Peizhao (Percy) Li

AI Research Scientist at GE HealthCare

peizhaoli [AT] brandeis.edu

Bio

My name is Peizhao Li, pronounced as 'Pay-Jow Lee' and written as '李沛钊' in Chinese. I am an AI Research Scientist at GE HealthCare, working on LLMs & Foundation Models for healthcare applications. I am based in Bellevue, WA. Prior to this, I received my Ph.D. from Brandeis University in 2024. Throughout my Ph.D. studies, I had the opportunity to work as a research intern at Google Research, Amazon Alexa AI, Adobe Research, Mitsubishi Electric Research Laboratories (MERL), NEC Laboratories America, and also as a research fellow at Harvard University.

I research and develop Artificial Intelligence and Machine Learning. I have worked on several topics such as Responsible AI, Multimodal Learning, Computer Vision, and Natural Language Processing. In my Ph.D. thesis 'Harmonizing Fairness with Utility in Data and Learning,' I developed methods from both computational and data-centric perspectives to make machine learning models fair and non-discriminatory, and simultaneously have no or minimal side effects on utility performance. My research work was recognized by a NIJ Graduate Research Fellowship, a Meta Ph.D. Research Fellowship Finalist, and a CVPR Best Paper Award.

I was very fortunate to have Prof. Hongfu Liu as my Ph.D. advisor.

Recent Publications

My five most recent papers. For full publications, please refer to my Google Scholar profile.

MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception

Mohammad Mahbubur Rahman, Ryoma Yataka, Sorachi Kato, Pu Wang, Peizhao Li, Adriano Cardace, Petros Boufounos

ECCV'24: European Conference on Computer Vision, 2024

Rich Human Feedback for Text-to-Image Generation

Youwei Liang, Junfeng He, Gang Li, Peizhao Li, Arseniy Klimovskiy, Nicholas Carolan, Jiao Sun, Jordi Pont-Tuset, Sarah Young, Feng Yang, Junjie Ke, Krishnamurthy Dj Dvijotham, Katie Collins, Yiwen Luo, Yang Li, Kai J Kohlhoff, Deepak Ramachandran, Vidhya Navalpakkam

CVPR'24: IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024 (Oral, Best Paper Award)

"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection

Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, Hongfu Liu

ICLR'24: International Conference on Learning Representations, 2024 (Oral)

Learning Antidote Data to Individual Unfairness

Peizhao Li, Ethan Xia, Hongfu Liu

ICML'23: International Conference on Machine Learning, 2023

Characterizing the Influence of Graph Elements

Zizhang Chen, Peizhao Li, Hongfu Liu, Pengyu Hong

ICLR'23: International Conference on Learning Representations, 2023