Asst. Professor, UIC

Hi, Thanks for your interest!

I am an assistant professor at the Department of Computer Science, University of Illinois (UIC), Chicago, since Fall, 2025. I am a member of UIC CS Theory group, as well as IDEAL Institute. Previously, I was a research scientist at Apple MLR, broadly working in the area of Machine Learning theory. I completed my postdoc at Microsoft Research (NYC) and PhD from the department of Computer Science, Indian Institute of Science, Bangalore. I was fortunate to be advised by Aditya Gopalan and Chiranjib Bhattacharyya during my PhD, and to have been a part of several internships, including Microsoft Research (In), Inria (Paris), and Google AI (MTV).

Research Focus: Building Personalized Educator (through Human-Aligned Learning)
Main Research Area: AI-Alignment with Reinforcement Learning with Human Feedback (RLHF)

Related Research Areas: Machine Learning (esp. Online Learning Bandits, RL theory), Optimization, Federated Learning, Quantum Information Theory, Differential Privacy, and Mechanism Design.


Brief Research Summary

My primary research focuses on developing, improving, and theoretically analyzing reinforcement learning with human feedback (RLHF) algorithms to enhance personalized machine assistance for humanity. Applications include language models, assistive robotics, autonomous driving, and personalized systems—-almost any system that can improve itself through user interaction.

Specifically, I am deeply motivated by the tremendous potential of AI to democratize learning—reshaping our current education system into a truly adaptive, accessible, and personalized experience for every learner! Driven by this transformative power of generative AI and language models, we hope to design foundations for equitable, intelligent education systems that turn this vision into reality. My research focuses on developing futuristic educational models by leveraging RLHF theory alongside tools from Machine Learning (Online Learning, Bandits, and RL theory), Optimization, Federated Learning, Differential Privacy, and Mechanism Design.

A bit more generally, my work focuses on building large-scale, robust, personalized AI models for sequential decision-making under partial or restricted feedback, such as user interactions, preferences, demonstrations, proxy observations, and rankings. In the past, I have also explored combinatorial decision spaces, dynamic regret, multiplayer games, and distributed optimization. My work falls under various interdisciplinary research areas, including ML, learning theory, optimization, operations research, mechanism design, privacy, federated learning, and algorithmic fairness. Recently, I have also been spending considerable time exploring Quantum Information Theory and its potential to improve algorithmic efficiency across different classes of ML algorithms. Feel free to reach out if you'd like to discuss any of these topics!

[Selected Papers]   [Full List]   [Google Scholar]   [DBLP]   [arXiv]

Prospective Students: I am looking to work with ML enthusiasts and motivated students! If you are interested in joining our research group, please send me your resume and research statement. I also encourage you to apply to the UIC-CS PhD program (and do mention my name in your application).

Selected Papers: