department at Blizzard; principal solutions architect at Cloudera, helping clients find success with the Hadoop ecosystem; and a lead architect at the Financial Industry Regulatory Authority (FINRA). He has contributed code to Apache Flume, Apache Avro, Apache Yarn, Apache HDFS, Apache Spark, Apache Sqoop, and many more. Ted is a coauthor of Hadoop Application Architectures, a frequent speaker at many conferences, and a frequent blogger on data architectures.

Presentations

Executive Briefing: Optimizing for skill sets—Data engineers, data scientists, and analysts Session

While at a big tech conference on AI, it's important to reflect on the human components. Ted Malaska walks you through scenarios and strategies to help different groups work together and how to evaluate success and sniff out trouble areas. You'll look at every part of the pipeline to see who's involved and how to optimize the interaction points throughout the pipeline—and how to have fun.

Tobias Martens is a researcher with Universal Namespace (UNS) where he’s involved with building a cocreation think tank with a focus on deep tech innovations. UNS works with international Industry 4.0 companies on the adaptation of their corporate organizations through the enablement of employees to “think and work like startups” in, for example, corporate entrepreneurship, Agile methods, and UX-based R&D processes. Previously, Tobias was a cofounder of two VC-financed startups, and he was young advisor of the EU commissioner Neelie Kroes.

Presentations

Make Alexa and Siri speak with each other: Toward a universal grammar in AI Session

More than 50% of all interactions between humans and machines are expected to be speech-based by 2022. The challenge: Every AI interprets human language slightly different. Tobias Martens details current issues in NLP interoperability and uses Chomsky's theory of universal hard-wired grammar to outline a framework to make the human voice in AI universal, accountable, and computable.

As the Corporate Vice President of Machine Learning software engineering, Ajit is the engineering leader responsible for design, development of ROCm (Radeon Open Compute) Machine Intelligence software spanning Deep Learning Frameworks, Compilers, Language Runtimes, Libraries and Linux Compute Kernel. Ajit is also responsible for the Machine Learning Software Roadmap and Strategy. Ajit is passionate about distributed machine learning and high performance computing. Ajit holds Masters in Computer Science and MBA from Kellogg.

Presentations

ROCm and Hopsworks for end-to-end deep learning pipelines Session

The Radeon open ecosystem (ROCm) is an open source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning. Jim Dowling outlines how the open source Hopsworks framework enables the construction of horizontally scalable end-to-end machine learning pipelines on ROCm-enabled GPUs.

Ahmed Menshawy is a machine learning engineer in the AI Practice, which forms part of the R&D Group in Mastercard Labs, where he works on a wide range of problems related to the application of AI and machine learning to Mastercard’s products and services. Ahmed is interested in studying the overlap between knowledge, logic, language, and learning. In particular, his focus is in how machine learning can be used for distilling large amounts of unstructured, semistructured, and structured data with hidden patterns into new knowledge about the world by using methods ranging from deep learning to statistical relational learning. Ahmed has authored two books, Deep Learning with TensorFlow and Deep Learning by Example, which focus on advanced deep learning topics. Ahmed has a BSc in computer science and an MSc in machine learning from Helwan University, Cairo, Egypt.

Presentations

Developing a modern, open source machine learning pipeline with Kubeflow Session

Steve Flinter and Ahmed Menshaw explore work that Mastercard Labs undertook to build an end-to-end machine learning pipeline, suitable for both R&D and production, using Kubernetes and Kubeflow. They demonstrate how the pipeline can be defined, configured, connected to a data streaming service, and used to train and deploy a model, which can be exposed for inference via an API.

Umberto Michelucci is a cofounder and the chief AI scientist of TOELT LLC, a company aiming to develop new and modern teaching, coaching, and research methods for AI to make AI technologies and research accessible to every company and everyone. He’s an expert in numerical simulation, statistics, data science, and machine learning. In addition to several years of research experience at the George Washington University (USA) and the University of Augsburg (DE), he has 15 years of practical experience in the fields of data warehouse, data science, and machine learning. His last book, Applied Deep Learning—A Case-Based Approach to Understanding Deep Neural Networks, was published by Springer in 2018. He’s working on his new book, Convolutional and Recurrent Neural Networks Theory and Applications. He’s very active in research in the field of artificial intelligence. He publishes his research results regularly in leading journals and gives regular talks at international conferences. Umberto studied physics and mathematics. Sharing is caring—for that, he is a lecturer at the ZHAW University of Applied Sciences for deep learning and neural networks theory and applications and at the HWZ University of Applied Science for big data analysis and statistics. He’s also responsible at Helsana Versicherung AG for research and collaborations with universities in the area of AI.

