October 28–31, 2019

Speakers

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

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

Presentations

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. Using deep learning can be very energy-efficient, and allows us to make sense of sensor data in real time. This talk shows you how.

Currently leads TensorFlow model optimization, aimed at making machine learning more efficient to deploy and execute. He is a co-founder and engineering lead of TensorFlow Lite. Prior to that he developed the framework used to execute embedded ML models for Google’s speech recognition software (now in TensorFlow Lite) and lead the development of the latest iteration of the “hey Google” hotword recognizer.

Before Google, Raziel co-designed and implemented the Self-Assembling Interface Layer that forms the core of Appian’s (APPN) low-code development platform.

He graduated summa cum laude from both the B.S. and master’s programs in computer science and machine learning at Mexico’s ITESM.

Presentations

High-Performing TensorFlow Session

Presentation for both best current practices and future directions in core technology.

Leonardo is a Machine Learning Engineer at Clarabridge. He is currently solving NLP tasks, like detecting emotion, call reason, expressed effort in the Customer Experience domain. He has experience in maintaining and improving NLP pipelines to extract entities and topics from over 30 million websites daily using the latest NLP and deep learning techniques. Leonardo has also built scalable analytics techniques for anomaly detection using datasets with billions of events.

Presentations

Enterprise AF Solution for Text Classification (using BERT) Tutorial

What is BERT? How does one use BERT to solve problems? Google Colab, Tensorflow, Kubernetes on Google Cloud

Josh Baer is currently leading the ML platformization effort at Spotify, building out the tools, processes and infrastructure for robust Machine Learning experience: enabling teams to leverage ML/AI sustainably in their products, research and services. Formerly: led Hadoop, Stream Processing teams.

Presentations

Personalizing the infinite jukebox: ML and the Tensorflow Ecosystem at Spotify Session

A background on Spotify's historical use of Machine Learning in the product and a discussion on how the introduction of Tensorflow and Tensorflow Extended in particular has standardized our ML workflows and improved our ability to bring ML-powered products to our users.

Paige Bailey is a TensorFlow developer advocate at Google.

Presentations

Getting Involved In The TensorFlow Community Session

Learn how you can be a part of the growing TensorFlow ecosystem, and become a contributor through code, documentation, education and community leadership.

Swift for TensorFlow Session

Swift for TensorFlow is a next generation machine learning platform that leverages innovations like first-class differentiable programming to seamlessly integrate deep neural networks with traditional AI algorithms and general purpose software development.

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

How Azure Machine Learning makes the Data Scientist’s life easier 2-Day Training

In this 2-day training, we will show how AzureML helps the data scientist to be more productive when working through the process of developing TensorFlow models for production. We will show participants aspects across the whole model development lifecycle from training, through deployment, MLOps, and all the way to model interpretability.

Joe Bowser is the Lead Developer on the Sensei On Device team that is deploying Machine Learning technologies into various products at Adobe. Prior to this, he was the creator of PhoneGap for Android and the longest contributing committer to the PhoneGap and Apache Cordova projects respectively. When he is not contributing to Open Source at Adobe, he spends his spare time working on various hardware projects, most of which involve First Person View mini-quadcopters.

Presentations

Working with TFLite on Android with C++ Session

How to TFLite with an existing C++ codebase on Android by using the NDK and the TFLite Build Tree.

Paris Buttfield-Addison is cofounder of Secret Lab, a game development studio based in beautiful Hobart, Australia. Secret Lab builds games and game development tools, including the multi-award-winning ABC Play School iPad games, 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 mobile product manager for Meebo (acquired by Google). Paris particularly enjoys game design, statistics, law, machine learning, and human-centered technology research and writes technical books on mobile and game development (more than 20 so far) for O’Reilly. He holds a degree in medieval history and a PhD in computing. He’s currently writing “Practical AI with Swift” for O’Reilly Media. You can find him on Twitter @parisba

Presentations

Swift for TensorFlow in 3 hours Tutorial

Isn't Swift just for app developers? No! Swift for TensorFlow provides the power of TensorFlow with all the advantages of Python (and complete access to Python libraries, as needed) and all the advantages of Swift, the safe, fast, incredibly capable open source programming language; Swift for TensorFlow is the perfect way to learn both deep learning and Swift.

Andy Chamberlain is a project manager in Theoretical Ecology Lab at Stanford University. He specializes in GIS analysis, drone operations, and machine learning.

Presentations

Building Deep Learning Applications using TensorFlow to Combat Schistosomiasis Session

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 250 million people worldwide; in this session, we will show how we build and deploy deep learning applications to detect disease transmission hotspots, make disease control interventions more efficient and scalable, and help local governments and stakeholders make data-driven decisions.

Wen-Heng (Jack) Chung has been working on ROCm stack since its early inception. He had experiences in compiler frontend, optimization passes, and runtime for high-level languages. His focus lately has been TensorFlow XLA.

Presentations

Modular convolution considered beneficial Session

We explore breaking convolution algorithms into modular pieces to be better fused with graph compilers such as XLA.

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

ML in Production - Getting started with TensorFlow Extended (TFX) Tutorial

Learn what’s involved in creating a production ML pipeline, and walk through working code with experts from Google.

Wisdom grew up in Togo where he earned a B.S. He is currently a Master’s student at KIIT University in India. Wisdom’s research interests include NLP and explainable AI, with a focus on commonsense reasoning for “clinical” natural language understanding and medical report generation. Wisdom won a Government of India National Award in 2018 and previously interned at Google in San Francisco, where he launched Google Cloud support for cloud emerging languages. He demoed at Google Cloud Next 2018 and frequently speaks at local meetups. In his spare time, you can see Wisdom struggling with his vocal chords and his guitar strings.

Presentations

Diagnose and Explain: Neural X-Ray Diagnosis with Visual and Textual Evidence Session

In this presentation, we will show how to design an encoder-decoder model that takes a chest x-ray image as input and generates a radiology report with visual and textual explanations, for interpretability. The model was designed with TensorFlow, trained on Cloud TPUs, and deployed in the browser with TensorFlow.js. The presentation includes a live demo of the model in action.

Keshi Dai is an engineer at Spotify, working to build out machine learning infrastructure that supports hundreds of engineers and the growth of ML in products at Spotify. In the past, Keshi has worked on the other side of ML as one of the engineers building out recommendation products at Spotify. He knows first hand the challenges presented when productionizing ML and the benefit in using standard infrastructure in many parts of the workflow.

Presentations

Personalizing the infinite jukebox: ML and the Tensorflow Ecosystem at Spotify Session

A background on Spotify's historical use of Machine Learning in the product and a discussion on how the introduction of Tensorflow and Tensorflow Extended in particular has standardized our ML workflows and improved our ability to bring ML-powered products to our users.

