Welcome to Peizhao Li’s Homepage


My name is Peizhao Li (pronounced as Pay-Jow Lee). I am a third-year Ph.D. student with the Michtom School of Computer Science at Brandeis University, advised by Prof. Hongfu Liu. I work on Machine Learning, with a special interest in Trustworthy and Responsible AI, Algorithmic Fairness, and Deep Learning.

Before getting to Brandeis, I obtained my bachelor’s degree from Beihang University in 2019, where I worked on Computer Vision and Deep Learning advising by Prof. Xiantong Zhen.

I have spent time as a research intern with Dr. Pu Wang at Mitsubishi Electric Research Laboratories, with Dr. Xuchao Zhang at NEC Laboratories America, and with Dr. Jiuxiang Gu at Adobe Research. During my internships, I worked on Contextual Modeling in Deep Learning with applications to Radar Perception, Source Code Editing, and Document Analysis.

Here is my latest Curriculum Vitae.

Contact: peizhaoli [at] brandeis [dot] edu

Open to Internship : I am looking for 2023 Summer Research Internship on Trustworthy AI.

I am collecting a paper and resource list at Awesome Machine Learning Fairness.


Fair Machine Learning

  1. Achieving Fairness at No Utility Cost via Data Reweighing with Influence [pdf]
    Peizhao Li, Hongfu Liu
    2022 International Conference on Machine Learning (ICML)

  2. Deep Clustering based Fair Outlier Detection [pdf]
    Hanyu Song, Peizhao Li, Hongfu Liu
    2021 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

  3. On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections [pdf]
    Peizhao Li, Yifei Wang, Han Zhao, Pengyu Hong, Hongfu Liu
    2021 International Conference on Learning Representations (ICLR)

  4. Deep Fair Clustering for Visual Learning [pdf]
    Peizhao Li, Han Zhao, Hongfu Liu
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Contextual Modeling in Deep Learning

  1. Exploiting Temporal Relations on Radar Perception for Autonomous Driving [pdf]
    Peizhao Li, Pu Wang, Karl Berntorp, Hongfu Liu
    2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

  2. Code Editing from Few Exemplars by Adaptive Multi-Extent Composition [pdf]
    Peizhao Li, Xuchao Zhang, Ziyu Yao, Wei Cheng, Haifeng Chen, Hongfu Liu
    2022 International Conference on Learning Representations Deep Learning For Code Workshop (ICLRW)

  3. SelfDoc: Self-Supervised Document Representation Learning [pdf]
    Peizhao Li, Jiuxiang Gu, Jason Kuen, Vlad Morariu, Handong Zhao, Rajiv Jain, Varun Manjunatha, Hongfu Liu
    2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)


  1. Applications of AlphaFold beyond Protein Structure Prediction [pdf]
    Yuan Zhang, Peizhao Li, Feng Pan, Hongfu Liu, Pengyu Hong, Xiuwen Liu, Jinfeng Zhang

  2. Mining Label Distribution Drift in Unsupervised Domain Adaptation [pdf]
    Peizhao Li, Zhengming Ding, Hongfu Liu
    arXiv preprint, arXiv: 2006.09565

Prior to my Ph.D. study

  1. Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking [pdf]
    Xiaolong Jiang*, Peizhao Li*, Yanjing Li, Xiantong Zhen (* equal contribution)
    arXiv preprint, arXiv: 1907.05315

  2. Two-Stream Multi-Task Network for Fashion Recognition [pdf]
    Peizhao Li, Yanjing Li, Xiaolong Jiang, Xiantong Zhen
    2019 IEEE International Conference on Image Processing (ICIP)

  3. Multi-Scale Aggregation Network for Direct Face Alignment [pdf]
    Peizhao Li*, Anran Zhang*, Lei Yue, Xiantong Zhen, Xianbin Cao (* equal contribution)
    2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

  4. Model-free Tracking with Deep Appearance and Motion Features Integration [pdf]
    Xiaolong Jiang, Peizhao Li, Xiantong Zhen, Xianbin Cao
    2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

Professional Services

Conference Reviewer

NeurIPS’22, ECCV’22, ICDM’22, KDD’22, UAI’22, ICML’22, CVPR’22, ICLR’22, AAAI’22, IJCNN’22, INFOCOMP’21

Journal Reviewer

IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Multimedia
IEEE Computational Intelligence Magazine
Machine Learning
Big Data
Journal of Combinatorial Optimization
Journal of Electronic Imaging
IET Image Processing
IET Computer Vision


Codes can be found at brandeis-machine-learning.