Presentations

Convolutional neural networks for image recognition in Keras and TensorFlow 2-Day Training

Convolutional neural networks (CNNs) are at the basis of many algorithms that deal with images from image recognition and classification to object detection. Umberto Michelucci uses practical examples to walk you through how to develop convolutional neural networks, how to use pretrained networks, and even how to teach a network to paint. TensorFlow or Keras will be used for all examples.

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. When not Googling, he’s also a published novelist, comic book writer, and screenwriter.

Presentations

Zero to hero with TensorFlow 2.0 Session

Laurence Moroney explores how to go from wondering what machine learning (ML) is to building a convolutional neural network to recognize and categorize images. With this, you'll gain the foundation to understand how to use ML and AI in apps all the way from the enterprise cloud down to tiny microcontrollers using the same code.

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 and director of community evangelism at Databricks and Apache Spark. Paco is the cochair of Rev conference and an advisor for Amplify Partners, Deep Learning Analytics, Recognai, and Primer. He was named one of the "top 30 people in big data and analytics" in 2015 by Innovation Enterprise.

Presentations

Executive Briefing: Unpacking AutoML Session

Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoML—translating from the hype into business concerns and practices in a vendor-neutral way.

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 his tiny little bio, most of which was taken up trying to stick a witty sci-fi reference in…before he simply gave up. He’s writing Practical Artificial Intelligence with Swift for O’Reilly and building a game for a power transmission company about a naughty quoll (a quoll is an Australian animal).

Presentations

Building, teaching, and training simulations for machine learning with a game engine Session

You're building a high-volume, expensive, robot-driven warehouse. Your robots need to get to the right place quickly, find the right item, and sort it to the right place without colliding with each other, the shelves, or people. But you don't have any robots, and you need to start writing the logic and training them. Paris Buttfield-Addison and Tim Nugent outline how to use a simulation to do it.

Practical on-device AI and ML using Swift Session

On-device ML and AI is the future for privacy-conscious, cloud-averse users of modern smartphones. Paris Buttfield-Addison and Tim Nugent explore what's possible using CoreML, Swift, and associated frameworks, and the powerful ML-tuned silicon in modern Apple iOS hardware. They demonstrate and create ML and AI features with Swift to show how much you can do without touching the cloud.

Edward is a second year PhD student at UC Berkeley and contributor to the Ray project. In the past, he has worked on isolation mechanisms for serverless computing and infrastructure for microservice deployments.

Presentations

Scalable AI and Reinforcement Learning with Ray Tutorial

This tutorial will provide a deep dive into Ray, a new distributed execution framework for distributed AI applications developed by machine learning and systems researchers at UC Berkeley’s RISELab, walking you through Ray’s API and system architecture and sharing application examples, including several state-of-the-art distributed training, hyperparameter search, and RL algorithms.

Richard Ott is a data scientist in residence at the Data Incubator, where he combines 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 PyTorch 2-Day Training

PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Join Ana Hocevar to get the knowledge you need to build deep learning models using real-world datasets and PyTorch.

Pedram is a Technical Program Manager on the Tensorflow Extended team at Google Brain, on a mission to create the best inference experience on Tensorflow. Previously, he managed some of Google Cloud’s internal efforts in the machine intelligence space while getting to work on distributed systems and Kubernetes. Aside from building infrastructure, he enjoys writing music, playing soccer and scrolling through memes.

Presentations

TFX: Production ML pipelines with TensorFlow Tutorial

Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe explores Google's open source community TensorFlow Extended (TFX), an open source version of the tools and libraries that Google uses internally, made using its years of experience in developing production ML pipelines.

Justina Petraityte is a developer advocate at Berlin-based startup Rasa, where she helps improve the developer experience in using open source software for conversational AI. Justina has a background in econometrics and data analytics, and her interests include chatbots, natural language processing, and open source. Her curiosity for data science and human-behavior analytics has taken her to many places and industries; over the past three years, she’s worked in the video gaming, fintech, and insurance industries.

Presentations

Building contextual AI assistants with machine learning and open source tools Session

AI assistants are getting a great deal of attention from the industry and research. However, the majority of assistants built to this day are still developed using a state machine and a set of rules. That doesn’t scale in production. Justina Petraityte explores how to build AI assistants that go beyond FAQ interactions using machine learning and open source tools.

