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.
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.
News []
- [04/2024] Two papes are accepted by IJCAI 2024.
- [04/2024] One paper is accepted by a CVPR 2024 workshop.
- [04/2024] I am invited to give a research talk by Equifax Inc.
- [04/2024] I will serve as a reviewer for the Journal of Advances in Artificial Intelligence and Machine Learning.
- [04/2024] I will serve as a panelist for an NSF panel.
- [03/2024] Our new work (distribution shifts on graphs) is now available on ArXiv.
- [03/2024] We're delighted to announce that our workshop proposal on Ethical AI has been accepted by KDD 2024.
- [03/2024] We're delighted to announce that our workshop proposal on Uncertainty Reasoning and Quantification has been accepted by KDD 2024.
- [03/2024] I will serve as a panelist for an NSF panel.
- [03/2024] One paper is accepted by IGARSS 2024.
- [03/2024] I will serve as the Co-Chair of the IEEE BigData 2024 Big Data Cup Challenges.
- [03/2024] I will serve as a PC member for CIKM 2024.
- [02/2024] I will serve as a PC member for ECML PKDD 2024.
- [02/2024] I will serve as a PC member for IEEE Bigdata 2024.
- [02/2024] Our new survey paper (Fairness in Distribution Shifts) is now available on ArXiv.
- [01/2024] I will serve as a PC member for ACM SIGKDD 2024.
- [01/2024] I will serve as a panelist for an NSF panel.
Publications
2024
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[IJCAI'24] Supervised Algorithmic Fairness in Distribution Shifts: A Survey. (To appear)
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
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[IJCAI'24] Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments. (To appear)
Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen
In Proceedings of the 33rd International Joint Conference on Artificial Intelligence, 2024
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[CVPR'24-W] Causal Diffusion Autoencoders: Toward Representation-Enabled Counterfactual Generation via Diffusion Probabilistic Models.
Aneesh Komanduri, Chen Zhao, Feng Chen, Xintao Wu
CVPR Workshop on Generative Models for Computer Vision, 2024
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[ArXiv] Graphs Generalization under Distribution Shifts. PDF
Qin Tian, Wenjun Wang, Chen Zhao, Minglai Shao, Wang Zhang, Dong Li.
ArXiv preprint, 2403.16334, 2024
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[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
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[TKDD'24] 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
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[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
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[ArXiv] Fairness-Aware Domain Generalization under Covariate and Dependence Shifts. PDF
Chen Zhao, Kai Jiang, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Grant, Feng Chen
ArXiv preprint, 2311.13816, 2023
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
- [PC Member] International Conference on Machine Learning (ICML), 2024.
- [PC Member] International Joint Conference on Artificial Intelligence (IJCAI), 2023-2024.
- [PC Member] The Web Conference (WWW), 2024.
- [PC Member] European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2023-2024.
- [PC Member] Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2024.
- [PC Member] AAAI Conference on Artificial Intelligence (AAAI), 2022, 2024.
- [Reviewer] IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024.
- [Reviewer] International Conference on Artificial Intelligence and Statistics (AISTATS), 2021, 2024.
- [PC Member] SIAM International Conference on Data Mining (SDM), 2022, 2024.
- [Reviewer] International Conference on Learning Representations (ICLR), 2024.
- [PC Member] IEEE International Conference on Big Data (BigData), 2021-2024.
- [Reviewer] The Conference on Neural Information Processing Systems (NeurIPS), 2023.
- [PC Member on Research Track] ACM Conference on Knowledge Discovery and Data Mining (KDD), 2020-2024.
- [PC Member on Applied Science Track] ACM Conference on Knowledge Discovery and Data Mining (KDD), 2020-2023.
- [Reviewer] IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023.
- [PC Member] ACM International Conference on Information and Knowledge Management (CIKM), 2022-2024.
- [PC Member] IEEE International Conference on Data Mining (ICDM), 2021-2023.
- [PC Member] International Conference on Web Search and Data Mining (WSDM), 2022.
- [Reviewer] International Conference on Intelligent Computing (ICIC), 2023.
- [TPC Member] International Congress on Blockchain and Applications (BLOCKCHAIN), 2022.
- [PC Member] International Conference on Automated Machine Learning (AutoML), 2022.
- [TPC member] International Conference on Networks, Communication and Information Technology (NCIT), 2023.
Workshop Reviewer
- [Reviewer] NeurIPS Workshop on Diffusion Models, 2023.
- [Reviewer] ICML Workshop on Structured Probabilistic Inference & Generative Modeling (SPIGM), 2023.
- [Reviewer] WACV Workshop on Dealing with Novelty in Open Worlds, 2022-2023.
- [Reviewer] AutoML Workshop on Late-Breaking, 2022.
- [Reviewer] NeurIPS Workshop on Meta-Learning, 2021.
Journal Reviewer
- [Reviewer] Journal of Frontiers in Big Data, 2022-2024.
- [Reviewer] Journal of Advances in Artificial Intelligence and Machine Learning, 2024.
- [Reviewer] Journal of Data-centric Machine Learning Research (DMLR), 2023.
- [Reviewer] ACM Transactions on Knowledge Discovery from Data (TKDD), 2023.
