Jianyi Yang
I am an Assistant Professor in the Department of Computer Science at the University of Houston. My research focuses on advancing trustworthy and efficient AI, with the goal of developing AI systems that are resilient, responsible, and resource-efficient. My recent research spans generative-AI-enabled uncertainty modeling and risk control, safe reinforcement learning, resource-efficient AI, and learning-augmented algorithms.
I received my PhD degree from University of California, Riverside in 2023, advised by Prof. Shaolei Ren. I was a visiting research assistant at Caltech and UC Riverside during 2023-2024, working with Prof. Adam Wierman and Prof. Shaolei Ren.
Contact: jyang66@uh.edu or jianyiyang.ai@gmail.com or jyang71@central.uh.edu
news
| Sep 24, 2026 |  Our paper Online Allocation with Differential Privacy was accepted (Spotlight) by NeurIPS 2026! It defines the differential privacy metrics for online resource allocation problems, formally establishes the performance limits of privacy-robustness trade-offs, and provides a theoretically-sound meta framework. |
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| Sep 14, 2026 | I will serve on the Local Steering Committee for the Texas Colloquium on Distributed Learning: Frontiers on Agents and Systems (TL;DR 2026), to be held at Rice University in Houston on October 8–9, 2026. Please consider submitting your posters to the workshop. |
| Jan 26, 2026 | Our paper Distributionally Robust Optimization via Generative Ambiguity Modeling was accepted by ICLR 2026. Congratulations to Jiaqi! |
| Dec 9, 2025 | Our paper 3D-Learning: Diffusion-Augmented Distributionally Robust Decision-Focused Learning is accepted by IEEE INFOCOM 2026. This paper exploits diffusion models to construct the adversarial environments for Predict-Then-Optimize (PTO) problems and trains distributionally robust prediction models. Congratulations to Jiaqi. |
| Sep 25, 2025 | Our paper Distributionally Robust Optimization via Diffusion Ambiguity Modeling was accepted by OPT 2025 collocated with NeurIPS 2025! This paper builds a diffusion-based ambiguity set for Distributionally Robust Optimization (DRO) problems. |
| Sep 20, 2025 | I was invited to give a talk at Hewlett Packard Enterprise Data Science Institute (HPE-DSI) on October. |
| Jul 15, 2025 | I will server in the Technical Program Committee of e-Energy’26 |
| Jul 1, 2025 | I will server in the Technical Program Committee of SIGMETRICS’26 |
| Jan 19, 2025 | Our paper Learning-Augmented Online Control for Decarbonizing Water Infrastructures has been accepted by e-Energy’25! This paper provides a learning-augmented control algorithm for a critical infrastructure: the municipal water supply systems. The algorithm guarantees the worst-case safety of water supply (e.g. for fire protection) while minizing the energy costs for pumping water. |
| Sep 25, 2024 | Our paper Online Budgeted Matching with General Bids has been accepted by NeurIPS 2024! It provides a provable algorithm to solve online budgeted matching problem with general bids. Manuscript will come out soon. |
| Sep 25, 2024 | Our paper Learning-Augmented Decentralized Online Convex Optimization in Networks has been accepted by ACM SIGMETRICS’25! It proposes a novel algorithm to provably robustify machine learning predictions for decentralized optimization in networks. |
| May 1, 2024 | Our paper Building Socially-Equitable Public Models was accepted by ICML 2024! |
| Dec 13, 2023 | Our paper Online Allocation with Replenishable Budgets: Worst Case and Beyond was accepted by ACM SIGMETRICS 2024! This paper provides fundamental algorithms with worst-case guarantees for online resource allocation problem and proposes a learning-augmented algorithm to improve the statistical performance under worst-case guarantee. |