Thomas Phelan is cofounder and chief architect of BlueData. Previously, a member of the original team at Silicon Graphics that designed and implemented XFS, the first commercially availably 64-bit file system; and an early employee at VMware, a senior staff engineer and a key member of the ESX storage architecture team where he designed and developed the ESX storage I/O load-balancing subsystem and modular pluggable storage architecture as well as led teams working on many key storage initiatives such as the cloud storage gateway and vFlash.

Presentations

Deep learning with Horovod and Spark using GPUs and Docker containers Session

Today, organizations understand the need to keep pace with new technologies when it comes to performing data science with machine learning and deep learning, but these new technologies come with their own challenges. Thomas Phelan and Nanda Vijaydev demonstrate the deployment of TensorFlow, Horovod, and Spark using the NVIDIA CUDA stack on Docker containers in a secure multitenant environment.

Soumya Ranjan is a data scientist at Gramener AI Labs specializing in using deep learning and machine learning techniques to solve problems across verticals like healthcare, environment conservation, and safety. He’s passionate about data and adores narrating beautiful stories around it, thanks to his experience in building data visualization tools and libraries covering real-time election analysis and visualization. Soumya strongly believes in making quality education free and accessible. To this end, he teaches at universities, is involved in discussing AI/ML curriculum, and has worked as a code reviewer and mentor at Udacity and Thinkful.

Presentations

Predicting the quality of life from satellite imagery Session

In many countries, policy decisions are disconnected from data and very few avenues exist to understand deeper demographic and socioeconomic insights. Ganes Kesari and Soumya Ranjan explain how satellite imagery can be a powerful aid when viewed through the lens of deep learning. When combined with conventional data, it can help answer important questions and show inconsistencies in survey data.

Walter joined Intel in 2017 as an AI TSS (Technical Solution Specialist) covering EMEA and he’s now playing an active role on most of the AI project engagements within the Data Centers business in Europe. He is responsible for increasing business awareness regarding the Intel AI Offer, enabling and provide technical support to end user customers, ISVs, OEMs, Partners in implementing HPC and/or Clouds solutions for AI based on Intel’s products and technologies.

Before joining Intel Walter has collected research experiences working on adopting ML techniques to enhance images retrieval algorithms for robotic applications, conducting sensitive data analysis in a start-up environment and developing software for Text To Speech applications.

Presentations

Keynote by Walter Riviera Keynote

Details to come.

Carlos Rodrigues is a lead cloud engineer and data scientist at Siemens Cyber Defense Department. Previously, Carlos worked at a financial institution that manages more than £20 billion of assets, helping them to design a data-driven strategy, among other things. During his spare time, Carlos teaches postgraduates in data science at Rumos.

Presentations

Fighting cybercrime with AI Session

An evolving landscape of cyber threats demands innovation. It's time to bring AI to the fight. Carlos Rodrigues explains why it's mandatory to use bleeding-edge AI in production to improve threat detection in a worldwide company such as Siemens. The corporate network has more than 500,000 endpoint and more than 370,000 employees. The attack vectors are endless; thus, legacy approaches don't scale.

Tom Sabo is a principal solutions architect at SAS. He’s been immersed in the field of text analytics as it applies to federal government challenges since 2005. Tom presents work internationally on diverse topics including modeling applied to government procurement, best practices in social media analysis, and using analytics to leverage and predict research trends. He also served on a panel for the Institute of Medicine’s Standing Committee on Health Threats Resilience to inform DHS and OHA on social media strategies. He holds a bachelor’s degree in cognitive science and a master’s in computer science, both from the University of Virginia.

Presentations

An artificial intelligence framework to counter international human trafficking Session

Efforts to counter human trafficking internationally must assess data from a variety of sources to determine where best to devote limited resources. Tom Sabo explores text-based machine learning, rule-based text extraction to generate training data for modeling efforts, and interactive visualization to improve international trafficking response.

Mathew Salvaris is a data scientist at Microsoft. Previously, Mathew was a data scientist for a small startup that provided analytics for fund managers and a postdoctoral researcher at UCL’s Institute of Cognitive Neuroscience, where he worked with Patrick Haggard in the area of volition and free will, devising models to decode human decisions in real time from the motor cortex using electroencephalography (EEG); and a postdoctoral position in the University of Essex’s Brain Computer Interface Group, where he worked on BCIs for computer mouse control. Mathew holds a PhD in brain-computer interfaces and an MSc in distributed artificial intelligence.

Presentations

Azure AI reference architectures Session

Dive into the the newly released GitHub repository for recommended ways to train and deploy models on Azure with Danielle Dean, Wee Hyong Tok, and Mathew Salvaris. The repository ranges from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes.

