Research

Please find a list of my publications and working publications. A full collection of my work can be found on my Google Scholar.

Journal and Peer-Reviewed Conference Publications

S. Wu, S. Yang, and S. C. Kou. Are Statistical Methods Obsolete in the Era of Deep Learning? A Study of ODE Inverse Problems. To appear in The American Statistician, 2026+. [arXiv]

D. M. Zoltowski*, S. Wu*, X. Gonzalez, L. Kozachkov, and S. W. Linderman. Parallelizing MCMC Across the Sequence Length. NeurIPS 2025. [arXiv]

S. Wu*, A. G. Meyer*, L. Clemente, L. M. Stolerman, F. Lu, A. Majumder, R. Verbeeck, S. Masyn, and M. Santillana. Ensemble Approaches for Short-Term Dengue Fever Forecasts: A Global Evaluation Study. Proceedings of the National Academy of Sciences 122. 2025. [PNAS]

S. Wu, F. Lu, E. Raff, and J. Holt. Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling. NeurIPS 2024. [arXiv]

Peer-Reviewed Workshop Papers

S. Wu*, E. M. Shen*, C. Badrinath*, J. Ma, and H. Lakkaraju. Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions. Challenges in Deployable Generative AI Workshop at ICML 2023. [arXiv]

S. Wu, F. Lu, E. Raff, and J. Holt. Exploring the Sharpened Cosine Similarity. I Can’t Believe It’s Not Better Workshop at NeurIPS 2022. [arXiv]

Working Papers and Preprints

S. Wu, Y. Nair, and E. J. Candès. Efficient Evaluation of LLM Performance with Statistical Guarantees. Working paper, 2026. [arXiv]

S. Wu* and A. Echarghaoui*. Intelligently Weighting Multiple Reference Models for Direct Preference Optimization of LLMs. Working paper, 2025. [arXiv]

K. Chasalow*, S. Wu*, and S. A. Murphy. Missing Data Multiple Imputation for Tabular Q-Learning in Online RL. Working paper, 2025. [arXiv]

(*Equal Contribution)