Sharan Sahu

I am a second-year PhD student in Statistics and Machine Learning at Cornell University, advised by Martin Wells and Yuchen Wu.

I am interested in statistical machine learning and establishing rigorous foundations for statistical and machine learning methods, while also developing new algorithms guided by theoretical insights. I am fortunate to have been supported by a Cornell University Fellowship.

Before Cornell, I was an undergraduate at UC Berkeley studying Computer Science, where I was advised by Iain Carmichael and Ryan Tibshirani.

If you're an undergrad or Master's student at Cornell and are interested in collaborating, please reach out!

Sharan Sahu

Featured Research

Selected recent work across optimization, alignment, generative models, and differential privacy.

All papers →

Presentations & Talks

Towards Optimal Differentially Private Regret Bounds in Linear MDPs

Cornell University, 2025

View Slides →

The Machine Learning Problems Behind Large Language Models: Self-Supervision, Fine-Tuning, and Reinforcement Learning

University of North Carolina, Chapel Hill, 2025

View Slides →

Beyond RNNs: An Introduction to Transformers and LLMs

Cornell Tech (Break Through Tech), 2025

View Slides →

Unlocking the Power of Databases: The Crucial Role of Theory and Indices in Scalable Vector Databases for Machine Learning

Naval Postgraduate School, 2024

View Slides →

Miscellanea

SOP Video

Writing a Technical SOP for PhD / Research Master's Applications

A video with Sithija Manage breaking down how I approached graduate school applications and wrote my Statement of Purpose. We discuss what to include, how to describe research experiences with technical detail, and how to tailor your SOP for committee-driven vs PI-driven admissions.

PhD Journey Interview

Interview with Sithija Manage: My PhD Application Journey

An interview about my journey through graduate school applications. We discuss my undergraduate research at Berkeley, Stanford, and USC, the admissions process, writing statements of purpose, building mentorships, and evaluating program fit.

Liftoff Podcast

Interview with Aman Manazir: From USAMO Math to Quant Trading, ML PhD, and AI Startups

A podcast episode on the Liftoff Podcast with Aman Manazir. I share how I got interested in competitive math, balanced internships and research at Berkeley, Stanford, and USC, and the decision between quant research and a PhD at Cornell.