Training and deploying Python models on Azure Tutorial

Danielle Dean, Mathew Salvaris, and Wee Hyong Tok outline the recommended ways to train and deploy Python models on Azure, ranging from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes.

Mathew Salvaris is a senior data scientist at Microsoft. Previously, Mathew was a data scientist for a small startup that provided analytics for fund managers; a postdoctoral researcher at UCL’s Institute of Cognitive Neuroscience, where he worked with Patrick Haggard in the area of volition and free will and devised models to decode human decisions in real time from the motor cortex using electroencephalography (EEG); and he held a postdoctoral position at the University of Essex’s Brain Computer Interface group and was a visiting researcher at Caltech. Mathew holds a PhD in brain-computer interfaces and an MSc in distributed artificial intelligence.

Presentations

Deploying machine learning models on the edge Session

When IoT meets AI, a new round of innovations begins. Yan Zhang and Mathew Salvaris examine the methodology, practice, and tools around deploying machine learning models on the edge. They offer a step-by-step guide to creating an ML model using Python, packaging it in a Docker container, and deploying it as a local service on an edge device as well as deployment on GPU-enabled edge devices.

Alejandro Saucedo is the chief scientist at the Institute for Ethical AI & Machine Learning. In his more than 10 years of software development experience, Alejandro has held technical leadership positions across hypergrowth scale-ups and tech giants including Eigen Technologies, Bloomberg LP, and Hack Partners. Alejandro has a strong track record of building multiple departments of machine learning engineers from scratch and leading the delivery of numerous large-scale machine learning systems across the financial, insurance, legal, transport, manufacturing, and construction sectors in Europe, the US, and Latin America.

Presentations

A practical guide toward algorithmic bias and explainability in machine learning Session

Alejandro Saucedo demystifies AI explainability through a hands-on case study, where the objective is to automate a loan-approval process by building and evaluating a deep learning model. He introduces motivations through the practical risks that arise with undesired bias and black box models and shows you how to tackle these challenges using tools from the latest research and domain knowledge.

Peter is a first year PhD student at UC Berkeley RISELab. His focus is AI systems which involves writing software that makes AI run faster, securely, explainably, and in a way that’s resilient to failures. Currently, he is building an operating system for self-driving cars based on Ray.

Presentations

Scalable AI and Reinforcement Learning with Ray Tutorial

This tutorial will provide a deep dive into Ray, a new distributed execution framework for distributed AI applications developed by machine learning and systems researchers at UC Berkeley’s RISELab, walking you through Ray’s API and system architecture and sharing application examples, including several state-of-the-art distributed training, hyperparameter search, and RL algorithms.

Tuhin Sharma is cofounder of Binaize Labs, an AI-based firm. Previously, he was a data scientist at IBM Watson and Red Hat, where he mainly worked on social media analytics, demand forecasting, retail analytics, and customer analytics, and he worked at multiple startups, where he built personalized recommendation systems to maximize customer engagement with the help of ML and DL techniques across multiple domains like fintech, edtech, media, and ecommerce. He’s filed five patents and published four research papers in the field of natural language processing and machine learning. He holds a postgraduate degree in computer science and engineering, specializing in data mining, from the Indian Institute of Technology Roorkee. He loves to play table tennis and guitar in his leisure time. His favorite quote is “life is beautiful.”

Presentations

Anomaly detection in smart buildings using federated learning Session

There's an exponential growth in the number of internet-enabled devices on modern smart buildings. IoT sensors measure temperature, lighting, IP camera, and more. Tuhin Sharma and Bargava Subramanian explain how they built anomaly-detection models using federated learning for data quality and cybersecurity. Federated learning is privacy preserving and doesn't require data to be moved to the cloud.

Julien Simon is a technical evangelist at AWS. Previously, Julien spent 10 years as a CTO and vice president of engineering at a number of top-tier web startups. He’s particularly interested in all things architecture, deployment, performance, scalability, and data. Julien frequently speaks at conferences and technical workshops, where he helps developers and enterprises bring their ideas to life thanks to the Amazon Web Services infrastructure.

Presentations

A pragmatic introduction to building NLP models Session

Many natural language processing (NLP) tasks require each word in the input text to be mapped to a vector of real numbers. Julien Simon explores word vectors, why they’re so important, and which are the most popular algorithms to compute them (Word2Vec, GloVe, BERT). You'll get to see how to solve typical NLP problems through several demos by either computing embeddings or reusing pretrained ones.

