Chen Zhao, Ph.D. (赵辰)  

Assistant Professor
Department of Computer Science
School of Engineering and Computer Science
Baylor University
One Bear Place #97141, Waco, Texas 76798, United States

Adjunct Professor
Department of Information Sciences and Technology
College of Emergency Preparedness, Homeland Security & Cybersecurity
University at Albany - State University of New York
1400 Washington Ave, Albany, New York 12222, United States

Email: chen[underscore]zhao[at]baylor[dot]edu



To Prospective Students: I am always looking for students with high motivations and strong coding skills to join my research group or work with me as summer interns. There are several RA openings for graduate students in my lab at Baylor University. These positions are supported by a monthly stipend and tuition waiver. Students are expected to work closely with me on several research projects. Applicants with a degree in related fields (e.g., CS/CE/EE/STAT) are preferred. Research backgrounds in machine learning, artificial intelligence, data mining, big data, and statistics are desired. If you are interested, please email me your application materials (e.g., Curriculum Vitae, Publications, GitHub/GitLab Accounts, Undergraduate/Graduate Transcripts, Personal Statement, and GRE/TOEFL Reports).
NOTE: Unfortunately, due to the volume of emails, I will not respond to all of them.

Call for papers: 3rd Workshop on Ethical Artificial Intelligence: Methods and Applications (EAI-KDD'24) (held in conjunction with ACM SIGKDD 2024).

Call for papers: 3rd Workshop on Uncertainty Reasoning and Quantification in Decision Making (UDM-KDD'24) (held in conjunction with ACM SIGKDD 2024).

Call for papers: 1st Workshop on Robust Machine Learning for Distribution Shifts (RobustMLDS’24) (held in conjunction with IEEE BigData 2024).

Biography

Dr. Chen Zhao is an Assistant Professor in the Department of Computer Science at Baylor University. Prior to joining Baylor, he was a senior R&D computer vision engineer at Kitware Inc. He earned his Ph.D. degree in Computer Science from The University of Texas at Dallas (UTD) in 2021, advised by Dr. Feng Chen. He received dual M.S. degrees in Computer Science and Biomedical Science from the University at Albany, SUNY, and Albany Medical College in 2016.

His research focuses on machine learning, data mining, and artificial intelligence, particularly fairness-aware machine learning, novelty detection, and domain generalization. His publications have appeared in prestigious conferences and journals, including KDD, CVPR, WWW, AAAI, IJCAI, ICDM, TKDD, etc. He has served as a PC member for several international conferences and workshops, such as NeurIPS, KDD, AAAI, IJCAI, ICML, ICLR, etc.

