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, I enjoy (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.
-
PhD students: Yukun Wang (王钰琨)
-
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. It is open to all, and please reach out to learn more.
Email address: zhiyuzresearch@gmail.com
Publication
Representative works
-
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
-
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