Pramod Singh is a manager for data science at Publicis Sapient and a track lead for a machine learning platform project with Mercedes Benz. He has extensive hands-on experience in machine learning, deep learning, AI, data engineering, programming, and designing algorithms for various business requirements in domains such as retail, telecom, automotive, and consumer goods and has spent the last eight years working on data projects at product and service-based organizations. He’s the author of Machine Learning with PySpark and is also a regular speaker at major conferences and universities. He’s currently writing a couple books on deep learning and AI techniques for O’Reilly and Apress. Pramod holds a bachelor’s degree in electrical and electronics engineering from Mumbai University, an MBA focused on operations and finance from Symbiosis International University, and a data analytics certification from IIM–Calcutta. He lives in Bangalore with his wife and two-year-old son. In his spare time, he enjoys playing guitar, coding, reading, and watching football.

Presentations

Text analytics 101: Deep learning and attention networks all the way to production Tutorial

An estimated 80% of data generated is an unstructured format, such as text, image, audio, or video. Vijay Srinivas Agneeswaran, Pramod Singh, and Akshay Kulkarni explore how to create a language model that generates natural language text by implementing and forming a recurrent neural network and attention networks built on top of TensorFlow 2.0.

Marie Smith is the chief information officer with Data 360. Previously, through various agencies Marie has worked with Warner Bros., Computer Associates, Electronic Arts, Datalabs, Digital Domain, Saatch & Saatchi, the Nobel Foundation, United Healthcare, Bausch and Lomb, Mercedenz Benz, Oprah Store, Lindsay Phillips, Valley Crest, Walmart, Macys, Blue Cross Blue Shield, Aetna, Twitter, and Microstrategy. Marie has continued her educational goals over the last 10 years by attending summer and online graduate programs with Harvard Program on Negotiation and Stanford Center for Professional Development. She’s pursuing a JD and PhD in legal informatics from Stanford University. Marie has recently become an instructor for corporate data analysis and technology development with Harvard Innovation Lab.

Presentations

UX considerations for designing research for big data, artificial intelligence, machine learning Session

Join Marie Smith to hear some key findings as illuminated by her career since 1998 of the rapid prototyping world of Silicon Valley and R&D and innovation projects from many large financial, insurance, health, real estate, retail, and entertainment companies.

Gianmario Spacagna is the chief scientist and head of AI at Helixa. His team’s mission is building the next generation of behavior algorithms and models of human decision making with careful attention to their potential and effects on society. His experience covers a diverse portfolio of machine learning algorithms and data products across different industries. Previously, he worked as a data scientist in IoT automotive (Pirelli Cyber Technology), retail and business banking (Barclays Analytics Centre of Excellence), threat intelligence (Cisco Talos), predictive marketing (AgilOne), plus some occasional freelancing. He’s a coauthor of the book Python Deep Learning, contributor to the “Professional Manifesto for Data Science,” and founder of the Data Science Milan community. Gianmario holds a master’s degree in telematics (Polytechnic of Turin) and software engineering of distributed systems (KTH of Stockholm). After having spent half of his career abroad, he now lives in Milan. His favorite hobbies include home cooking, hiking, and exploring the surrounding nature on his motorcycle.

Presentations

Audience projection of target consumers over multiple domains: A NER and Bayesian approach Session

AI-powered market research is performed by indirect approaches based on sparse and implicit consumer feedback (e.g., social network interactions, web browsing, or online purchases). These approaches are more scalable, authentic, and suitable for real-time consumer insights. Gianmario Spacagna proposes a novel algorithm of audience projection able to provide consumer insights over multiple domains.

Vijay Srinivas Agneeswaran is a director of data sciences at Walmart Labs in India, where he heads the machine learning platform development and data science foundation teams, which provide platform and intelligent services for Walmart businesses around the world. He’s spent the last 18 years creating intellectual property and building data-based products in industry and academia. Previously, he led the team that delivered real-time hyperpersonalization for a global automaker, as well as other work for various clients across domains such as retail, banking and finance, telecom, automotive, etc; he built PMML support into Spark and Storm and realized several machine learning algorithms such as LDA and Random Forests over Spark; he led a team that designed and implemented a big data governance product for a role-based fine-grained access control inside of Hadoop YARN; and he and his team also built the first distributed deep learning framework on Spark. He’s a professional member of the ACM and the IEEE (senior) for the last 10+ years. He has five full US patents and has published in leading journals and conferences, including IEEE transactions. His research interests include distributed systems, artificial intelligence, and big data and other emerging technologies. Vijay has a bachelor’s degree in computer science and engineering from SVCE, Madras University, an MS (by research) from IIT Madras, a PhD from IIT Madras, and a postdoctoral research fellowship in the LSIR Labs, Swiss Federal Institute of Technology, Lausanne (EPFL).

