Scalable machine learning, scalable Python, for everyone
May 27 – 28, 2020
Grand Hyatt San Francisco
San Francisco, CA
Ray Summit is about Ray, the open-source Python framework for building distributed applications that run at any scale. This inaugural, two-day conference showcases Ray best practices, real-world case studies, and the latest research in AI and other scalable systems built on Ray. It brings together the growing Ray community interested in building scalable AI applications used in ecommerce, media, logistics and transportation, finance, IoT, and more.
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley.
His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
Azalia Mirhoseini is a Senior Research Scientist at Google Brain and an Advisor at Cmorq. She is the co-founder/lead of the Machine Learning for Systems Moonshot at Brain where they focus on deep reinforcement learning based approaches to solve problems in computer systems and metalearning. You can find her most recent publications in her Google Scholar page. Azalia sometimes tweets about her work. She has a Ph.D. in Electrical and Computer Engineering from Rice University. She has received a number of awards, including the MIT Technology Review 35 Under 35 Award, the Best Ph.D. Thesis Award at Rice University and a Gold Medal in the National Math Olympiad in Iran.
Since 2007, Wes McKinney has been developing data analysis software, mostly for use in the Python programming language. His primary objective has been improving user productivity, increasing performance and efficiency, and enhancing data interoperability. He is best known for creating the pandas project and writing the book Python for Data Analysis. Since 2015, Wes has been focused on the Apache Arrow project. He has also contributed to Apache Kudu (incubating) and Apache Parquet (where he is a PMC member). He was the co-founder and CEO of DataPad. He later spent a couple years leading efforts to bring Python and Hadoop together at Cloudera. In 2018, he founded Ursa Labs, a not-for-profit open source development group in partnership with RStudio. In 2018, he became a Member of The Apache Software Foundation
Manuela M. Veloso is the Head of J.P. Morgan AI Research, which pursues fundamental research in areas of core relevance to financial services, including data mining and cryptography, machine learning, explainability, and human-AI interaction. J.P. Morgan AI Research partners with applied data analytics teams across the firm as well as with leading academic institutions globally.
Professor Veloso is on leave from Carnegie Mellon University as the Herbert A. Simon University Professor in the School of Computer Science, and the past Head of the Machine Learning Department. With her students, she had led research in AI, with a focus on robotics and machine learning, having concretely researched and developed a variety of autonomous robots, including teams of soccer robots, and mobile service robots. Her robot soccer teams have been RoboCup world champions several times, and the CoBot mobile robots have autonomously navigated for more than 1,000km in university buildings.
Professor Veloso is the Past President of AAAI, (the Association for the Advancement of Artificial Intelligence), and the co-founder, Trustee, and Past President of RoboCup. Professor Veloso has been recognized with a multiple honors, including being a Fellow of the ACM, IEEE, AAAS, and AAAI. She is the recipient of several best paper awards, the Einstein Chair of the Chinese Academy of Science, the ACM/SIGART Autonomous Agents Research Award, an NSF Career Award, and the Allen Newell Medal for Excellence in Research.
Professor Veloso earned a Bachelor and Master of Science degrees in Electrical and Computer Engineering from Instituto Superior Tecnico in Lisbon, Portugal, a Master of Arts in Computer Science from Boston University, and Master of Science and PhD in Computer Science from Carnegie Mellon University. See www.cs.cmu.edu/~mmv/Veloso.html for her scientific publications.
Gaël Varoquaux is a tenured research director at Inria. His research focuses on statistical-learning tools for data science and scientific inference. Since 2008, he has been exploring data-intensive approaches to understand brain function and mental health. He is one of the leaders of the scikit-learn project.
Ion Stoica is a Professor in the EECS Department at the University of California at Berkeley. He is currently the leader of RISELab. He does research on cloud computing and networked computer systems. Past work includes Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an ACM Fellow and has received numerous awards, including the SIGOPS Hall of Fame Award (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001). He is also a co-founder of Anyscale in 2019 to commercialize technologies for distributed Python especially for AI applications, a co-founder of Databricks in 2013 to commercialize technologies for Big Data processing, and a co-founder of Conviva Networks in 2006 to commercialize technologies for large scale video distribution.