Scalable Statistical Machine Learning (S2ML) Lab @ UC San Diego

("Work hard, play hard!" Advancing ML frontiers from Goleta to La Jolla.)

Current Members

Yu-Xiang Wang

Director of S2ML lab. Associate Professor at UCSD (2024 - now). Previously associate professor at UCSB (2018-2023) and Amazon Web Services (2017-2018). Yu-Xiang received his PhD 2017 from the Machine Learning Department at Carnegie Mellon University (CMU). His research interests include on statistical theory and methodology, differential privacy, large-scale machine learning, reinforcement learning and deep learning.

Xuandong Zhao

PhD Student. Xuandong is working on responsible use of generative AI, e.g., watermarking, copyright, security, and privacy. He earned his B.S. in Computer Science from Zhejiang University in 2019. Xuandong is a recipient of the Chancellor's Fellowship from UC Santa Barbara.

Jianyu Xu

PhD Student. A fifth-year PhD student studying on-line learning and bandits, with specialization in dynamic pricing.

Dan Qiao

PhD Student. Dan's research focuses on reinforcement learning, differential privacy and deep learning optimization. He has done some work about low-adaptive (batched) RL and differentially private RL in both online and offline settings. Currently, Dan is developing a modern theory for deep learning.

Esha Singh

PhD Student. Esha is interested in the mathematical foundations of machine learning and building efficient machine-learning solutions. Recently, she has been focusing on understanding the optimization and generalization of neural networks.

Erchi Wang

PhD student. Erchi is working on differentially private tree-boosting with applications to healthcare. He is also interessted in theory of private learning.

Momin Haider

MS student. Momin worked on online and offline reinforcement learning with applications to computer networking. Momin is joining University of Virginia (UVA) as a PhD student.

Sunil Madhow

BS/MS Student. Sunil worked on offline reinforcement learning with adaptively collected data and is currently designing new adaptive nonparametric regression methods. Sunil is joining UC San Diego as a PhD Student.


Dheeraj Baby

PhD in CS (2023). Dheeraj worked on online learning under non-staionarities and non-parametric regression / forecasting, as well as online adaptation to distribution-shifts in deep learning. He received Best Student Paper from COLT'21. Currently Dheeraj works at Amazon as an Applied Scientist.

Rachel Redberg

PhD in CS (2023). Now Experiential AI Postdoctoral Fellow at Northeastern University. Rachel worked on differential privacy and differentially private machine learning.

Chong Liu

PhD in CS (2023). Chong worked on Bayesian optimization, bandits, active learning as well as adaptive algorithms for data-driven new material discovery. He is currently a Data Science Institute Postdoctoral Scholar at UChicago. He is starting as an Assistant Professor of Computer Science at University at Albany - State University New York.

Yuqing Zhu

PhD in CS (2023). Now research scientist at TikTok. Yuqing worked on modern accounting methods for differential privacy and private learning. Yuqing is a main contributor to autodp.

Ming Yin

PhD in Statistics (2023); PhD in CS (2023). Now postdoc at Princeton University. Ming worked on offline policy evaluation and learning in reinforcement learning.

Kaiqi Zhang

PhD in ECE (2023). Now scientist at Huawei. Kaiqi worked on the theory of deep learning.

Other S2ML Affiliates / Alumni

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