- Co-director of the Kempner Institute
- Gordon McKay Professor of Computer Science and Statistics
Research
I am a full-stack researcher in machine learning and AI, focusing on the engineering, scientific, and mathematical aspects of deep learning. My focus is on developing efficient and practical algorithms for foundation models and real-world AI applications. My current research interests include: (i) optimization with an emphasis on real engineering challenges, (ii) exploring the foundational science and mathematical underpinnings of deep learning, and (iii) advancing the usefulness of LLMs and generative AI. These efforts include understanding how to scale AI architectures and algorithms, taking into account hardware constraints and data composition; exploring the role of RL in language learning and communication and examining the impact of scale on deep learning optimization.
Prospective Students
I am actively seeking students with a strong background in applied deep learning or a keen interest in acquiring these skills. As co-director of the newly-established Kempner Institute, we offer substantial computational resources for cutting-edge research. If you're passionate about driving innovation in these domains or interested in a blend of applied and fundamental research, I encourage you to apply to Harvard!
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
Activities and Services
- Committee for the ACM Prize in Computing
- 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