Pengfei Li

I am Pengfei Li, a third-year CS Ph.D. student in University of California, Riverside, under the supervision of Prof. Shaolei Ren. 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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• One paper accepted by ICML 2023, see you all in Hawaii !🥳

• Received Student Travel Grant from INFOCOM 2022.

• One paper accepted to INFOCOM 2022.

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

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.

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.

We propose a novel expert-robustified learning (ERL) approach, with a novel projection operator. Our method can be easily extended to multistep memory costs, achieving both good average performance and strict robustness.

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

A technique to interpret 3D object detection results from volumetric-based networks

Estimated car pose and activity based on global information (ground plane parameter) estimation. Copy real car's behavior into a game engine, UE4.

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