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Publications

2024
  1. [KDD'24] Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously. (To appear)
    Chen Zhao*, Kai Jiang*, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Earl Grant, Feng Chen. (Equal Contribution)
    In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (research track), 2024
  2. [IJCAI'24] Supervised Algorithmic Fairness in Distribution Shifts: A Survey. PDF
    Minglai Shao*, Dong Li*, Chen Zhao*, Xintao Wu, Yujie Lin, Qin Tian. (Equal Contribution)
    In Proceedings of the 33rd International Joint Conference on Artificial Intelligence, 2024
  3. [IJCAI'24] Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments. PDF
    Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen
    In Proceedings of the 33rd International Joint Conference on Artificial Intelligence, 2024
  4. [CVPR'24-W] Causal Diffusion Autoencoders: Toward Representation-Enabled Counterfactual Generation via Diffusion Probabilistic Models. PDF
    Aneesh Komanduri, Chen Zhao, Feng Chen, Xintao Wu
    CVPR Workshop on Generative Models for Computer Vision, 2024
  5. [ArXiv] Graphs Generalization under Distribution Shifts. PDF
    Qin Tian, Wenjun Wang, Chen Zhao, Minglai Shao, Wang Zhang, Dong Li.
    ArXiv preprint, 2403.16334, 2024
  6. [IGARSS'24] Uncovering Bias in Building Damage Assessment from Satellite Imagery. PDF
    Dennis Melamed, Cameron Johnson, Isaac D. Gerg, Chen Zhao, Russell Blue, Anthony Hoogs, Brian Clipp, Philip Morrone
    In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2024
  7. [TKDD] Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness. PDF
    Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen.
    The ACM Transactions on Knowledge Discovery from Data, 2024
2023
  1. [NeurIPS'23-W] Achieving Counterfactual Fairness in Changing Environments via Sequential Autoencoder.
    Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Baoluo Meng, Haifeng Chen
    NeurIPS Workshop on Algorithmic Fairness through the Lens of Time (AFT), 2023
  2. [ArXiv] Pursuing Counterfactual Fairness via Sequential Autoencoder Across Domains. PDF
    Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen
    ArXiv preprint, 2309.13005, 2023
  3. [ArXiv] Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective. PDF
    Haoliang Wang, Chen Zhao, Yunhui Guo, Kai Jiang, Feng Chen
    ArXiv preprint, 2309.10209, 2023
  4. [CIKM'23] Adaptation Speed Analysis for Fairness-aware Causal Models. PDF
    Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen.
    In Proceedings of the ACM International Conference on Information and Knowledge Management (full paper), 2023
  5. [CIKM'23] Contrastive Representation Learning Based on Multiple Node-centered Subgraphs. PDF
    Dong Li, Wenjun Wang, Minglai Shao, Chen Zhao.
    In Proceedings of the ACM International Conference on Information and Knowledge Management (full paper), 2023
  6. [KDD'23] Towards Fair Disentangled Online Learning for Changing Environments. PDF
    Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Christan Earl Grant, Feng Chen.
    In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (research track), 2023
  7. [KDD'23-W] Learning Fair and Domain Generalization Representation.
    Dong Li, Chen Zhao, Minglai Shao, Xujiang Zhao
    KDD workshop on Ethical Artificial Intelligence: Methods and Applications (EAI), 2023
  8. [KDD'23-W] Adaptation Speed of Causal Models Concerning Fairness.
    Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen
    KDD workshop on Ethical Artificial Intelligence: Methods and Applications (EAI), 2023
  9. [AAAI'23-W] Detecting Multi-Label Out-of-Distribution Nodes on Graphs.
    Ruomeng Ding, Xujiang Zhao, Chen Zhao, Minglai Shao
    AAAI workshop on Uncertainty Reasoning and Quantification in Decision Making (UDM), 2023
  10. [CVPR'23] Open Set Action Recognition via Multi-Label Evidential Learning. PDF
    Chen Zhao, Dawei Du, Anthony Hoogs, Christopher Funk.
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
  11. [ICASSP'23] Multi-Label Temporal Evidential Neural Networks for Early Event Detection. PDF
    Xujiang Zhao, Xuchao Zhang, Chen Zhao, Jin-Hee Cho, Lance Kaplan, Dong Hyun Jeong, Audun Jøsang, Haifeng Chen, Feng Chen