- [Reviewer] Journal of Knowledge-based Systems (KBS), 2022.
- [Reviewer] Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
- [Reviewer] Journal of Springer Machine Learning (MACH), 2022.
- [Reviewer] Journal of IEEE Transactions on Software Engineering (TSE), 2022.
- [Reviewer] Environment and Planning B: Urban Analytics and City Science, 2022.
- [Reviewer] Journal of Big Data Research (JBDR), 2020.
Others
- [Orgnizer, Chair] Workshops on Ethical Artificial Intelligence (EAI): Methods and Applications,
- [Orgnizer, Chair] Workshops on Uncertainty Reasoning and Quantification in Decision Making (UDM),
- [Co-Chair] IEEE BigData 2024 Big Data Cup Challenges, 2024.
- [Panelist] National Science Foundation panels, 2023(x1), 2024(x3).
- [Orgnizer] The Southwest Data Science Conference, 2024.
- [Session Chair at ADS] ACM Conference on Knowledge Discovery and Data Mining (KDD), 2023.
- [Mentor at KDD-Undergraduate Consortium] ACM Conference on Knowledge Discovery and Data Mining (KDD), 2022.
- [Editorial Board Member] American Journal of Data Mining and Knowledge Discovery (AJDMKD), 2022-2024.
- [Editorial Member] American Journal of Artificial Intelligence (AJAI), 2022-2024.
Academic Talks
Invited Talks
- 04/2024: Equifax Inc, Equifax DnA University, Online.
- 10/2023: Baylor University, Data Science Seminar, Waco TX, USA.
- 07/2023: AI TIME, Beijing, China.
- 05/2023: Yunnan University, School of Information Science and Engineering, Kunming, China.
- 05/2023: Southwestern University of Finance and Economics, School of Business Administration, Chengdu, China.
- 05/2023: AI TIME, Beijing, China.
- 04/2023: Indiana State University, Department of Electronics and Computer Engineering Technology, Terre Haute, IN, USA.
- 04/2023: SUNY at Albany, Department of Computer Science, Albany NY, USA.
- 03/2023: Tennessee State University, Department of Computer Science, Nashville TN, USA.
- 03/2023: Millersville University, Department of Computer Science, Millersville PA, USA.
- 03/2023: Baylor University, Department of Computer Science, Waco TX, USA.
- 02/2023: University of Mississippi, Department of Computer and Information Science, Oxford MS, USA.
- 01/2023: University of Alabama at Birmingham, Department of Computer Science, Birmingham AL, USA.
- 12/2022: University of Central Missouri, Department of Computer Science, Warrensburg CO, USA.
- 11/2022: East Carolina University, Department of Computer Science, Greenville NC, USA.
- 11/2022: The University of Texas at Dallas, Department of Computer Science, Richardson TX, USA.
- 10/2022: NEC America Laboratory, Data Science and System Security team, Princeton NJ, USA.
- 07/2022: AI TIME, Beijing, China.
- 05/2022: Tianjin University, Smart Society and Big Data Intelligence Lab, Tianjin, China.
- 11/2021: AI TIME, Beijing, China.
- 04/2015: Shaker High School, Latham NY, USA.
Teaching
Baylor University
- Fall 2024, Instructor, DSC 3334 Algorithms and Data Structures.
- Spring 2024, Instructor, DSC 4320 Data Visualization.
- Fall 2023, Instructor, DSC 2350 Discrete Structures for Data Science.
The State University of New York at Albany
- Fall 2023, Spring 2023, Spring 2022, Instructor, INF 108 Programming for Problem Solving.
- Spring 2024, Fall 2022, Spring 2022, Instructor, INF 428/528 Analysis, Visualization, and Prediction in Analytics.
- Spring 2019, Fall 2018, Spring 2018, Teaching Assistant, ICSI 500 Operating Systems.
- Fall 2017, Teaching Assistant, ICSI 518 Software Engineering.
- Fall 2016, Spring 2016, Fall 2015, Teaching Assistant, ICSI 201 Introduction of Computer Science.
The University of Texas at Dallas
- Summer 2021, Teaching Assistant, CS 6385 Algorithmic Aspects of Telecommunication Networks.
- Summer 2021, Teaching Assistant, CS 4365 Artificial Intelligence.
- Summer 2021, Lab Instructor, CS 1136 Computer Science Laboratory.
- Fall 2019, Co-Instructor, CS 6364 Artificial Intelligence.
- Fall 2019, Teaching Assistant, CS 6301 Convolutional Neural Network.
Albany Medical College
- Spring 2015, Spring 2014, Teaching Assistant, MCP 608 Cardiovascular Physiology.
- Spring 2015, Spring 2014, Teaching Assistant, MCP 609 Respiratory & Renal Physiology.
Awards & Honors
- Dissertation Research Award, $1,224, The University of Texas at Dallas, 2021-2022.
- Student Travel Award, IEEE International Conference on Data Mining (ICDM), U.S. National Science Foundation, 2020.
- First Prize in the 11th "Challenge Cup" National Undergraduate Academic Science and Technology, Tianjin Municipal Education Commission, Top 5% among all works in Tianjin City, 2011.