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• Ph.D. University of California, Riverside
• M.S. Beijing University of Posts and Telecommunications
• B.E. Xidian University

Dr. Yang is an Assistant Professor in the Department of Computer Science at the University of Houston. He received his Ph.D. in Computer Science from the University of California, Riverside in 2023 and held visiting research assistant positions at Caltech and UC Riverside during 2023–2024. His research focuses on responsible AI for decision making and decision intelligence for AI systems, with recent projects spanning generative AI, robust and privacy-preserving machine learning, online allocation and matching, reinforcement learning, AI data centers, and large language model (LLM) serving.

• Jianyi Yang, Pengfei Li, Adam Wierman, and Shaolei Ren. "Online budgeted matching with general bids." Advances in Neural Information Processing Systems 37 (2024): 9885-9919.
• Yang, Jianyi, Pengfei Li, Mohammad Jaminur Islam, and Shaolei Ren. "Online allocation with replenishable budgets: Worst case and beyond." Proceedings of the ACM on Measurement and Analysis of Computing Systems 8, no. 1 (2024): 1-34.
• Yang, Jianyi, Pengfei Li, Tongxin Li, Adam Wierman, and Shaolei Ren. "Anytime-competitive reinforcement learning with policy prior." Advances in Neural Information Processing Systems 36 (2023): 77852-77866.
• Yang, Jianyi, and Shaolei Ren. "Informed learning by wide neural networks: Convergence, generalization and sampling complexity." In International Conference on Machine Learning, pp. 25198-25240. PMLR, 2022.
• Li, Pengfei*, Jianyi Yang*, and Shaolei Ren. "Expert-calibrated learning for online optimization with switching costs." Proceedings of the ACM on Measurement and Analysis of Computing Systems 6, no. 2 (2022): 1-35.