| 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 Sigmetrics’25! It proposes a novel algorithm to provably robustify machine learning predictions for decentralized optimization in networks. Our first-author Pengfei Li is on the job market this year. Pengfei is an excellent researcher and a reliable collaborator. We have worked together on a lot of works. Please reach out to him if you have position information! |
| May 1, 2024 | Our paper Building Socially-Equitable Public Models was accepted by ICML 2024! Congratulations to Yejia and other co-authors! |
| Feb 23, 2024 | I attended ITA Workshop 2024 at San Diego and gave a talk named Online Budgeted Allocation: Worst Case Guarantee and Learning Augmentation (paper accepted by SIGMETRICS’24). |
| Dec 13, 2023 | Our paper Online Allocation with Replenishable Budgets: Worst Case and Beyond was accepted by ACM SIGMETRICS 2024! Online resource allocation with replenishments is one of the key problems for carbon neutral and/or water self-sufficient computing and is important for network resource management, online advertising, etc. This paper provides fundamental algorithms with worst-case guarantees for this problem and proposes a learning-augmented algorithm to improve the statistical performance under worst-case guarantee. |
| Nov 28, 2023 | Our study on AI water Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models was featured by Nature Briefing! The paper quantitatively reveals the enormous water usage of AI, highlighting the necessity of addressing water footprint along with carbon footprint to enable truly sustainable AI. |
| Jun 21, 2023 | Check out our new paper on environmentally equitable AI Towards Environmentally Equitable AI via Geographical Load Balancing! This paper takes a first step toward addressing AI’s environmental inequity by optimization methods. |