Jules S. Damji is an Apache Spark Community and Developer Advocate at Databricks. He is a hands-on developer with over 20 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, LoudCloud/Opsware, VeriSign, ProQuest, and Hortonworks, building large-scale distributed systems. He holds a B.Sc and M.Sc in Computer Science and MA in Political Advocacy and Communication from Oregon State University, Cal State, and Johns Hopkins University respectively.

Presentations

How to Track and Manage TensorFlow 2.0/Keras Model Experiments with MLflow Session

This session introduces MLflow open-source platform to manage model lifecycle. It supports many model flavors, such as MLeap, MLlib, Scikit-Learn, PyTorch, and TensorFlow, and Keras. In this particular session, we'll focus on TensorFlow 2.0/Keras models

Shajan Dasan works on prediction service – which enables different services perform high scale inference at Twitter. Before ML, he built distributed systems for Information Retrieval (web crawler and indexer for Bing), Data Storage (video / photo / large object store at Twitter) and Video Transcoding (video backend at Twitter). Prior to Distributed Systems, Shajan worked on the first version of C# language, where he implemented the type safety verifier.

Presentations

Reliable, High Scale Tensorflow Inference Pipelines at Twitter Session

Twitter heavily relies on Scala/JVM and has a lot of expertise knowledge. To provide a reliable Tensorflow inference offering to the Twitter customer teams, we’ve had to overcome new problems along the way to make our offering reliable. In this presentation, we'll present our key learnings.

Presentations

TensorFlow Lite: Beginner to Expert Tutorial

Introduces TensorFlow Lite to users and takes them through the conversion, performance and optimization path utilizing Android & iOS applications.

Giulio De Leo is a theoretical ecologist by formation, he is interested in investigating factors and processes driving the dynamics of natural and harvested populations and in understanding how to use this knowledge to inform practical management. He is the scientific director of the newly established Center for Disease Ecology, Health and the Environment at Stanford.

Presentations

Building Deep Learning Applications using TensorFlow to Combat Schistosomiasis Session

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 250 million people worldwide; in this session, we will show how we build and deploy deep learning applications to detect disease transmission hotspots, make disease control interventions more efficient and scalable, and help local governments and stakeholders make data-driven decisions.

Jeff Dean (ai.google/research/people/jeff) joined Google in 1999 and is currently a Google Senior Fellow and SVP for Google AI and related research efforts. His teams are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google’s crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google’s initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google’s distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools.

Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing.

Presentations

Keynote with Jeff Dean Keynote

Jeff Dean, Google Senior Fellow and SVP for Google AI

Victor Dibia is a Research Engineer with Cloudera’s Fast Forward Labs where his work focuses on prototyping state of the art machine learning algorithms and advising clients. Prior to this, he was a Research Staff Member at the IBM TJ Watson Research Center, New York. His research interests are at the intersection of human computer interaction, computational social science, and applied AI. A senior member of IEEE, Victor has published work at venues like the AAAI Conference on Artificial Intelligence and ACM Conference on Human Factors in Computing Systems. His work has been featured in outlets such as the Wall Street Journal and VentureBeat. He holds an M.S. from Carnegie Mellon University and a Ph.D. from City University of Hong Kong.

Presentations

Handtrack.js: Building Gesture-based Interactions in the Browser Using Tensorflow.js Session

This talk explores the state of the art for Machine Learning in the browser using Tensorflow.js, and covers its use in the design of Handtrack.js - a library for prototyping real-time hand-tracking interactions in the browser

Tulsee is the Product Lead for Google’s ML Fairness Effort. In this role, she leads the development of Google-wide resources and best practices for developing more inclusive and diverse products. Prior to ML Fairness, Tulsee worked on the YouTube recommendations team. She received her BS in Symbolic Systems and MS in Computer Science from Stanford University.

Presentations

Build more inclusive TF pipelines with Fairness Indicators! Session

Today, we are announcing and launching Fairness Indicators; Fairness Indicators is built on-top of Tensorflow Model Analysis, and provides the ability to measure and improve algorithmic bias.

Yann is a machine learning engineer and privacy researcher at Dropout Labs. He started his career as an actuary at the largest insurance company in Canada, first in reinsurance then in research and development. He then managed a data science team at Deloitte in San Francisco, working with several Fortune 500 enterprises in the Consumer and Product industry. He holds a MASc in Electrical and Computer Engineering from Institut Superieur d’Electronique de Paris. In his free time, you can find him surfing at Ocean Beach or indoor rock climbing in San Francisco.

Presentations

Privacy-Preserving Machine Learning with TensorFlow and TF Encrypted Tutorial

Build and serve privacy-preserving machine learning models using TF Encrypted and the TensorFlow ecosystem.

Jordan Edwards is a principal program manager at Microsoft, working on machine learning frameworks and tools. He focuses on all aspects of ML ops, bringing machine learning workflows to production by augmenting existing DevOps (CI/CD) practices to account for the complexity of model training, validation, deployment and monitoring.

Presentations

How Azure Machine Learning makes the Data Scientist’s life easier 2-Day Training

In this 2-day training, we will show how AzureML helps the data scientist to be more productive when working through the process of developing TensorFlow models for production. We will show participants aspects across the whole model development lifecycle from training, through deployment, MLOps, and all the way to model interpretability.

Úlfar Erlingsson is a Research Scientist in the Google Brain team, currently working primarily on privacy and security of deep learning systems. Previously, Úlfar has led computer security research at Google, and been been a researcher at Microsoft Research, Silicon Valley and Associate Professor at Reykjavik University. Úlfar was co-founder and CTO of the Internet security startup Green Border Technologies and Director of Privacy Protection at deCODE Genetics. Úlfar holds a PhD in computer science from Cornell University.

Presentations

TensorFlow Privacy: Learning with Differential Privacy for Training Data Session

Learn how to offer strong privacy guarantees for ML training data by using TensorFlow Privacy.

Pengfei is a senior heterogeneous computing engineer at Alibaba Cloud. He joined applied AI architecture team in 2018 after a 6-year stint in NVIDIA where he worked on GPU compute architecture. Lately, Pengfei is focused on designing and implementing virtualization and scheduling systems for heterogeneous infrastructure to accelerate AI applications and improve hardware utilization.

Presentations

HARP: An Efficient and Elastic GPU-Sharing System Session

We developed an efficient and elastic GPU-sharing system for users who do research and development with TensorFlow.

Will’s role as Machine Learning (ML) Researcher concentrates on the technological progress of the company.

He contributes an understanding of the most advanced methods in ML, along with experience in research and an eye for innovation. His academic career begun as a chemist at Oxford; later, he moved to UCL for a further MSc in Computational Statistics and ML.

Project and research work aside, Will has been delivering training days for companies to help them get started with ML. He believes in continuous education and learning as an essential part of technical excellence. This passion extends into his personal life, where he plays with maths, programming and puzzles.

Presentations

Effective sampling methods within TensorFlow input functions Session

A demonstration of sampling techniques applied to training / testing data directly inside the input function using the tf.data API.

