- Gordon McKay Professor of Computer Science and Statistics
- Co-director of the Kempner Institute
- Affiliate Professor in the Department of Computer Science and Department of Statistics
Sr. Principal Researcher at MSR-NYC
Research
I am interested in the mathematical foundations of machine learning and AI. I focus on the design of provably efficient and practical algorithms that are relevant for a broad range of paradigms. My current interests include : i) reinforcement learning and controls ii) representation (and deep) learning and iii) natural language processing and memory. I enjoy collaborating with a diverse set of researchers to tackle these issues!
Book
Reinforcement Learning: Theory and Algorithms
Alekh Agarwal, Nan Jiang, Wen Sun, and I are writing a monograph on Reinforcement Learning. We will be periodically making updates to the draft. Also, see current course CS 6789: Foundations of Reinforcement Learning.
News
Projects
Cover Tree for Nearest Neighbor Search
COVID19 Reports and CommonCircle
Activities and Services
- Committee for the Sloan Research Fellowships in Computer Science (active).
- Co-organizer for the Simons Symposium on New Directions in Theoretical Machine Learning, May 2019.
- Co-organizer for the Simons Foundations of Machine Learning, Winter, 2017
- Co-chair for the Simon's Representational Learning workshop, March, 2017
- Co-chair for the IMS-MSR Workshop: Foundations of Data Science, June 11th, 2015.
- Steering committee for the fourth New England Machine Learning Day, May 18th, 2015.
- Program committee for the third New England Machine Learning Day, May 13th, 2014.
- Co-chair for New York Computer Science and Economics Day V, Dec 3rd, 2012.
- Program committee for the first New England Machine Learning Day, May 16th, 2012.
- Program chair for the 24th Annual Conference on Learning Theory (COLT 2011) which took place in Budapest, Hungary, on July 9-11, 2011.
Past Courses
- CS 6789: Foundations of Reinforcement Learning, guest teacher, Fall 2020
- CSE 599m: Reinforcement Learning and Bandits, Spring 2019
- CSE 446: Machine Learning, Winter 2019
- CSE 547 / STAT 548: Machine Learning for Big Data, Spring 2018
- CSE 446: Machine Learning, Winter 2018
- CSE 547 / STAT 548: Machine Learning for Big Data, Spring 2017
- CSE 546: Machine Learning, Autumn 2016
- CSE 547 / STAT 548: Machine Learning for Big Data, Spring 2016
- CSE 546: Machine Learning, Autumn 2015
- Stat 928: Statistical Learning Theory
- Stat 991: Multivariate Analysis, Dimensionality Reduction, and Spectral Methods
- Large Scale Learning
- Learning Theory