Hello! I’m an assistant professor at Zhejiang University working on the foundations of data science and automation.
Bio. Previously I did my phd at Boston University, advised by Yannis Paschalidis and Ashok Cutkosky. After that I did postdoc at Harvard University hosted by Heng Yang, and at Carnegie Mellon University hosted by Aaditya Ramdas. Long ago I did my undergrad at Tsinghua University.
Research. My research focuses on algorithmic problems at the intersection of optimization, statistics, and game theory. In particular, my priorities are (i) drawing connections across these areas to derive simple, interpretable, and quantitatively strong algorithms from first principles; and (ii) developing theory that leads, rather than follows, the state-of-the-art practice in data science. With collaborators, my group also explores a range of downstream applications with real-world impact, including GenAI, robotics, energy systems, and economics.
Group. I’m fortunate to lead a warm, supportive and intellectually stimulating research group, which also includes the following members.
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PhD students: Yukun Wang (王钰琨)
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MS students: Yiming Sun (孙一鸣)
Please reach out if you are interested in joining us! We have a weekly group meeting that focuses on understanding fundamental topics in data science (topic for Spring’26). It is open to all, and please reach out to learn more.
Email address: zhiyuzresearch@gmail.com
Publication
Representative works
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Operationalizing Stein’s Method for Online Linear Optimization: CLT-Based Optimal Tradeoffs
ZZ, Aaditya Ramdas.
Preprint. -
The Benefit of Being Bayesian in Online Conformal Prediction
ZZ, Zhou Lu, Heng Yang.
Preprint. -
Unconstrained Dynamic Regret via Sparse Coding
ZZ, Ashok Cutkosky, Ioannis Paschalidis.
NeurIPS 2023.
Other works
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Instance-Optimal Matrix Multiplicative Weight Update and Its Quantum Applications
Weiyuan Gong, Tongyang Li, Xinzhao Wang, ZZ. (alphabetical order)
Preprint. -
Population Dynamics Control with Partial Observations
Zhou Lu, Y. Jennifer Sun, ZZ. (alphabetical order)
Preprint. -
Sparsity-Based Interpolation of External, Internal and Swap Regret
Zhou Lu, Y. Jennifer Sun, ZZ. (alphabetical order)
COLT 2025. -
Adapting Conformal Prediction to Distribution Shifts Without Labels
Kevin Kasa, ZZ, Heng Yang, Graham Taylor.
UAI 2025. -
Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning
Aneesh Muppidi, ZZ, Heng Yang.
NeurIPS 2024. -
Discounted Adaptive Online Learning: Towards Better Regularization
ZZ, David Bombara, Heng Yang.
ICML 2024. -
Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise
Kwangjun Ahn, ZZ, Yunbum Kook, Yan Dai.
ICML 2024. -
Improving Adaptive Online Learning Using Refined Discretization
ZZ, Heng Yang, Ashok Cutkosky, Ioannis Paschalidis.
ALT 2024. -
Optimal Comparator Adaptive Online Learning with Switching Cost
ZZ, Ashok Cutkosky, Ioannis Paschalidis.
NeurIPS 2022. -
PDE-Based Optimal Strategy for Unconstrained Online Learning
ZZ, Ashok Cutkosky, Ioannis Paschalidis.
ICML 2022. -
Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory
ZZ, Ashok Cutkosky, Ioannis Paschalidis.
AISTATS 2022. -
Provable Hierarchical Imitation Learning via EM
ZZ, Ioannis Paschalidis.
AISTATS 2021.
PhD Dissertation. Temporal Aspects of Adaptive Online Learning: Continuity and Representation