Marina Rose Geldard, more commonly known as Mars, is a technologist from Down Under in Tasmania. Entering the world of technology relatively late as a mature-age student, she has found her place in the world: an industry where she can apply her lifelong love of mathematics and optimization. When she isn’t busy being the most annoyingly eager graduate student ever, she compulsively volunteers at industry events, conducts research, and serves on the executive committee for her state’s branch of the Australian Computer Society (ACS) as well as the AUC. She’s currently writing Practical Artificial Intelligence with Swift for O’Reilly and working on machine learning projects to improve public safety through public CCTV cameras in her hometown of Hobart.

Presentations

Swift for TensorFlow in 3 hours Tutorial

Isn't Swift just for app developers? No! Swift for TensorFlow provides the power of TensorFlow with all the advantages of Python (and complete access to Python libraries, as needed) and all the advantages of Swift, the safe, fast, incredibly capable open source programming language; Swift for TensorFlow is the perfect way to learn both deep learning and Swift.

Aurélien Géron is a machine learning consultant at Kiwisoft. Previously, he led YouTube’s video classification team and was founder and CTO of two successful companies (a telco operator and a strategy firm). Aurélien is the author of several technical books, including the O’Reilly book Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow.

Presentations

Natural Language Processing using Transformer Architectures Session

Transformer architectures have recently taken the field of Natural Language Processing by storm and pushed Recurrent Neural Networks to the sidelines. This talk will present Transformers and the amazing language models based on them (e.g., BERT and GPT-2), and show how you can use them in your projects.

Josh Gordon is a Developer Advocate at Google AI, and teaches Applied Deep Learning at Columbia University, and Machine Learning at Pace University. He has over a decade of machine learning experience to share. You can find him on Twitter at

Presentations

Introduction to TensorFlow 2.0: Easier for beginners, and more powerful for experts Session

TensorFlow 2.0 is all about ease of use, and there has never been a better time to get started.

Martin Gorner is a developer advocate at Google, where he focuses on parallel processing and machine learning. Martin is passionate about science, technology, coding, algorithms, and everything in between. He spent his first engineering years in the Computer Architecture Group of ST Microlectronics, then spent the next 11 years shaping the nascent ebook market at Mobipocket, which later became the software part of the Amazon Kindle and its mobile variants. He’s the author of the successful TensorFlow Without a PhD series. He graduated from Mines Paris Tech.

Presentations

Fast and lean data science with TPUs Session

Neural networks are now shipping in consumer-facing projects. Enterprises need to train and ship them fast and data scientist want to waste less time on endless training. Google TPUs (Tensor Processing Units) are here to help.

Recurrent neural networks without a PhD Tutorial

Martin Gorner leads a hands-on introduction to recurrent neural networks and TensorFlow. Join in to discover what makes RNNs so powerful for time series analysis.

Christina Greer is a Software Engineer on the Google Brain team. She focuses specifically on Machine Learning Fairness in the context of Model Evaluation and Understanding, and scaling up solutions for ML Fairness to support many teams across Google.

Prior to her work on ML Fairness, Christina has worked on building infrastructure to support diverse Google products: Google Assistant, Cloud Dataflow, and Ads. Working in this area of ML Fairness allows her to combine building infrastructure at Google scale with advancing efforts to avoid creating or reinforcing existing biases. Christina received her B.S. in Computer Science from the University of Kansas.

Presentations

Build more inclusive TF pipelines with Fairness Indicators! Session

Today, we are announcing and launching Fairness Indicators; Fairness Indicators is built on-top of Tensorflow Model Analysis, and provides the ability to measure and improve algorithmic bias.

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

Presentations

Introduction to machine learning in JavaScript using TensorFlow.js Tutorial

Hands on tutorial to learn building and deploying ML models using JavaScript using official documentation, examples, and codelabs from the TensorFlow.js team

Kevin Haas is a senior engineering manager at Google Research, driving the open source adoption of Tensorflow Extended (https://github.com/tensorflow/tfx), one of Google’s production ML platforms. Kevin previously served as an engineering leader for multiple machine learning and infrastructure efforts in Google Cloud, Research, and Infrastructure teams. Prior to Google, Kevin led knowledge and search infrastructure efforts in multiple Internet and software companies, including IBM, Microsoft, and Yahoo!.

Kevin received his MS from Stanford University in Computer Science in dual specializations of distributed systems and databases.

Presentations

TFX: Production ML Pipelines with TensorFlow Session

Learn about TensorFlow Extended.

Adam joined Quantiphi, a Deep Learning and Artificial Intelligence Solutions company, 5 years ago. Since then, he has been actively involved in developing and delivering on solutions in the healthcare and insurance industries (both of which often call for interpretable models). Adam holds an MBA from Bentley University and an undergraduate degree in economics.

Presentations

Tagging Cancer Recurrence through Machine Learning Session

Learn how Tensorlfow and Cloud Machine Learning Engine helped a healthcare provider to develop a solution designed to predict the patient encounters associated with recurrence of cancer.

Asif is the co-founder of Quantiphi, Inc., a category-defining Applied AI and Big Data software and services provider. He has over 15 years of experience in technology services, healthcare and financial services industries working on a variety of initiatives such as building applied AI and advanced analytics capabilities at a global scale, post-merger integration, supply-chain operations, business transformation, and professional services. 



Prior to founding Quantiphi, Asif lead a global team of analytics and data science professionals focused on developing leading-edge analytical algorithms and solutions for business decision support for a multi-billion dollar global healthcare services business including customer experience, service delivery, supply chain, and professional services.

He holds an MBA from McCallum Graduate school of Business at Bentley University and participated in executive ed. programs at Harvard Business School.

Presentations

Tagging Cancer Recurrence through Machine Learning Session

Learn how Tensorlfow and Cloud Machine Learning Engine helped a healthcare provider to develop a solution designed to predict the patient encounters associated with recurrence of cancer.

Jonathan is a software engineer at Google where he manages the TensorFlow TPU project. His current focus areas include improving the usability, performance, and capabilities of TPU hardware for both internal and Cloud users. His interests also include large batch training and training massive models.

Presentations

Scaling TensorFlow using tf.distribute Session

Come learn about how you can use tf.distribute to scale your machine learning model on a variety of hardware platforms ranging from commercial cloud platforms to dedicated hardware. We will have a dedicated section talking about tools and tips to get the best scaling for your training in TensorFlow.

Shengsheng (Shane) Huang is a software architect at Intel and an Apache Spark committer and PMC member, leading the development of large-scale analytical applications and infrastructure on Spark in Intel. Her area of focus is big data and distributed machine learning, especially deep (convolutional) neural networks. Previously at NUS (National University of Singapore), her research interests are large-scale vision data analysis and statistical machine learning.

