Zhong Chen

Zhong Chen
Assistant Professor in DS/ML/LLMs/Agentic-AI
School of Computing, Southern Illinois University (SIU), Carbondale, IL, United States

Office: Engineering Building
Address: 1230 Lincoln Dr, Carbondale, IL 62901 (MAIL CODE 4511)
Southern Illinois University (SIU), Carbondale, IL, United States

Emails: zhong.chen AT cs DOT siu DOT edu (or zchen2 AT kumc DOT edu or zchen AT xula DOT edu) 
Homepage: https://pursuerchen.github.io
Dirtector of LOAD Lab: Learning, Optimization, and Analysis from Data Lab (LOAD Lab). School of Computing at SIU: https://soc.siu.edu/ 
SIU department website: https://cs.siu.edu/faculty-staff/continuing_faculty.php

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Biography

I'm currently working as a tenure-track Assistant Professor (DS/ML/LLMs/Agentic-AI) in the School of Computing, Southern Illinois University (SIU), Carbondale, Illinois. I'm also the Director of Learning, Optimization, and Analysis from Data Lab (LOAD Lab) at School of Computing, SIU. Previously, I was working as a Visiting Assistant Professor in Department of Radiation Oncology, UT Southwestern Medical Center (UTSW), a Research Assistant Professor in Department of Radiation Oncology, University of Kansas Medical Center (KUMC), and a Computational Scientist in the Department of Computer Science, Xavier University of Louisiana (XULA).

My current research interests include Data-centric AI, Large Language Models (LLMs) and Applications, (Interpretable and Trustworthy) Machine Learning, Deep Learning, Big Data Mining, Online Optimization, Outlier/Anomaly/Novelty Detection, Multi-omics Data Integration, Multi-modal Learning, Interpretable Time Series Forecasting, and AI-related Applications. I have published over 90 peer-reviewed papers in scientific journals such as BioData Mining, Bioinformatics, Medical Physics, Physica Medica, IEEE Transactions on Computational Biology and Bioinformatics (TCBB), IEEE Transactions on Knowledge and Data Engineering (TKDE), Transactions on Machine Learning Research (TMLR), and Machine Learning (MLJ), as well as at top-tier international conferences including SDM'17'24'25, IEEE BigData'18'22'24, ICDM'22'25'26, IJCAI'25'26, AAAI'23'24'25'26, NeurIPS'25'26, CVPR'26, and ICML'26. I have received several honors, including the KDD'23 Excellent Reviewer Award, KDD'25 Outstanding Reviewer Award, IJCAI'25 Travel Grant, ICML'26 Gold Reviewer Award, and NeurIPS'26 Top Reviewer Award. Additionally, I have secured funding from the IIN Seed Grant ($40,000; Co-PI), the AHRF Richard G. Muench Chairman's Grant ($65,000; Co-PI), and the USDA NIFA Seed Grant ($300,000; PI). I have served as a panelist multiple times for the U.S. National Science Foundation (NSF) CISE directorate and the U.S. Department of Agriculture (USDA) NIFA. I was elevated to the grade of IEEE Senior Member (IEEE St. Louis Section) in August 2025.

Employment History

What's New

Honors

Funded Grants

Selected Publications


Representative Papers: 20 first-author and 23 co-author/corresponding author.
  • Conference Papers: SDM'17'24'25, IEEE BigData'18'22'24, ICDM'22'25'26, IJCAI'25'26, AAAI'23'24'25'26, NeurIPS'25'26, CVPR'26, and ICML'26.
  • Journal Papers: BioData Mining, Bioinformatics, Medical Physics, Physica Medica, IEEE Transactions on Computational Biology and Bioinformatics (TCBB), Knowledge and Information Systems (KAIS), International Journal of Machine Learning and Cybernetics (JMLC), IEEE Transactions on Knowledge and Data Engineering (TKDE), Transactions on Machine Learning Research (TMLR), and Machine Learning (MLJ).

