Niladri S. Chatterji
Papers
2
Total Citations
2,180
H-Index
2
About
Niladri S. Chatterji is a leading researcher at the intersection of artificial intelligence, reinforcement learning, and statistical learning theory. His work addresses foundational questions about how AI systems learn from limited, delayed, or complex feedback—critical for deploying reliable models in the real world. Chatterji is best known for his co-authorship of the landmark report *On the Opportunities and Risks of Foundation Models* (2021), which has amassed over 2,100 citations. This influential paper defined the paradigm of foundation models—large-scale, adaptable systems like GPT-3 and DALL-E—and sparked widespread discussion on their transformative potential and inherent risks. In reinforcement learning, Chatterji has advanced the theory of learning from sparse feedback, notably in his work on once-per-episode binary feedback, a challenging yet realistic setting for many applications. His research provides rigorous theoretical guarantees for algorithms that must learn effectively under severe information constraints. Through these contributions, Chatterji is shaping the future of robust, generalizable AI systems.
Research Focus
Key Achievements
Top Papers
- 1On the Opportunities and Risks of Foundation Models2,177 citations · 2021
- 2On the Theory of Reinforcement Learning with Once-per-Episode Feedback3 citations · 2021