Presentations

Building AI to play FIFA video game using distributed Tensorflow Session

In this session we will share our experience and learning about building AI to play FIFA video game using distributed Tensorflow

Hamel Husain is a Machine Learning Engineer at Github who is focused on creating the next generation of developer tools powered by machine learning. His work involves extensive use of natural language and deep learning techniques to extract features from code and text. Prior to Github, Hamel was a Data Scientist at Airbnb where he worked on growth marketing and at DataRobot where he helped build automated machine learning tools for data scientists.

Presentations

Automating Your Developer Workflow on GitHub with Tensorflow Session

A tutorial on how to use a freely available, natural language dataset to build practical applications for anyone who writes software, with Tensorflow.

Ankit currently works as a Senior Research Scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of Deep Learning methods to a variety of Uber’s problems ranging from forecasting, food delivery to self driving cars. Previously, he has worked in variety of data science roles at Bank of America, Facebook and other startups. 
He has co-authored a book on machine learning titled “Tensorflow Machine Learning Projects”. Additionally, he has been a featured speaker in many of the top AI conferences and universities across US including UC Berkeley, OReilly AI conference etc. He completed his MS from UC Berkeley and BS from IIT Bombay (India).

Presentations

Enhance Recommendations in Uber Eats with Graph Convolutional Networks Session

In this talk, we show how to generate better restaurant and dish recommendations in UberEats by learning entity embeddings using Graph Convolutional Networks implemented in Tensorflow

Michał Jastrzębski is Staff Data Engineer at GitHub where he builds machine learning infrastructure for internal purposes. Before GitHub, he was architect at Intel’s Open Source Technology Center. Michał has a long experience in cloud technologies like OpenStack and Kubernetes, both as an operator and contributor. As former leader of OpenStack Kolla, he was managing a community of more than 200 people and almost 40 companies. Lately Michal has been involved with machine learning on Kubernetes communities like Kubeflow.

Presentations

Automating Your Developer Workflow on GitHub with Tensorflow Session

A tutorial on how to use a freely available, natural language dataset to build practical applications for anyone who writes software, with Tensorflow.

Lingling is a Senior Manager at Alibaba. She joined NVIDIA’s Compute Architecture group in 2006 after finishing her Ph.D. at University of California, Riverside. After a 10-year stint in NVIDIA she joined Alibaba AI applied architecture group in 2017 where she focused on heterogeneous infrastructures to accelerate AI applications and improve hardware utilization.

Presentations

HARP: An Efficient and Elastic GPU-Sharing System Session

We developed an efficient and elastic GPU-sharing system for users who do research and development with TensorFlow.

Al Kari is CEO and principal consultant at Manceps, where he leads the company’s mission to augment human capabilities with machine intelligence, with a focus on blending machine learning and artificial intelligence with cloud computing and big data technologies. Al is a Google Developer Expert (GDE) in machine learning, organizer of the TensorFlow-Northwest and OpenStack-Northwest user groups. Previously, Al was a global cloud evangelist at Microsoft, where he helped top-tier ISV partners onboard on the Microsoft Azure Platform. Al started his career in the mid-’90s as a software architect by founding Softwarehouse overseas before moving to the United States. He later held product and services leadership roles at Dell, where he helped build the company’s virtualization and cloud computing services portfolio; cofounded DetaCloud, an OpenStack engineering powerhouse; and was a principal cloud architect at Red Hat, where he was responsible for helping customers build enterprise-ready cloud infrastructure. A frequent speaker at major industry conventions, Al has been an outspoken advocate for building the future of open artificial intelligence and cloud technologies in support of academic, industrial, and scientific development. He is a standing member of the Cloud Advisory Council, the Linux Professional Institute, and the OpenStack Foundation.

Presentations

Don’t Beat the Market, Beat the Bots: Adversarial Networks in Finance Session

Automated investing has brought an immense amount of stability to the market, but it has also brought predictability; can an adversarial network game the behavior of automated investors by learning the patterns in market activity to which they are most vulnerable?

Meenakshi is a product manager for Cisco Container Platform, an enterprise grade Kubernetes offering that supports GPU/Kubeflow for hybrid AI/ML workloads. Meenakshi has interest in AI/ML space and is excited how the technology can enhance human well being and productivity.

Presentations

Hyperparameter tuning for TensorFlow using Katib/Kubeflow Tutorial

This is a tutorial that demostrates how to automate hyperparameter tuning for a given data set using Katib/Kubeflow

Valliappa Lakshmanan is Tech Lead at Google Cloud focusing on Data and Machine Learning. He is the author of “Data Science on GCP” (O’Reilly), co-author of “BigQuery: the definitive guide” (O’Reilly) and multiple Coursera courses.

Presentations

Machine Learning with SQL and TensorFlow on Google Cloud Platform 2-Day Training

An introduction to designing and building machine learning models on Google Cloud Platform, and how to deploy them into production. We'll walk through the process of building a complete machine learning pipeline, from ingest and exploration to training, evaluation, deployment, and prediction.

Garrett Lander is a Machine Learning Architect at Manceps, an ML consulting agency based out of Portland, OR. Garrett works with clients ranging from those taking their first steps into automation, to seasoned ML practitioners looking to optimize their production models. Garrett is especially interested in the growing areas of AI pen-tests and ethicality, and the effort to build models that improve on human decision-making without inheriting its shortcomings.

Presentations

Don’t Beat the Market, Beat the Bots: Adversarial Networks in Finance Session

Automated investing has brought an immense amount of stability to the market, but it has also brought predictability; can an adversarial network game the behavior of automated investors by learning the patterns in market activity to which they are most vulnerable?

Vitaly Lavrukhin is a Senior Applied Research Scientist at NVIDIA working on deep learning algorithms for speech and language technologies. Before that, he conducted research to solve computer vision problems with deep learning methods at Samsung R&D Institute Russia.

Presentations

Speech Recognition with OpenSeq2Seq Session

OpenSeq2Seq, a TensorFlow-based toolkit, provides a large set of state-of-the-art models and building blocks for automatic speech recognition (Jasper, Wav2Letter, DeepSpeech2), speech synthesis (Centaur, Tacotron2), and natural language processing. We'll focus on large vocabulary speech recognition and speech command recognition tasks and show how to solve these problems with OpenSeq2Seq.

Joohoon Lee is a principal product manager for AI inference software at NVIDIA. He previously led the automotive deep learning software solutions team focusing on the production deployment of neural networks in DRIVE AGX platform using TensorRT. His expertise includes quantization, sparsity optimization, compilers, GPU and AI accelerator architecture design. Joohoon received his B.S. and M.S. in electrical and computer engineering from Carnegie Mellon University.

Presentations

Faster Inference in TensorFlow 2.0 with TensorRT Session

In this talk we will use examples to show how to optimize an app using TensorRT with the new Keras APIs in TensorFlow 2.0. We will also cover show tips and tricks to get highest performance possible on GPUs and examples of debugging/ profiling tools by NVIDIA and TensorFlow.

Jeremy Lewi is a cofounder and lead engineer for the Kubeflow project at Google—an effort to help developers and enterprises deploy and use ML cloud natively everywhere. He’s been building on Kubernetes since its inception, starting with Dataflow and then moving onto Cloud ML Engine and now Kubeflow.

Presentations

Automating Your Developer Workflow on GitHub with Tensorflow Session

A tutorial on how to use a freely available, natural language dataset to build practical applications for anyone who writes software, with Tensorflow.

Jason (Jing Yao) Li is a Deep Learning Software Engineer on the AI Applications team at NVIDIA. He has completed his BASc and MScAC at the University of Toronto working with Roger Grosse and Jimmy Ba. His current research focus is on sequence to sequence models and speech; specifically in the domains of speech synthesis and speech recognition.

Presentations

Speech Recognition with OpenSeq2Seq Session

OpenSeq2Seq, a TensorFlow-based toolkit, provides a large set of state-of-the-art models and building blocks for automatic speech recognition (Jasper, Wav2Letter, DeepSpeech2), speech synthesis (Centaur, Tacotron2), and natural language processing. We'll focus on large vocabulary speech recognition and speech command recognition tasks and show how to solve these problems with OpenSeq2Seq.

Tommy Li is a software developer at IBM focusing on cloud, container, and infrastructure technology. He has worked on various developer journeys, which provide use cases on cloud-computing solutions, such as Kubernetes, microservices, and hybrid cloud deployments. He is passionate about machine learning and big data.

Presentations

Running TFX end to end in Hybrid Clouds leveraging Kubeflow Pipelines Session

In this talk we are going to demonstrate how to run TFX in Hybrid Cloud environments

Dr. Chao Liu is a software developer of AMD’s open source high performance deep learning library MIOpen. His interests include development of parallel algorithms and numerical methods for a variety of applications, including deep learning and physics based simulation. Prior to joining AMD, Chao Liu had been developing techniques for Computational Fluid Dynamics, Finite Element Analysis, iterative solvers and mesh generations on shared and distributed-memory machines.

Presentations

Modular convolution considered beneficial Session

We explore breaking convolution algorithms into modular pieces to be better fused with graph compilers such as XLA.

Zac Yung-Chun Liu specializes in machine learning, artificial intelligence applications, remote sensing imagery processing, and geospatial analysis. His deep learning work includes computer vision, image classification, segmentation, object detection, and natural language processing, related to disease ecology and shark conservation.

Presentations

Building Deep Learning Applications using TensorFlow to Combat Schistosomiasis Session

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 250 million people worldwide; in this session, we will show how we build and deploy deep learning applications to detect disease transmission hotspots, make disease control interventions more efficient and scalable, and help local governments and stakeholders make data-driven decisions.

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 and Edd Wilder-James open the second day of keynotes.

Wednesday opening welcome Keynote

Program Chairs, Edd Wilder-James and Ben Lorica open the first day of keynotes.

Daniel Lowell is the team lead and software architect for MIOpen, AMD’s the deep learning GPU kernels library. Previously he has worked at AMD Research in the HPC arena, in compiler technology and reliability. His interests include deep learning, brain-machine interfaces, auto-code generation, and high performance computing.

Presentations

Modular convolution considered beneficial Session

We explore breaking convolution algorithms into modular pieces to be better fused with graph compilers such as XLA.

Nathan joined NVIDIA in 2015 where he works to accelerate deep learning frameworks. His background is in theoretical chemistry. He holds a doctoral degree from Stanford University where he worked to accelerate electronic structure calculations on GPUs.

Presentations

Accelerating Training, Inference and ML Applications on NVIDIA GPUs Tutorial

We share software components from NVIDIA’s software stack to get the best out of your end to end AI applications on modern NVIDIA GPUs. In addition we share features and tips tricks to optimize your workloads right from data loading, processing, training, inference and then deployment.

Maxim Lukiyanov is a principle program manager on the Azure HDInsight team. He works Spark and HBase.

Presentations

How Azure Machine Learning makes the Data Scientist’s life easier 2-Day Training

In this 2-day training, we will show how AzureML helps the data scientist to be more productive when working through the process of developing TensorFlow models for production. We will show participants aspects across the whole model development lifecycle from training, through deployment, MLOps, and all the way to model interpretability.

Pete MacKinnon is a Principal Software Engineer in the AI Center of Excellence at Red Hat. He is actively involved in the open source Kubeflow project to bring TensorFlow machine learning workloads to container environments (Kubernetes and OpenShift)

Presentations

Running TFX end to end in Hybrid Clouds leveraging Kubeflow Pipelines Session

In this talk we are going to demonstrate how to run TFX in Hybrid Cloud environments

Jason is a research scientist at Dropout Labs, the founder of Cleveland AI, and an active member of the AI Village at DEFCON and of the OpenMined community. He works on novel methods making machine learning more performant for privacy-preserving techniques, most notably by contributing to the TF Encrypted project. He has worked on a variety of safety and security problems, including safe reinforcement learning, secure and verifiable agent auditing, and neural network robustness. His previous work with the Cleveland Clinic established a state-of-the-art blood test classification and demonstrated that machine learning can virtually eliminate the problem of medical malpractice due to contaminated blood samples.

Presentations

Privacy-Preserving Machine Learning with TensorFlow and TF Encrypted Tutorial

Build and serve privacy-preserving machine learning models using TF Encrypted and the TensorFlow ecosystem.

ML Engineer

Presentations

Reliable, High Scale Tensorflow Inference Pipelines at Twitter Session

Twitter heavily relies on Scala/JVM and has a lot of expertise knowledge. To provide a reliable Tensorflow inference offering to the Twitter customer teams, we’ve had to overcome new problems along the way to make our offering reliable. In this presentation, we'll present our key learnings.

Omoju Miller is a Machine Learning Engineer with Github. In the past, she has co-led the non-profit investment in Computer Science Education for Google and served as a volunteer advisor to the Obama administration’s White House Presidential Innovation Fellows. She is a member of the World Economic Forum Expert Network in AI.

Presentations

Automating Your Developer Workflow on GitHub with Tensorflow Session

A tutorial on how to use a freely available, natural language dataset to build practical applications for anyone who writes software, with Tensorflow.

Piero Molino is a Senior Research Scientist at Uber AI. He works on natural language understanding and conversational AI. He is a co-founder of Uber AI.

Presentations

Enhance Recommendations in Uber Eats with Graph Convolutional Networks Session

In this talk, we show how to generate better restaurant and dish recommendations in UberEats by learning entity embeddings using Graph Convolutional Networks implemented in Tensorflow

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

Presentations

Zero to ML Hero with TensorFlow 2.0 Tutorial

Take the users from first principles in programming for ML all the way through to doing image classification with Convolutional Neural Networks

Neelima Mukiri is a Principal Engineer in Cisco’s Cloud Platform Solutions group working on the architecture and development of Cisco’s Container Platform. Prior to this she worked on core virtualization layer at VMware and systems software in Samsung Electronics.

