Jianyi Yang
I am an incoming Assistant Professor at the University of Houston.
I am currently a postdoctoral researcher at UC Riverside and a visiting researcher at Caltech, working with Prof. Adam Wierman and Prof. Shaolei Ren. Prior of that, I received PhD in Electrical and Computer Engineering Department at the University of California, Riverside, advised by Prof. Shaolei Ren. I graduated from BUPT with M.S. and Xidian University with B.E.
My research goal is to advance responsible and efficient AI and computing. Currently, I am focusing on addressing the social concerns of AI, perticularly in terms of sustainability, privacy, fairness, and safety issues. In general, my research interests lie in the following ares.
- Machine Learning and Artificial Intelligence (trustworthy ML, learning-augmented algorithms, efficient large AI models, etc)
- Decision Intelligence and Optimization (reinforcement learning, online optimization/control, online allocation algorithms, etc)
- Cyber Physical Systems and Critical Infrastructures (resource managment in data centers, energy systems and smart city applications, etc)
- Cloud/Edge Computing and Networks (energy harvesting systems, edge AI, distributed computing, etc.)
I will join the Department of Computer Science at the University of Houston as a tenure-track Assistant Professor in 08/2024.
[PhD Openings] I am seeking PhD students starting in Spring 2025 or Fall 2025. Please contact me if you have an interest.
news
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). |
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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 quantitively reveals the enormous water usage of AI, highlighting the necessity of addressing water footprint along with carbon footprint to enable truly sustainable AI. |
Oct 17, 2023 | I attended Informs Annual Meeting at Phoneix, AZ and gave an invited talk on Anytime-Constrained Sequential Decision-making with Provable Guarantee (paper accepted by NeurIPS’23) at the session of Learning-Augmented Decision-Making In Cyber-Physical System. |
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. |