Pengfei Li

I am Pengfei Li, a fourth-year CS Ph.D. student in University of California, Riverside, under the supervision of Prof. Shaolei Ren. Recently, we also work closely with Adam Wierman on online optimization problems. I obtained a M.S.E degree in Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, under the supervision of Prof. Alan Yuille and Prof. Gregory Hager.

Prior to joining JHU, I graduated from Zhejiang University with honors, majoring in Electrical Engineering. I was also a member of Advanced Class of Engineering Education (ACEE) in Chu Kochen Honor College (CKC). In summer 2017, I took part in the International Summer Research Program in UCSD under the supervision of Prof. Atanasov as a research intern.

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• Two papers accepted by ACM eEnergy 2024, the paper A dataset for research on water sustainability was awarded as Best Notes Paper Award (1 out of 40 submissions). .

• Our preprint about LLM water consumption has been covered by Nature Briefing.

My research interests mainly focus on Nonlinear Optimization, Machine Learning and Graph Theory. Representative papers are highlighted, * indicates equal contributions.

While many approaches have been proposed to make AI more energy-efficient and environmentally friendly, environmental inequity -- the fact that AI's environmental footprint can be disproportionately higher in certain regions than in others. This paper takes a first step toward addressing AI's environmental inequity by balancing its regional negative environmental impact.

We study the most general form of Smoothed Online Convex Optimization, a.k.a. SOCO, including multi-step nonlinear switching costs and feedback delay. We propose a novel machine learning (ML) augmented online algorithm, Robustness-Constrained Learning(RCL). Importantly, RCL is the first ML-augmented algorithm with a provable robustness guarantee in the case of multi-step switching cost and feedback delay.

The growing carbon footprint of large artificial intelligence (AI) models, such as GPT-3, has been undergoing public scrutiny. Unfortunately, however, the equally important and enormous water footprint of AI models has remained under the radar. We highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI.

A novel RL-based approach for edge-weighted online bipartite matching with robustness guarantees, achieving both good average-case performance and strict worst-case guarantee. This framework (LOMAR) supports training the RL policy by explicitly considering the online robustification operation.

EC-L2O is the first to address the "how to learn" challenge for online convex optimization, which requires new algorithm design, closed-form differentiation and theoritical analysis.

A novel 3D crowd human pose estimation method, which proposed a faster cross-view matching based on graphical model

©Pengfei Li, last updated June 2024.
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