Duo Zhou

Hi, I'm a student of computer science at the University of Illinois Urbana-Champaign (UIUC). My research interests are in AI Safety, focusing on the theory and application of the following areas:

  • Trustworthy Machine Learning: I build ML systems that remain reliable far beyond the i.i.d. setting. My work spans distribution shift (AdaNet), adversarial robustness, and certifiable safety via neural network verification-designing algorithms with proofs and scalable implementations (Clip-and-Verify, Lookahead Branching, BICCOS) and studying attack-defense dynamics with formal guarantees.
  • Decision Making Under Uncertainty: I develop reinforcement learning, online learning, game-theoretic methods, and optimal control that optimize worst-case and risk-sensitive performance under uncertainty. Recent projects include robust RL (DR-SAC), multi-agent market simulations under information asymmetry (ShortageSim), and distributionally robust path-integral control.
  • Optimization & Learning Theory Foundations: I use convex/nonconvex optimization, duality, and learning theory to derive finite-sample guarantees and stability-robustness trade-offs, then turn them into practical algorithms. Current interests include multi-stage, bi-level and semi-infinite formulations for unlearning, certification, distributionally robust objectives, and generalization under strategic or feedback-coupled environments.
Email: duozhou2 at illinois dot edu Google Scholar GitHub
Profile photo of Duo Zhou

Selected Papers

* indicates contributed equally    † indicates corresponding author

ShortageSim: Simulating Drug Shortages under Information Asymmetry

Mingxuan Cui*, Yilan Jiang*, Duo Zhou*, Cheng Qian, Yuji Zhang†, Qiong Wang†

Accepted by AAAI 2026 (Oral Presentation)

[paper] [code]

Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification

Duo Zhou*, Jorge Chavez*, Hesun Chen, Grani A. Hanasusanto, Huan Zhang

Accepted by NeurIPS 2025

[paper] [code]

Lookahead Branching for Neural Network Verification

Liam Davis, Duo Zhou

Accepted by Formal Methods in Computer-Aided Design 2025

[paper]

DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty

Mingxuan Cui*, Duo Zhou*†, Yuxuan Han, Grani A Hanasusanto, Qiong Wang, Huan Zhang, Zhengyuan Zhou

preprint, 2025

[paper] [code]

Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes

Duo Zhou, Christopher Brix, Grani A Hanasusanto, Huan Zhang

Advances in Neural Information Processing Systems 37, 29324--29353, 2024.

[paper] [code]

Distributionally robust path integral control

Hyuk Park, Duo Zhou, Grani A Hanasusanto, Takashi Tanaka

American Control Conference (ACC), pp. 1164-1171. IEEE, 2024.

[paper] [code]

An adaptive and dynamical neural network for machine remaining useful life prediction

Ruibing Jin*, Duo Zhou*, Min Wu, Xiaoli Li, Zhenghua Chen

IEEE Transactions on Industrial Informatics 20, no. 2 (2023): 1093-1102, 2023

[paper]

Selected Honors

  • The Winner for both Regular & Extended Track in VNN-COMP 2025.
  • The Winner for both Regular & Extended Track in VNN-COMP 2024.
  • First-Class Scholarship for the Graduating Class. 2021.
  • Meritorious Winner of Mathematical Contest in Modeling and Interdisciplinary Contest in Modeling. 2020.
  • Provincial First Prize, Contemporary Undergraduate Mathematical Contest in Modeling. 2019.

Selected Services

  • Reviewer: NeurIPS, ICLR, ICML, AAAI