Pengfei Li

I am Pengfei Li, a final-year CS Ph.D. candidate in University of California, Riverside, under the supervision of Prof. Shaolei Ren. Prior to that, I obtained a M.S.E degree in Robotics from Johns Hopkins University and a B.E. degree in Electrical Engineering from Zhejiang University. Since the summer of 2022, I has been working closely with Adam Wierman at Caltech, focusing on trustworthy learning augmented online optimization. In the summer 2023, I was fortunate to intern at Nokia Bell Labs mentored by Dr Matthew Andrews, focusing on autonomous ware-house operations with digital twins.

Research Highlights

My overarching research goal is to develop trustworthy, sustainable and equitable machine learning systems that enhance the efficiency of real-world systems and address critical societal and environmental challenges.

• Trustworthy Online Algorithms: Addressing fundamental theoretical and algorithmic challenges in online algorithms, towards strict robustness, safety and fairness guarantees (NeurIPS’23a, NeurIPS’23b, ICML’23).
• Sustainable AI: Modeling and measuring the resource consumption and environmental footprint during the deployment of AI system, mitigating these negative impacts with principled approaches (SIGMETRICS’22, eEnergy’24a, eEnergy’24b, SIGMETRICS’25, CACM’25).
• Environmental and Social Fairness: Developing algorithmic solutions to mitigate the environmental and social inequities (ICML’24, HotEthics’24).