Hello! I’m a postdoc at Harvard University, hosted by Prof. Heng Yang. Previously, I had a blast doing my phd at Boston University, advised by Prof. Yannis Paschalidis and Prof. Ashok Cutkosky. Long ago I did my undergrad at Tsinghua University.

I am broadly interested in sequential data science. How do we process information revealed on a data stream, and rigorously use that to support real-time decision making? For me, this is a fascinating field at the intersection of optimization, signal processing, statistics and game theory.

Specifically, my research centers around adaptive online learning. The goal is to design online decision making algorithms that Bayes-optimally exploit offline knowledge, such as domain structures, physical models and deep learning. I envision it as a bridge between offline methods and their robust online deployment against uncertainties.

Email address: zhiyuz [at] seas (dot) harvard (dot) edu

CV   Github   Google Scholar   Semantic Scholar

Publication

• Improving Adaptive Online Learning Using Refined Discretization
  ZZ, Heng Yang, Ashok Cutkosky, Ioannis Paschalidis.
  arXiv 2023.

• Unconstrained Dynamic Regret via Sparse Coding
  ZZ, Ashok Cutkosky, Ioannis Paschalidis.
  NeurIPS 2023.

• Optimal Comparator Adaptive Online Learning with Switching Cost
  ZZ, Ashok Cutkosky, Ioannis Paschalidis.
  NeurIPS 2022. Also presented at ICML 2022 workshop.

• 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. Also presented at ICML 2020 Workshop.

PhD Dissertation.  Temporal Aspects of Adaptive Online Learning: Continuity and Representation