Bio


I am a Postdoctoral Scholar at Stanford, where I am excited to work with Amin Saberi. I completed my Ph.D. at Yale University where I was fortunate to be advised by Amin Karbasi and Manolis Zampetakis.

My research focuses on machine learning under complex conditions where traditional assumptions break down. My work has two parts. First, I develop foundations for machine learning with missing and selectively observed data (spanning causal inference, limited-dependence, truncated statistics, and omissions shaped by societal biases). Second, I study why generative AI systems (including language models) are effective and design methods to evaluate and improve their safety.

My work has received the Best Paper Award at COLT, been featured in WIRED, and received the Sir Binay Kumar Sinha award from IIT Kanpur. As an undergraduate, I represented IIT Kanpur at the ICPC World Final. While at Yale, I also taught at the Yale ICPC Club.

Stanford Advisors


All Publications


  • Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases (Extended Abstract) Kalavasis, A., Mehrotra, A., Zhou, F. edited by Hanneke, S., Lattimore, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2026: 3904-3905
  • DDPM Score Matching and Distribution Learning Chewi, S., Kalavasis, A., Mehrotra, A., Montasser, O. edited by Hanneke, S., Lattimore, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2026: 1434-1435
  • Language Generation with Infinite Contamination Mehrotra, A., Velegkas, G., Yu, X., Zhou, F. edited by Hanneke, S., Lattimore, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2026: 5053-5112
  • Differentially Private Language Generation and Identification in the Limit (Extended Abstract) Mehrotra, A., Velegkas, G., Yu, X., Zhou, F. edited by Hanneke, S., Lattimore, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2026: 5113-5114