    In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2023
  12. [ArXiv] An Automated Vulnerability Detection Framework for Smart Contracts. PDF
    Feng Mi, Chen Zhao, Zhuoyi Wang, Sadaf MD Halim, Xiaodi Li, Zhouxiang Wu, Latifur Khan, Bhavani Thuraisingham
    ArXiv preprint, 2301.08824, 2023
2022
  1. [KDD'22] Adaptive Fairness-Aware Online Meta-Learning for Changing Environments. PDF
    Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen
    In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (research track), 2022
  2. [ArXiv] xFBD: Focused Building Damage Dataset and Analysis. PDF
    Dennis Melamed, Cameron Johnson, Chen Zhao, Russell Blue, Philip Morrone, Anthony Hoogs, Brian Clipp
    ArXiv preprint, 2212.13876, 2022
  3. [PAKDD'22] Layer Adaptive Deep Neural Networks for Out-of-distribution Detection. PDF
    Haoliang Wang, Chen Zhao, Xujiang Zhao, Feng Chen
    In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2022
  4. [AAAI'22] A Nested Bi-level Optimization Framework for Robust Few-Shot Learning. PDF
    Krishnateja Killamsetty, Changbin Li, Chen Zhao, Rishabh Krishnan Iyer, Feng Chen
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2022
2021
  1. [KDD'21] Fairness-Aware Online Meta-learning. PDF
    Chen Zhao, Feng Chen, Bhavani Thuraisingham
    In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (research track), 2021
  2. [ICBC'21] VSCL: Automating Vulnerability Detection in Smart Contracts with Deep Learning. PDF
    Feng Mi, Zhuoyi Wang, Chen Zhao, Jinghui Guo, Fawaz Ahmed, Latifur Khan
    In Proceedings of the IEEE International Conference on Blockchain and Cryptocurrency, 2021
  3. [NeurIPS’21-W] A Reweighted Meta Learning Framework for Robust Few Shot Learning.
    Krishnateja Killamsetty, Changbin Li, Chen Zhao, Rishabh Krishnan Iyer, Feng Chen
    NeurIPS Workshop on Meta-Learning (MetaLearn), 2021
  4. [WWW'21] CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining. PDF
    Zhuoyi Wang, Yuqiao Chen, Chen Zhao, Hemeng Tao, Yu Lin, Xujiang Zhao, Yigong Wang and Latifur Khan
    In Proceedings of the ACM International World Wide Web Conference, 2021
2020
  1. [ArXiv] PDFM: A Primal-Dual Fairness-Aware Framework for Meta-learning. PDF
    Chen Zhao, Feng Chen, Zhuoyi Wang, Latifur Khan
    ArXiv preprint, 2009.12675v2, 2020
  2. [ICDM'20] A Primal-Dual Subgradient Approach for Fair Meta Learning. PDF
    Chen Zhao, Feng Chen, Zhuoyi Wang, Latifur Khan
    In Proceedings of the IEEE International Conference on Data Mining(full paper), 2020
  3. [ICKG'20] Fair Meta-Learning for Few-Shot Classification. PDF
    Chen Zhao, Changbin Li, Jincheng Li, Feng Chen
    In Proceedings of the IEEE International Conference on Knowledge Graph, 2020
  4. [ICKG'20] Unfairness Discovery and Prevention for Few-Shot Regression. PDF
    Chen Zhao, Feng Chen
    In Proceedings of the IEEE International Conference on Knowledge Graph, 2020
Before 2020
  1. [ICDM'19] Rank-Based Multi-task Learning for Fair Regression. PDF
    Chen Zhao, Feng Chen
    In Proceedings of the IEEE International Conference on Data Mining(full paper), 2019
  2. [Development'17] CDC42 is required for epicardial and pro-epicardial development by mediating FGF receptor trafficking to plasma membrane. PDF
    Jingjing Li, Lianjie Miao, Chen Zhao, Wasay Mohiuddin Shaikh Qureshi, David Shieh, Hua Guo, Yangyang Lu, Saiyang Hu, Alice Huang, Lu Zhang, Chen-leng Cai, Leo Q. Wan, Hongbo Xin, Peter Vincent, Harold A. Singer, Yi Zheng, Ondine Cleaver, Zhen-Chuan Fan, Mingfu Wu
    Development, 147-173, 2017
  3. [Development'14] Numb family proteins are essential for cardiac morphogenesis and progenitor differentiation. PDF
    Chen Zhao, Hua Guo, Jingjing Li, Thomas Myint, William Pittman, Le Yang, Weimin Zhong, Robert J. Schwartz, John J. Schwarz, Harold A. Singer, Michelle D. Tallquist, Mingfu Wu
    Development, 141(2):281-95, 2014

Academic Services

Conference PC Member and Reviewer

Workshop Reviewer

Journal Reviewer

Others

Academic Talks

Invited Talks

Teaching

Baylor University

The State University of New York at Albany

The University of Texas at Dallas

Albany Medical College

Awards & Honors


Last Updated on May, 2024
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