- 'MultiScale Contextual Bandits for Long-term Objectives', NeurIPS 2025
- 'Fairness in Ranking under Disparate Uncertainty', EAAMO 2024
- 'Semi-Parametric Inducing Point Networks and Neural Processes', ICLR 2023
Research Experience
Worked with several Netflix researchers during 2025; Prior to starting PhD, had worked at Amazon, Stanford, Georgia Tech, and Delhi University, India.
Education
PhD Candidate at Cornell University, advised by Professor Thorsten Joachims; Specific details on time and major not provided.
Background
Research interests: Learning from feedback in interactive systems, including recommender systems and LLMs; Focus areas: Machine learning, probabilistic modeling, and reinforcement learning; Summary: Aiming to develop methods that address real-world complexities such as sparse rewards, long horizons, computational inefficiency, etc., with an eye towards the social impact of these systems.
Miscellany
Will be presenting the MultiScale Bandits paper at NeurIPS'25 in San Diego. Open to collaboration or scheduling meetings.