Presentations

Hyperparameter tuning for TensorFlow using Katib/Kubeflow Tutorial

This is a tutorial that demostrates how to automate hyperparameter tuning for a given data set using Katib/Kubeflow

Robby Neale is a senior software engineer who joined Google in 2011. He currently leads the TF.Text effort on the NLX Infrastructure team, focusing on expanding the capabilities of the Tensorflow platform to make creation of text-based models easier for developers.

Presentations

Building models with tf.text Session

There are many resources for building models from numeric data, which meant processing text had to occur outside the model.

Ton Ngo is a senior software developer in the IBM Cognitive OpenTech Group at the IBM Silicon Valley Lab. Previously, he was with the IBM Research Lab at Yorktown and Almaden. He has been active in the open source community for four years and is currently working on TensorFlow and deep learning. He was a core contributor in OpenStack for Magnum and Heat-Translator, focusing on the networking and storage support for container orchestrator such as Kubernetes. Ton frequently gives talks and programming tutorials on TensorFlow in San Francisco, Seattle, and New York and at OpenStack Summits worldwide. He has published papers on a wide range of subjects.

Presentations

Building Deep Learning Applications using TensorFlow to Combat Schistosomiasis Session

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 250 million people worldwide; in this session, we will show how we build and deploy deep learning applications to detect disease transmission hotspots, make disease control interventions more efficient and scalable, and help local governments and stakeholders make data-driven decisions.

I’m an AI leader passionate about deploying AI into production through rapid software development and creative thinking. My latest interests include working at the interface of DevOps and Data Science, to provide SaaS solutions based on Bayesian methods for machine learning, system engineering and operations research applied to Big Data. My teams are responsible for delivering process and network models, large scale ML models and predictive analytics, employing probabilistic graph models, causal inference and Bayesian Deep Learning into our CI/CD pipeline. I have fostered a culture to support and advocate for the deployment of large scale GPU ML pipelines on cloud infrastructure.

Presentations

Scaling Industrial AI Model Building using Tensorflow Probability and Kubeflow Pipelines Session

We will showcase the development and deployment of large scale system-of-systems probabilistic models, with evolutionary architecture search, using tensorflow probability and kubeflow pipelines for predicting complex events and phenomena, applied to anomaly detection and predictive maintenance in large scale industrial systems.

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

Presentations

Swift for TensorFlow in 3 hours Tutorial

Isn't Swift just for app developers? No! Swift for TensorFlow provides the power of TensorFlow with all the advantages of Python (and complete access to Python libraries, as needed) and all the advantages of Swift, the safe, fast, incredibly capable open source programming language; Swift for TensorFlow is the perfect way to learn both deep learning and Swift.

Babusi Nyoni is a Zimbabwean innovator focused on the uses of Artificial Intelligence on the African continent. In 2016 he created what Forbes magazine described as “the world’s first AI football commentator” for the UEFA Champions League final. In the same year he created a prototype for the prediction of human displacement in Africa using AI & thereafter worked with UNHCR Innovation to actualise a pilot project in the same field.
He founded the Ulwazi Accelerator in 2018 to equip young Zimbabweans with the skills needed to contribute to the global digital economy. In 2019 he created an app for the early diagnosis of Parkinson’s disease & presented his findings at Oxford University on the Skoll World Forum stage.
Babusi has a strong passion for fresh new ideas that will change the lives of those around him and is a firm believer that AI is shaping the technological zeitgeist worldwide.

Presentations

From dance to diagnosis: How Tensorflow.js is shaping AI in Africa Session

In 2018 we created a dance app that used Tensorflow.js-powered pose estimation to rate a popular South African dance known as 'iVosho" on mobile phones. In this session I will unpack the possibilities for AI in disadvantaged African communities and explain how and why we turned this dance app into a tool to diagnose Parkinson's disease.

Krzys Ostrowski is a Research Scientist at Google AI, currently
focusing on developing programming abstractions for machine learning in large-scale distributed environments. He received his Ph.D. in Computer Science from Cornell University in the area of Distributed Systems and Programming Languages.

Presentations

A journey into the world of federated learning with TensorFlow Federated Session

We introduce federated learning (FL) - an approach to machine learning where a shared model is trained across many clients that keep their training data locally, including a hands-on with FL using TensorFlow Federated (TFF): we'll demonstrate step by step how to train your TensorFlow model in a federated environment, implement custom federated computations, and setup large simulations.

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

Presentations

AI for Managers 2-Day Training

This course is a non-technical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls to avoid. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

He is Senior Malware Scientist within Trend Micro’s Machine Learning Group, an elite team of researchers solving highly difficult problems in the battle against cybercrime. His main research focus is deep learning based threat detection including generative adversarial malware clustering, metamorphic malware detection using semantic hashing and Fourier transform, malicious URL detection with attention mechanism, OS X malware outbreak detection, semantic malicious script autoencoder, and heterogeneous neural network for Android APK detection. He previously worked for Kaspersky, FireEye, Symantec, and Sophos. He also created a critical security system for banking malware at a top Australian bank.

Presentations

Generative Malware Outbreak Detection Session

This talk demonstrates how to detect in-the-wild malware samples with a single training sample of a kind with the help of tensorflow's flexible architecture in implementing a novel variable length generative adversarial autoencoder.

Aalok Patwa: he/him

Aalok is a sophomore at Archbishop Mitty High School interesting in machine learning and healthcare. He has done several research projects in the past which have won awards at the regional, state, and national level. He is also committed to outreach, imparting his knowledge about computer science and medicine to the broader public. He is President of Computer Programming Club at his high school and an avid participant in Speech and Debate.

Awards and Recognitions:

Synopsys Science Fair 1st Place Category Award – 2019
Synopsys Science Fair 1st Place Category Award – 2018
Broadcom MASTERS Science Fair National Finalist – 2016
California State Science Fair Raytheon Achievement Award – 2016
Speech and Debate National Qualifier

Presentations

TensorFlow and Medicine: Using Deep Learning for Real-Time Segmentation of Colon Polyps Session

I will describe how to use TensorFlow to create a deep learning model that detects, localizes, and segments colon polyps from colonoscopy image and video, thus giving the audience technical knowledge of TensorFlow and Keras as well as ideas for the application of TensorFlow in medicine.

After a masters in Astrophysics from UCL, Laxmi has held several technical roles in industry. She is currently at Datatonic as a Data Scientist, with involvement in end-to-end project delivery including stakeholder management, data exploration, machine learning, algorithm design, automation and productionization of solutions on Google Cloud.
She is at her happiest when learning new things and challenging herself. Laxmi is always looking to expand her knowledge and apply it practically, especially in the fields of machine learning and engineering.
Outside of work, she enjoys exploring new cuisines or finding a book to get lost in.

Presentations

Effective sampling methods within TensorFlow input functions Session

A demonstration of sampling techniques applied to training / testing data directly inside the input function using the tf.data API.

