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Ray Summit 2020

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Please note: All Sessions are in Pacific Daylight Time (PDT), UTC-7

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9:00am PDT

AI in Production - Key Challenges & Use Cases - Jonathan Berte, Robovision Virtual 4 Ray Internals: A Peek at `ray.get` - Stephanie Wang, Anyscale Virtual 1 AlphaDow: Reinforcement Learning for Industrial Production Scheduling - Adam Kelloway, Dow Chemical Virtual 2 State Management in Distributed Online Learning with Ray - Edmon Begoli, Oak Ridge National Laboratory Virtual 3

9:35am PDT

Distributed Black-Box Model Explanation with Ray - Alexandru Coca, Seldon Virtual 3 RLlib: Scalable RL for TensorFlow, PyTorch, and Beyond - Eric Liang, Anyscale Virtual 1 Project Bonsai - How Microsoft is Using Ray and RLlib Inside a Machine Teaching Service for Autonomous Systems - Edilmo Palencia, Microsoft Virtual 2 Cloudstate—Towards Stateful Serverless - Jonas Bonér & James Roper, Lightbend Virtual 4

10:10am PDT

Dask-on-Ray: Using Dask and Ray to Analyze Petabytes of Remote Sensing Data - Clark Zinzow, Descartes Labs Virtual 4 What's New with Ray Libraries: Tune - Richard Liaw, Anyscale Virtual 1 Data Scientists are More Valuable than Hardware: The Design of Modin - Devin Petersohn, Intel Virtual 3 Utilizing Reinforcement Learning Techniques for Supply Chain with Amazon SageMaker - Anna Luo, AWS Virtual 2

10:40am PDT

Stretching Break: Lower Back & Spine - Maryam Sharifzadeh, Office Yoga Virtual Break Virtual Sponsored Office Hours: Anyscale

11:10am PDT

Human-First AI: Because AI Needs The Human Eye - Christopher Nguyen, Arimo Virtual 4 Project Zouwu: Scalable AutoML for Telco Time Series Analysis using Ray and Analytics Zoo - Ding Ding, Intel Virtual 3 Introducing Ray Serve: Scalable and Programmable ML Serving Framework - Simon Mo, Anyscale Virtual 1 Connecting Reinforcement Learning to Simulation Software - Max Pumperla, Pathmind Virtual 2

11:45am PDT

Turbocharging State-of-the-art Natural Language Processing on Ray - David Talby, John Snow Labs Virtual 3 Adopting Ray @ LinkedIn - Jonathan Hung & Nitin Pasumarthy, LinkedIn Virtual 2 Debugging and Observability for Distributed Ray Applications - SangBin Cho, Anyscale Virtual 1

12:20pm PDT

Ray + Arize - Why you Need Model Observability - Aparna Dhinakaran, Arize AI Virtual 3 Advanced Ray RLlib: New Features and New Possibilities - Sven Mika, Anyscale Virtual 1 Information Gain Regulation in Reinforcement Learning with the Digital Twins' Level of Realism In RlLib - Géza Szabó, Ericsson Research & József Pető, Budapest University of Technology and Economics Virtual 2

12:50pm PDT

Afternoon Break Virtual

1:00pm PDT

BoF: Reinforcement Learning

1:45pm PDT

BoF: Ray

2:30pm PDT

BoF: Python Data

3:15pm PDT

BoF: Natural Learning Processing (NLP)

4:00pm PDT

Keynote: A Secure Collaborative Learning Platform - Raluca Ada Popa, Computer Security Professor, UC Berkeley Virtual

4:15pm PDT

Keynote: Every Five Decades: A New Field of Engineering - Michael I. Jordan, Distinguished Professor, University of California at Berkeley Virtual

4:30pm PDT

Keynote: AlphaStar: How Google Infrastructure Made it Possible - Oriol Vinyals, Principal Scientist, Google DeepMind Virtual

4:50pm PDT

Distributed Deep Learning with Horovod on Ray - Travis Addair, Uber Virtual 4 Building Complex Data Analytics Pipelines with Ray - Qingqing Mao, Dascena Virtual 2 Leveraging Ray to Enhance Machine Learning Models in Finance - James Dunworth-Crompton, Goldman Sachs Virtual 3 Using Ray On Large-scale Applications at Ant Group - Jiaying Zhou, Ant Group Virtual 1

5:25pm PDT

ML Engineering Kubeflow - Pavel Dournov, Google Cloud Virtual 2 Java API and Cross Language Programming on Ray - Hao Chen, Ant Group Virtual 1 Distributed Reinforcement Learning for Robotic Assembly - Rodger Luo, Autodesk Research Virtual 3