Presentations

Industrialized capsule networks for text analytics Session

Vijay Srinivas Agneeswaran and Abhishek Kumar outline how to industrialize capsule networks by detailing capsule networks and how capsule networks help handle spatial relationships between objects in an image and how to apply them to text analytics and tasks such as NLU or summarization. Join in to see a scalable, productionizable implementation of capsule networks over KubeFlow.

Text analytics 101: Deep learning and attention networks all the way to production Tutorial

An estimated 80% of data generated is an unstructured format, such as text, image, audio, or video. Vijay Srinivas Agneeswaran, Pramod Singh, and Akshay Kulkarni explore how to create a language model that generates natural language text by implementing and forming a recurrent neural network and attention networks built on top of TensorFlow 2.0.

Bargava Subramanian is a cofounder and machine learning engineer of the boutique AI firm Binaize Labs in Bangalore, India. He has 15 years’ experience delivering business analytics and machine learning solutions to B2B companies, and he mentors organizations in their data science journey. He holds a master’s degree from the University of Maryland, College Park. He’s an ardent NBA fan.

Presentations

Anomaly detection in smart buildings using federated learning Session

There's an exponential growth in the number of internet-enabled devices on modern smart buildings. IoT sensors measure temperature, lighting, IP camera, and more. Tuhin Sharma and Bargava Subramanian explain how they built anomaly-detection models using federated learning for data quality and cybersecurity. Federated learning is privacy preserving and doesn't require data to be moved to the cloud.

Zaid Tashman is a R&D data scientist at Accenture Labs exploring new research problems in the areas of probabilistic programming, casual inference, and stochastic optimization. Zaid has a progressive experience in recommendation systems, customer behavior analysis, survival modeling, failure time prediction, hierarchical Bayesian networks, and anomaly detection. Previously, Zaid was a senior data scientist at ABB where he led the analytics efforts within ABB’s IoT platform serving all of their business units and a senior data scientist at Spacetime Insights, a Silicon Valley IoT startup where he successfully led and completed many machine learning projects in areas of predictive maintenance, anomaly detection, fraud detection, and optimization. Zaid holds a MSc in electrical engineering from Washington State University.

Presentations

Rethinking predictive maintenance Session

Today traditional approaches to predictive maintenance fall short. Zaid Tashman dives into a novel approach to predict rare events using a probabilistic model, the mixed membership hidden Markov model, highlighting the model's interpretability, its ability to incorporate expert knowledge, and how the model was used to predict the failure of water pumps in developing countries.

Bharath Thota is a vice president with A.T. Kearney’s analytics practice with over 14 years of deep expertise in the application of data science, advanced analytics, and technology to help clients with analytics transformation, improve business performance, drive operational excellence, and become more insight driven. He’s contributed to research and written on the topic of big data.

Presentations

Executive Briefing: From laggard to leader—Winning the AI race Session

The Analytics Impact Index gives organizations an understanding of the value potential of analytics as well as the capabilities required to capture the most value. Ujwal Kayande, Anastasia Kouvela, and Bharath Thota walk you through the 2019 results and the analytics journey of leading global organizations and empower companies to develop a case for change.

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

Azure AI reference architectures Session

Dive into the the newly released GitHub repository for recommended ways to train and deploy models on Azure with Danielle Dean, Wee Hyong Tok, and Mathew Salvaris. The repository ranges from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes.

Time series forecasting: Build and deploy your ML models to forecast the future 2-Day Training

Francesca Lazzeri, Wee Hyong Tok, Krishna Anumalasetty, and Aashish Bhateja walk you through the core steps of training your machine learning time series forecasting models using Python and Azure Machine Learning both locally and on remote compute resources.

Training and deploying Python models on Azure Tutorial

Danielle Dean, Mathew Salvaris, and Wee Hyong Tok outline the recommended ways to train and deploy Python models on Azure, ranging from running massively parallel hyperparameter tuning using Hyperdrive to deploying deep learning models on Kubernetes.

Jameson Toole is the CEO and cofounder of Fritz, a company building tools to help developers optimize, deploy, and manage machine learning models on mobile devices. Previously, he built analytics pipelines for Google X’s Project Wing and ran the data science team at Boston technology startup Jana Mobile. He holds undergraduate degrees in physics, economics, and applied mathematics from the University of Michigan and both an MS and PhD in engineering systems from MIT, where he worked on applications of big data and machine learning to urban and transportation planning at the Human Mobility and Networks Lab.

