Welcome

I am an Associate Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and founding co-director of the Data System and AI Lab (DSAIL) at MIT.

Currently, my research focuses on building systems for machine learning , and using machine learning for systems. For example, with our work on Learned Indexes we started to explore how we can enhance or even replace core systems components using machine learning models and early results suggest, that we are able to achieve significant performance improvements over state-of-the-art techniques and sometimes are even able to change the complexity class of certain algorithms. On the other hand, with Northstar we are exploring new user interfaces and infrastructure to democratize data science by enabling visual, interactive, and assisted data exploration and model building. One particular focus of our work is to not only make the model building process faster, but also safer by automatically preventing the user from common pitfalls.

Before joining MIT, I was an Assistant Professor at Brown University, spent time at Google Research, and was a PostDoc in the AMPLab at UC Berkeley after I got my PhD from ETH Zurich. I am a 2017 Alfred P. Sloan Research Fellow in computer science and received the 2019 VLDB Early Career Research Contribution Award, the 2017 VMware Systems Research Award, an NSF CAREER Award, an Air Force Young Investigator award, two Very Large Data Bases (VLDB) conference best-demo awards, and a best-paper award from the IEEE International Conference on Data Engineering (ICDE).

Current Research Interests

  • ML-enhanced data structures and algorithms
  • Systems for interactive data exploration and model building
  • Infrastructure for rack-scale analytics and machine learning
  • Transaction processing over high-speed networks
  • Hybrid human-machine data management systems

Research Projects

In the following, a list of my current and past research projects:

  • Learned Systems Components - How to Enhance traditional data structures and algorithms through machine learning
  • Northstar - A System for Interactive Data Science
  • NAM - Redefining Databases for the Next Generation of Networks
  • QUDE - Quantifying the Uncertainty in Data Exploration
  • Tupleware - Redefining Modern Analytics on Modern Hardware
  • MLBase - The Distributed Machine-Learning Management System
  • S-Store - A streaming OLTP system for big velocity applications
  • MDCC - The Fastest Strong Consistent Multi-Data Center Replication Protocol
  • CrowdDB - Answering Queries with Crowdsourcing
  • PIQL - Performance Insightful Query Language
  • Cloudy/Smoky - a distributed storage and streaming service in the cloud
  • Building a database on cloud infrastructure
  • CloudBench - a benchmark for the cloud
  • Zorba - a general purpose XQuery processor implementing in C++
  • MXQuery - A lightweight, full-featured Java XQuery Engine
  • Mapping Data to Queries (MDQ) - data integration with XQuery
  • XQIB - XQuery In the Browser

Room 32-G914
MIT - CSAIL
32 Vassar St.
Cambridge, MA 02139


Phone: +1 (510) 926-5856

News

Support