Spark + AI Summit is the premier global event for the data and machine learning community to discuss the latest advances in open-source technologies such as Apache Spark™, Delta Lake, MLflow, Koalas and TensorFlow as well as best practices for deploying AI in the real world.
In addition to over 100 exciting breakout sessions, this year’s Spark + AI Summit in Amsterdam 15-17 October will feature keynotes from some of the leading thinkers and innovators in AI. In this blog, we’ll highlight a few of the esteemed keynote speakers, including the original creators of Spark, Delta Lake, and researchers from leading academic institutions.
Keynotes You Won’t Want to Miss!
Developing ML Algorithms to Image Black Holes
Computing and Mathematical Sciences, Caltech
We’re pleased to announce Katie Bouman’s keynote address at this year’s Spark + AI Summit Europe. Katie was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics, and she received her Ph.D. from MIT’s Computer Science and Artificial Intelligence Laboratory in EECS. Currently, she specializes in using emerging computational methods to push the boundaries of imaging. In her keynote address, Katie will discuss how she developed an ML algorithm to capture the first-ever picture of a black hole.
Building AI to Beat Professional Starcraft Players
We’re excited to welcome Oriol Vinyals to this year’s Spark + AI Summit Europe. Oriol holds a Ph.D. in EECS from UC, Berkeley, and he received the 2016 MIT TR35 innovator award. In his keynote address, AlphaStar: Mastering the Real-Time Strategy Game StarCraft II with AI, Oriol will discuss AlphaStar, the first AI program to defeat a top professional StarCraft player under professional match conditions. Games are critically important in testing and evaluating AI systems, and StarCraft has emerged as a “grand challenge” for AI research.
Insights from Matei Zaharia, the Original Creator of Apache Spark and MLflow
CTO, Databricks and Assistant Professor of Computer Science
Matei Zaharia will be joining us at this year’s summit to share his latest insights on streamlining the end-to-end machine learning lifecycle. Matei initiated the Apache Spark project in 2009, while working on his Ph.D. at UC, Berkeley, and he has also worked on datacenter systems, co-creating the Apache Mesos project and contributing as a committer on Apache Hadoop. He is currently an Assistant Professor of Computer Science at Stanford University and Chief Technologist at Databricks, where he heads the MLflow development effort. Matei’s research work has been recognized by ACM, with its 2014 Doctoral Dissertation Award for the best computer science Ph.D. dissertation, along with receiving an NSF CAREER Award and several best paper awards.
Democratizing Machine Learning: Perspectives from a scikit-learn Creator
Brain Imaging Research
Gaël Varoquaux is an Inria faculty researcher, specializing in data science and brain imaging. He holds a joint position at Inria (French Computer Science National research) and the Neurospin Brain Research Institute. Gaël’s research focuses on using data and machine learning for scientific inference, applying it to brain-imaging data to understand cognition, and developing tools that simplify the use of machine learning for non-specialists. During his keynote, Gaël will share insights from his research on how to democratize machine learning for the masses. He has long dreamed of making bleeding-edge data processing available to new fields, and he is working on a master plan to build easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi, and nilearn, and a nominated member of the PSF.
Delta Lake Open Source Community Momentum
Principal Software Engineer
Michael Armbrust is committer and PMC member of Apache Spark and the original creator of Spark SQL and Delta Lake. He currently leads the team at Databricks that designed and built Structured Streaming and Delta Lake. During his keynote, he will share insights from leading these projects. He received his PhD from UC Berkeley in 2013, and was advised by Michael Franklin, David Patterson, and Armando Fox. His thesis focused on building systems that allow developers to rapidly build scalable interactive applications, and specifically defined the notion of scale independence. His interests broadly include distributed systems, large-scale structured storage and query optimization.
Find out more
Learn more about Spark+AI Summit, including descriptions of breakout sessions and a complete schedule.