Presentations

Creating smaller, faster, production-worthy mobile machine learning models Session

Getting machine learning models ready for use on device is a major challenge. Drag-and-drop training tools can get you started, but the models they produce aren’t small enough or fast enough to ship. Jameson Toole walks you through optimization, pruning, and compression techniques to keep app sizes small and inference speeds high.

Arun Verma is the head of the quantitative research solutions team at Bloomberg. He also serves on the board of a nonprofit that helps with humanitarian projects in India serving impoverished children and women in the areas of education and vocational training. Since he joined the Bloomberg Quantitative Research Group, Arun has worked on stochastic volatility models for derivatives and exotics pricing and hedging (e.g., variance swaps and VIX Futures fair pricing and cross-currency volatility surface construction) and at the intersection of diverse areas such as data science, innovative quantitative finance models across asset classes, and using machine learning methods to help reveal embedded signals in traditional and alternative data that can be used to construct quantitative trading strategies. He holds a PhD in computer science and applied mathematics from Cornell University and a bachelor of technology in computer science from IIT Delhi. Arun lives in central New Jersey with his lovely wife and two children.

Presentations

Extracting trading signals from alternative data using machine learning Session

To gain an edge in the markets, quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly nontraditional sources of data. Arun Verma illustrates the use of NLP, AI, and ML techniques that help extract derived signals that have significant trading alpha or risk premium and lead to profitable trading strategies.

Nanda Vijaydev is the lead data scientist and head of solutions at BlueData (now HPE), where she leverages technologies like TensorFlow, H2O, and Spark to build solutions for enterprise machine learning and deep learning use cases. Nanda has more than 10 years of experience in data science and data management. Previously, she worked on data science projects in multiple industries as a principal solutions architect at Silicon Valley Data Science and served as director of solutions engineering at Karmasphere.

Presentations

Deep learning with Horovod and Spark using GPUs and Docker containers Session

Today, organizations understand the need to keep pace with new technologies when it comes to performing data science with machine learning and deep learning, but these new technologies come with their own challenges. Thomas Phelan and Nanda Vijaydev demonstrate the deployment of TensorFlow, Horovod, and Spark using the NVIDIA CUDA stack on Docker containers in a secure multitenant environment.

Bruno Wassermann is a research staff member at IBM Research – Haifa, where he’s worked on parts of the distributed systems infrastructure of Watson Developer Cloud, is trying to help SREs make better sense of monitoring and log data, and, more recently, has begun working on some of the issues that arise from the productionization of machine learning applications.

Presentations

Clue: Evaluate the impact of your new training pipeline on existing models in production Session

There's a new version of your complex machine learning pipeline, but you need to make sure it doesn't negatively impact the performance of large numbers of existing customer models in production. Bruno Wassermann explains how IBM Research tackled the challenge for the natural language understanding layer of the IBM Watson Assistant service and demonstrates a new tool called Clue.

Emily Webber is a machine learning specialist solutions architect at Amazon Web Services (AWS), where she guides customers from project ideation to full deployment, focusing on Amazon SageMaker, where her customers are household names across the world, such as T-Mobile. She’s been leading data science projects for many years, piloting the application of machine learning into such diverse areas as social media violence detection, economic policy evaluation, computer vision, reinforcement learning, IoT, drone, and robotic design. Previously, she was a data scientist at the Federal Reserve Bank of Chicago and a solutions architect for an explainable AI startup in Chicago. Her master’s degree is from the University of Chicago, where she developed new applications of machine learning for public policy research with the Data Science for Social Good Fellowship.

Presentations

Public policy and deep reinforcement learning on AWS Keynote

If you've ever wondered if you could use AI to inform public policy, join Emily Webber as she combines classic economic methods with AI techniques, training a reinforcement learning agent on decades of randomized control trials. You'll learn about classic philosophical foundations for public policy decision making and how these can be applied to solve the problems that impact the many.

Tony Xing is a senior product manager in the AI, data, and infrastructure (AIDI) team within Microsoft’s AI and Research Organization. Previously, he was a senior product manager on the Skype data team within Microsoft’s Application and Service Group, where he worked on products for data ingestion, real-time data analytics, and the data quality platform.

Presentations

Introducing a new anomaly-detection algorithm (SR-CNN) inspired by computer vision Session

Anomaly detection may sound old fashioned, yet it's super important in many industry applications. Tony Xing, Bixiong Xu, Congrui Huang, and Qun Ying detail a novel anomaly-detection algorithm based on spectral residual (SR) and convolutional neural network (CNN) and how this method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention.

