Systems and ML at RISELab

Abstract:
In this talk, I will present several of the projects we are developing at RISELab, a two-year old lab at UC Berkeley that focusses on building platforms and algorithms for real-time intelligent decisions, decisions that are secure and explainable. These projects include both systems to better support machine learning (ML) workloads, and leveraging ML to build better systems. In the first category, I will present, Ray, a general-purpose distributed system which provides both task-parallel and actor abstractions. Ray is highly scalable employing an in-memory storage system and a distributed scheduler. Ray already supports several popular libraries, including a reinforcement learning library (RLlib) and a hyperparameter search library (Tune), and it is deployed in production at tens of organizations. In the second category, I will present Autopandas, a system that synthesizes snippets of API calls from input-output examples for Pandas, the most popular data science library today, and NeuroCuts, a tool to generate decision trees that implement network packet classifiers. 

Bio:
Ion Stoica is a Professor in the EECS Department at University of California at Berkeley, and the Director of RISELab (https://rise.cs.berkeley.edu/). He is currently doing research on cloud computing and AI 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 Executive Chairman at Databricks, a company he co-founded in 2013 to commercialize Apache Spark. In 2006 he also co-founded Conviva, a startup to commercialize technologies for large scale video distribution.