Sharan Sahu

I am a second-year PhD student in Statistics and Machine Learning at Cornell University, advised by Martin Wells and Yuchen Wu. Broadly, I am interested in statistical machine learning and using statistical/machine learning tools to improve decision-making and bridge the gap between machine learning methodology and practice by making these methods more reliable, human-compatible, statistically rigorous, and deployable. I am fortunate to be supported by a Cornell University Fellowship.

Before joining Cornell University, I was an undergraduate at UC Berkeley where I studied Computer Science. I was very fortunate to be advised by Iain Carmichael and Ryan Tibshirani working on statistics and machine learning problems in computational precision medicine and statistical learning theory.

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

Email / Google Scholar / CV / Twitter / GitHub / LinkedIn

Research

I am interested in theory and methods in the areas of high-dimensional statistics, stochastic optimization, reinforcement learning, deep learning, language and diffusion models, and differential privacy.

On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
Sharan Sahu, Cameron J. Hogan, and Martin T. Wells
A sharp finite-time theory of tracking under distribution shift showing when and why momentum (Heavy-Ball/Nesterov) fails: we decompose tracking error into initialization + noise + drift, prove momentum suppresses noise but amplifies drift with a blow-up as the momentum parameter tends towards 1. We match this with minimax dynamic-regret lower bounds that formalize an unavoidable “inertia window” where plain SGD is provably better.
[ArXiv]

Online Distributionally Robust LLM Alignment via Regression to Relative Reward Reinforcement Learning
Sharan Sahu and Martin T. Wells
A family of distributionally robust policy optimization algorithms for LLM alignment that reduce to simple relative-reward regressions under Wasserstein, Kullback Leibler, and Chi-Squared ambiguity sets, achieving minimax-optimal rates and significantly tighter constants than prior DRO-DPO methods.
[ArXiv]

Towards Optimal Differentially Private Regret Bounds in Linear Markov Decision Processes
Sharan Sahu
Privatizing LSVI-UCB++ with Bernstein bonus achieves state-of-the-art regret in linear MDPs under joint differential privacy with minimal utility loss.
[ArXiv]

Presentations and Talks

Towards Optimal Differentially Private Regret Bounds in Linear MDPs
Sharan Sahu
Cornell University, 2025
[Slides]

The Machine Learning Problems Behind Large Language Models: Self-Supervision, Fine-Tuning, and Reinforcement Learning
Sharan Sahu
University of North Carolina, Chapel Hill, 2025
[Slides]

Beyond RNNs: An Introduction to Transformers and LLMs (Break Through Tech)
Sharan Sahu
Cornell Tech, 2025
[Slides]

Unlocking the Power of Databases: The Crucial Role of Theory and Indices in Scalable Vector Databases for Machine Learning
Sharan Sahu
Naval Postgraduate School, 2024
[Slides]

Miscellanea

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Interview with Aman Manazir: From USAMO Math to Quant Trading, ML PhD, and AI Startups
This is a podcast episode I did with Aman Manazir on the Liftoff Podcast. I share how I first got interested in competitive math and what that journey taught me about problem-solving. We discuss how I balanced early ML internships and research at Berkeley, Stanford, and USC, the process of deciding between a quant research role at a top firm and a PhD in machine learning at Cornell, and what it’s like building a small AI startup with LLMs. Along the way we dig into how to prep for quant trading interviews, land research roles as an undergrad, and take a tech idea from zero to product. Whether you're curious about ML research, quant trading/research careers, or turning an AI project into a startup, I hope my journey offers a few helpful takeaways.

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Interview with Sithija Manage: My PhD Application Journey
This is an interview I did with Sithija Manage about my journey through graduate school applications. We discuss my undergraduate research journey at Berkeley, Stanford, and USC, and how those early experiences guided my focus toward a PhD. I detail the graduate admissions process: writing a clear and impactful statement of purpose, building meaningful mentorships that led to supportive recommendation letters, departmental visits, and what to look for in a graduate program that fits your career and research goals. Along the way, I try to offer some advice on securing undergrad research roles, crafting a cohesive application narrative, and evaluating program decisions. If you're considering doing a PhD or interested in getting into research, I hope this video offers some helpful advice.

Writing a Technical SOP for PhD / Research Master’s Applications
This is a video I did with Sithija Manage where I break down how I approached graduate school applications and, in particular, how I wrote my Statement of Purpose. We talk about what to include in your SOP, how to describe research experiences with real technical detail, and how to clearly communicate your motivation for grad school. I also explain how to tailor your SOP depending on whether admissions are committee-driven or PI-driven, how to frame industry experience, and how to show strong faculty fit by connecting your interests to specific professors. If you’re applying to research-focused master’s programs or PhD programs, I hope this gives you a sense of what a strong SOP can look like and offers some practical guidance.

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