Victoria has over a decade of experience in the semiconductor space. She currently heads up Strategic Partnerships at Graphcore, working with key customers and leading Research & Universities AI engagements. Previously she held several leadership positions at NVIDIA from global alliances, product marketing and campaigns to the founding of the GPU Technology Conference. Prior to joining NVIDIA, Victoria worked in the hedge fund space, as Executive Director for the Hedge Fund Business Operations Association. Victoria is a frequent contributor to ACM SIGGRAPH and is AR, MR & VR Chair for the SIGGRAPH 2019 Conference. She’s also an active member of the Consumer Technology Association’s AI Working Group.

Presentations

Targeting High-Performance ML Accelerators Using XLA Session

This software focused talk will cover optimization for new accelerators using TensorFlow and the XLA.

Taylor is a member of the TensorFlow high level APIs team focusing on performance with a particular emphasis on out-of-the-box performance of Keras. Prior to that he was a maintainer of the TensorFlow official models repository and optimized several of the Google MLPerf submissions.

Presentations

Scaling TensorFlow using tf.distribute Session

Come learn about how you can use tf.distribute to scale your machine learning model on a variety of hardware platforms ranging from commercial cloud platforms to dedicated hardware. We will have a dedicated section talking about tools and tips to get the best scaling for your training in TensorFlow.

Josh Romero is a developer technology engineer at NVIDIA. He has extensive experience in GPU computing, from porting and optimizing HPC applications to more recent work with deep learning. Before joining NVIDIA, Josh received his Ph.D. from Stanford University, where his research focused on developing new computational fluid dynamics methods to better exploit GPU hardware.

Presentations

Accelerating Training, Inference and ML Applications on NVIDIA GPUs Tutorial

We share software components from NVIDIA’s software stack to get the best out of your end to end AI applications on modern NVIDIA GPUs. In addition we share features and tips tricks to optimize your workloads right from data loading, processing, training, inference and then deployment.

Brennan Saeta is a software engineer on the Google Brain team leading the Swift for TensorFlow project. Previously, he was the TensorFlow tech lead for Cloud TPUs.

Presentations

Swift for TensorFlow Session

Swift for TensorFlow is a next generation machine learning platform that leverages innovations like first-class differentiable programming to seamlessly integrate deep neural networks with traditional AI algorithms and general purpose software development.

Mehrnoosh Sameki is a technical program manager at Microsoft responsible for leading the product efforts on machine learning interpretability within the Azure Machine Learning platform. Previously, she was a data scientist at Rue Gilt Groupe, incorporating data science and machine learning in retail space to drive revenue and enhance personalized shopping experiences of customers. She earned her PhD degree in computer science at Boston University.

Presentations

How Azure Machine Learning makes the Data Scientist’s life easier 2-Day Training

In this 2-day training, we will show how AzureML helps the data scientist to be more productive when working through the process of developing TensorFlow models for production. We will show participants aspects across the whole model development lifecycle from training, through deployment, MLOps, and all the way to model interpretability.

Kaz Sato is a staff developer advocate on the Cloud Platform team at Google, where he leads the developer advocacy team for machine learning and data analytics products such as TensorFlow, the Vision API, and BigQuery. Kaz has been leading and supporting developer communities for Google Cloud for over seven years. He is a frequent speaker at conferences, including Google I/O 2016, Hadoop Summit 2016 San Jose, Strata + Hadoop World 2016, and Google Next 2015 NYC and Tel Aviv, and has hosted FPGA meetups since 2013.

Presentations

AutoML Vision and Edge TPU: bringing TensorFlow Lite models to edge devices Session

AutoML Vision allows you to upload the images with labels, press a Train button and wait for a day to get a image recognition model with the state of the art accuracy. Without any ML expertise, you can easily train the image recognition model at the Cloud, exporting the TensorFlow Lite model, and use it at mobile devices, RasPi and Edge TPU with super low latency and power consumption.

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

Presentations

Introduction to TensorFlow 2-Day Training

The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. This training will introduce TensorFlow's capabilities in Python. It will move from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.

Siddharth Sharma is a Senior Technical Marketing Manager for Accelerated Computing at NVIDIA. Before joining NVIDIA, Siddharth was a product marketing manager for Simulink and Stateflow at Mathworks, working closely with automotive and aerospace companies to adopt model-based designs for creating control software.

Presentations

Faster Inference in TensorFlow 2.0 with TensorRT Session

In this talk we will use examples to show how to optimize an app using TensorRT with the new Keras APIs in TensorFlow 2.0. We will also cover show tips and tricks to get highest performance possible on GPUs and examples of debugging/ profiling tools by NVIDIA and TensorFlow.

Animesh Singh is an STSM and Program Director, working leading IBM AI OSS strategy working with IBM Watson and Cloud Platform. He leads machine learning and deep learning initiatives and works with communities and customers to design and implement deep learning, machine learning, and cloud computing frameworks. He has a proven track record of driving design and implementation of private and public cloud solutions from concept to production. In his decade-plus at IBM, Animesh has worked on cutting-edge projects for IBM enterprise customers in the telco, banking, and healthcare Industries, particularly focusing on cloud and virtualization technologies, and led the design and development first IBM public cloud offering.

Presentations

Running TFX end to end in Hybrid Clouds leveraging Kubeflow Pipelines Session

In this talk we are going to demonstrate how to run TFX in Hybrid Cloud environments

Daniel Situnayake leads developer advocacy for TensorFlow Lite at Google. He co-founded Tiny Farms, the first US company using automation to produce insect protein at industrial scale. He began his career lecturing in automatic identification and data capture at Birmingham City University.

Presentations

TensorFlow Lite: Solution for running ML on-device Session

TensorFlow Lite is TensorFlow’s lightweight cross-platform solution for mobile and embedded devices.

Dr. Sokolow studies basic and applied research at the interface of disease ecology, health, and development. Her current research program seeks natural solutions to modern health and environmental problems plaguing the developing world. She currently holds a joint research associate appointment at Stanford University and UC Santa Barbara. In addition, she is Executive Director of the newly founded Center for Disease Ecology, Health and Development at Stanford University and is co-founder and an Executive Board Member of The Upstream Alliance: an initiative joining partners across the globe in research for schistosomiasis reduction.

Presentations

Building Deep Learning Applications using TensorFlow to Combat Schistosomiasis Session

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 250 million people worldwide; in this session, we will show how we build and deploy deep learning applications to detect disease transmission hotspots, make disease control interventions more efficient and scalable, and help local governments and stakeholders make data-driven decisions.

Zak is the Product Manager for Cloud TPUs on the Google Brain team and the founder of the TensorFlow Research Cloud (TFRC). He is interested in making hardware acceleration for machine learning universally accessible and useful. Prior to joining Google, Zak earned a PhD in Computer Vision and founded a mobile-focused deep learning startup that was acquired by Apple. While at Apple, Zak contributed to the privacy-preserving on-device face identification technology in iOS 10 and macOS Sierra that was announced at WWDC 2016.