Bixiong Xu is the principal development manager on the AI, data, and infrastructure team at Microsoft.

Presentations

Introducing a new anomaly-detection algorithm (SR-CNN) inspired by computer vision Session

Anomaly detection may sound old fashioned, yet it's super important in many industry applications. Tony Xing, Bixiong Xu, Congrui Huang, and Qun Ying detail a novel anomaly-detection algorithm based on spectral residual (SR) and convolutional neural network (CNN) and how this method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention.

Qun Ying is a senior product manager with AI platform team of the Microsoft Cloud and AI Division.

Presentations

Introducing a new anomaly-detection algorithm (SR-CNN) inspired by computer vision Session

Anomaly detection may sound old fashioned, yet it's super important in many industry applications. Tony Xing, Bixiong Xu, Congrui Huang, and Qun Ying detail a novel anomaly-detection algorithm based on spectral residual (SR) and convolutional neural network (CNN) and how this method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention.

Ji Chao Zhang is the director of software engineering and a member of the Georgian impact team. In that role, he leads its internal software engineering efforts and supports portfolio engagements.

Presentations

Building differentially private machine learning models using TensorFlow Session

The world is increasingly data driven, and people have developed an awareness and concern for their data. Chang Liu and Ji Chao Zhang examine differential privacy and its use cases, the component of the TensorFlow privacy library that allows users to train differentially private logistic regression and support vector machines, and real-world scenarios and demonstrations for how to apply the tools.

Yan Zhang is a senior data scientist with the algorithm and data science team of the Data Group, Cloud and Enterprise with Microsoft. She builds predictive analytics models and generalizes machine learning solutions on the cloud machine learning platform. Her recent research includes cost prediction and fraud claim detection in the healthcare domain, predictive maintenance in IoT applications, customer segmentation, and text mining. Previously, she was a research faculty at Syracuse University. Yan earned her PhD in data mining at the computer science department at the University of Vermont. She’s an author of 23 publications, including journal articles, conference papers, and blog posts. Her first authored paper won the best paper award in the 17th IEEE International Conference on tools with artificial intelligence. She’s one of the reviewers for the book Predictive Analytics with Microsoft Azure Machine Learning, second edition, published in September 2015.

Presentations

Deploying machine learning models on the edge Session

When IoT meets AI, a new round of innovations begins. Yan Zhang and Mathew Salvaris examine the methodology, practice, and tools around deploying machine learning models on the edge. They offer a step-by-step guide to creating an ML model using Python, packaging it in a Docker container, and deploying it as a local service on an edge device as well as deployment on GPU-enabled edge devices.

Zhe Zhang is an engineering manager at LinkedIn, where he leads an excellent engineering team to provide big data services (Hadoop distributed file system (HDFS), YARN, Spark, TensorFlow, and beyond) to power LinkedIn’s business intelligence and relevance applications. Zhe’s an Apache Hadoop PMC member; he led the design and development of HDFS Erasure Coding (HDFS-EC).

Presentations

Machine learning challenges at LinkedIn: Spark, TensorFlow, and beyond Keynote

From people you may know (PYMK) to economic graph research, machine learning is the oxygen to power how LinkedIn serves its 630M+ members. Zhe Zhang provides you with an architectural overview of LinkedIn’s typical machine learning pipelines complemented with key types of ML use cases.

Weifeng Zhong is a senior research fellow at the Mercatus Center at George Mason University. His work focuses on bridging the field of natural language processing and machine learning to economic policy studies. His other research interests include the political economy, US-China economic relations, and China’s economic issues. Weifeng is a core maintainer of the open source Policy Change Index (PCI) project, a framework that uses machine learning to “read” large volumes of text and detect subtle, structural changes embedded in it. As a first use case, the PCI for China is an algorithm that can predict China’s policy changes using the information in the government’s official newspaper. The PCI framework has received significant academic interest and media coverage. The resources of this project are freely available at Policychangeindex.org. Weifeng has been published in a variety of scholarly journals, including the Journal of Institutional and Theoretical Economics. His research and writings have been featured in the Financial Times, Foreign Affairs, The National Interest, Real Clear Markets, Real Clear Politics, the South China Morning Post, and the Wall Street Journal, among others.

Presentations

Learning structural changes from text data Session

Weifeng Zhong explores a novel method to learn structural changes embedded in unstructured texts based on the Policy Change Index (PCI) framework developed by economists Julian Chan and Weifeng Zhong. He explains how an off-the-shelf application of deep learning—with an important twist—can help you detect structural breakpoints in time series text data.

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