Presentations

Great TensorFlow Research Cloud projects from around the world (and how to start your own) Session

See how researchers all over the world are expanding the frontiers of ML using free Cloud TPU capacity from the TensorFlow Research Cloud.

Arun Subramaniyan leads the global data science & analytics team in BHGE Digital. His team developsdeep learning augmented domain analytics forall aspects of the Oil &Gas industry. He joined BHGE Digital from GE Global Research Center in Niskayuna, NY where he led the development of the Digital Twin framework. The framework has enabled several thousand engineers to build advanced models efficiently. The asset specific cumulative damage modeling techniques his team pioneered have saved millions of dollars for the several businesses. As a Six Sigma Master Black Belt, he developed advanced techniques and tools for efficiently modeling large scale systems like jet engines and accelerated design times by 3-4X. Arun is a prolific researcher with a Ph.D. in Aerospace Engineering from Purdue University with over 50 international publications that have been cited more than 600 times. He is a recipient of the Hull Award from GE which honors technologists for their outstanding technical impact

Presentations

Scaling Industrial AI Model Building using Tensorflow Probability and Kubeflow Pipelines Session

We will showcase the development and deployment of large scale system-of-systems probabilistic models, with evolutionary architecture search, using tensorflow probability and kubeflow pipelines for predicting complex events and phenomena, applied to anomaly detection and predictive maintenance in large scale industrial systems.

A year ago, Mikhail knew nothing about machine learning, object detection, or the physical challenges blind people face. With a strong will, he set out to learn about AI and make an impact in the world. Over the course of just 8 months, he researched and developed a prototype device to assist the blind with their navigational challenges.

His endeavours show how machine learning technologies can impact the future and his experiences at just the age of 16 are a great example of how we live in a world where new powerful technologies can be leveraged by anyone and even teenagers can make a difference.

Presentations

How Machine Learning can Empower a 16-year-old to make Crossing the Street Safer Session

At just the age of 16, with his experience of using TensorFlow to develop object detection models to assist the visually impaired, Mikhail Szugalew will be highlighting his learning points, struggles, process, key takeaways, and vision for a better future in this presentation about his journey and endeavours.

KC Tung is an AI architect in Microsoft. He also has experience as a cloud architect, machine learning (ML) engineer, data scientist with hands-on experience and success in development and serving of artificial intelligence (AI), deep learning, computer vision, and natural language processing (NLP) models in many enterprise use-case driven architectures, using open source machine learning libraries such as Tensorflow, Keras, PyTorch, H2O.

His specialties are:
*AI/ML: end-to-end model and data structure design, testing, and serving in cloud or on-prem.
*Technical core: Design of experiment, hypothesis development, reference architecture for AI/ML in cloud centric implementation.

KC holds a PhD in Molecular Biophysics from The University of Texas Southwestern Medical Center in Dallas, TX.

Presentations

A Novel Solution for Data Augmentation and Bias Problem in NLP using Tensorflow Session

Come and see how I use Tensorflow to discover a better way to solve NLP model bias problem with data augmentation for an enterprise customer (one of the largest airlines in the world); you will also learn how I leverage hidden gems in tf.data and the new API to easily find a novel use for text generation and how it surprisingly improved my NLP model.

Pete Warden is the technical lead of the mobile and embedded TensorFlow Group on Google’s Brain team.

Presentations

TensorFlow Lite: Solution for running ML on-device Session

TensorFlow Lite is TensorFlow’s lightweight cross-platform solution for mobile and embedded devices.

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

Presentations

Getting Involved In The TensorFlow Community Session

Learn how you can be a part of the growing TensorFlow ecosystem, and become a contributor through code, documentation, education and community leadership.

Thursday opening welcome Keynote

Program Chairs, Ben Lorica and Edd Wilder-James open the second day of keynotes.

Wednesday opening welcome Keynote

Program Chairs, Edd Wilder-James and Ben Lorica open the first day of keynotes.

Sam is a Google Developer Expert for Machine Learning. He has extensive experience in startups and mobile applications and is helping developers and companies create smarter applications with machine learning. He is especially passionate about Deep Learning and AI in the fields of Natural Language and conversational agents. Sam regularly shares his knowledge at events and trainings across Asia and is co-organiser of the Singapore TensorFlow and Deep Learning group.

Presentations

TensorFlow and TPUs in the Real World: Converting Deep Learning projects to train faster Session

Tips and Tricks to take advantage of TPUs in TensorFlow 2.0 and to take a current Deep Learning project and convert it to something that will run smoothly and quickly on Cloud TPUs

Li Xu is currently a software engineer at Twitter Health Machine Learning Team, working on the development of machine learning technologies for health, security and privacy. Before joining Twitter, he was a software engineer at Security Machine Learning Platform team at Uber Technologies, working on the architecture development of Machine Learning Platform for security at Uber. Before that, he was a Researcher at Yahoo Research/Labs, conduct state-of-the-art research on Security, Privacy and Machine Learning. Dr. Xu has shipped many inventions and technologies to Yahoo, Uber and Twitter products. Nowadays, more than a billion users are using these products. His current research interests lie in the security and machine learning. He has authored or co-authored papers in top ranked journals, conferences, book chapter and U.S. patents. He served as a Program Committee member for top conferences of security, AI and big data.

Presentations

Improving the Health of Public Conversations on Twitter with TensorFlow Session

When people discuss on Twitter we want to ensure that they can have respectful conversations with genuine people. We rely on Machine Learning to improve the health of public conversations and information integrity. This presentation describes how Twitter uses TensorFlow to detect abusive, toxic, and spammy content, and promote healthy conversations on the platform.

Maggie Zhang joined NVIDIA in 2017 and she is working on deep learning frameworks. She got her PhD in Computer Science & Engineering from the University of New South Wales in Australia. Her research background includes GPU/CPU heterogeneous computing, compiler optimization, computer architecture, and deep learning.

Presentations

Accelerating Training, Inference and ML Applications on NVIDIA GPUs Tutorial

We share software components from NVIDIA’s software stack to get the best out of your end to end AI applications on modern NVIDIA GPUs. In addition we share features and tips tricks to optimize your workloads right from data loading, processing, training, inference and then deployment.

Yi Zhuang is a machine learning software engineer at Twitter Cortex, where he tech leads a group of people to build a platform for working with ML models. Currently, he works on uniting ML practitioners around a single ML platform, bringing consistency to ML practices at Twitter. Previously, Yi led a group of people to develop a trillion-document scale distributed search engine at Twitter. Yi holds an MS in computer science from Carnegie Mellon University. He loves cats and enjoys pondering over all things technical and logical.

Presentations

Improving the Health of Public Conversations on Twitter with TensorFlow Session

When people discuss on Twitter we want to ensure that they can have respectful conversations with genuine people. We rely on Machine Learning to improve the health of public conversations and information integrity. This presentation describes how Twitter uses TensorFlow to detect abusive, toxic, and spammy content, and promote healthy conversations on the platform.

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