    • ( * First  Author;  # Collaborative  Author)


      2026

    1. Beyond Buffer Limits: Energy-Based Data Reassembly for Continual Learning [PDF]
      Zhenyi Wang, Yixuan Sun, Yue Wang, Zhong Chen#, and Heng Huang
      The 43rd International Conference on Machine Learning (ICML 2026 Main Track) (ICML'26). (6,352/23,918=26.56%)

    2. Adaptive Bayesian Early-Exit Networks for Efficient Non-Transferable Learning [PDF]
      Siyu Luan, Yan Li, Zhong Chen#, and Zhenyi Wang
      The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026 Main Track) (CVPR'26). (Acceptance Rate 4,090/16,092=25.42%)

    3. An ensemble-based network architecture search framework for 3D dose prediction in radiotherapy [PDF]
      Yang Yan, Chen Zhao, Yi He, Zhenyi Wang, Hao Gao, and Zhong Chen#
      The 25th International Conference on Data Mining (ICDM 2026 Applied Track) (ICDM'26). (Acceptance Rate 393/1,966=19.99%)

    4. Face4FairShifts: A Large Image Benchmark for Fairness and Robust Learning across Visual Domains [PDF]
      Yumeng Lin, Dong Li, Xintao Wu, Minglai Shao, Xingcheng Fu, Xujiang Zhao, Zhong Chen#, Chen Zhao
      The 25th International Conference on Data Mining (ICDM 2026 Applied Track) (ICDM'26). (Acceptance Rate 393/1,966=19.99%)

    5. PURE: Purging Unrelated Representations for Content-Agnostic Deepfake Detection [PDF]
      Xinyu Wu, Dong Li, Minglai Shao, Xintao Wu, Zhong Chen#, and Chen Zhao
      The 35th International Joint Conference on Artificial Intelligence (IJCAI 2026 Main Track) (IJCAI'26). (Acceptance Rate 713/5,233=13.62%)

    6. LEMD: Latent Environment Extrapolation and Message Disentanglement for Dynamic Graph Under Distribution Shift [PDF]
      Xiaoran Wei, Chen Zhao, Minglai Shao, Xintao Wu, Zhong Chen#, Wenjun Wang, Qin Tian, and Chang Liu
      The 35th International Joint Conference on Artificial Intelligence (IJCAI 2026 Main Track) (IJCAI'26). (Acceptance Rate 713/5,233=13.62%)

    7. Learning Counterfactual Fairness from Authentic Generation [PDF]
      Zichong Wang, Zhipeng Yin, Zhong Chen#, Jack Yang, Jun Liu, and Wenbin Zhang
      The 35th International Joint Conference on Artificial Intelligence (IJCAI 2026 Main Track) (IJCAI'26). (Acceptance Rate 713/5,233=13.62%)

    8. Disentangled Graph-Enhanced Large Language Models for Fair Learning [PDF]
      Zhipeng Yin, Zichong Wang, Zhong Chen#, Jack Yang, Xin Ning, and Wenbin Zhang
      The 35th International Joint Conference on Artificial Intelligence (IJCAI 2026 Main Track) (IJCAI'26). (Acceptance Rate 713/5,233=13.62%)

    9. Multi-Modal Foundation Models for Computational Pathology: A Survey [PDF]
      Dong Li, Guihong Wan, Xintao Wu, Xinyu Wu, Xiaohui Chen, Yi He, Zhong Chen#, Peter Sorger, and Chen Zhao
      Transactions on Machine Learning Research (TMLR). 

    10. A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks [PDF]
      Dong Li, Guihong Wan, Xintao Wu, Xinyu Wu, Yi He, Zhong Chen#, Ajit J. Nirmal, Christine G. Lian, Peter K. Sorger, Yevgeniy R. Semnov, and Chen Zhao
      Knowledge and Information Systems (KAIS), 68(1), p.209, 2026. 

    11. MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery [PDF]
      Dong Li, Zhengzhang Chen, Xujiang Zhao, Zhong Chen#, Yi He, Haifeng Chen, and Chen Zhao
      The 40th Annual AAAI Conference on Artificial Intelligence (AAAI 2026 Main Track) (AAAI'26). (Acceptance Rate 4,167/23,680=17.60%)

    12. Fine-tuning Zero-shot Large Language Models for Patient-reported Outcomes (Student Abstract) [PDF]
      Yang Yan, Matthew W. Chen, Jiayi Lyu, Chen Zhao, Hao Gao, and Zhong Chen#
      The 40th Annual AAAI Conference on Artificial Intelligence (AAAI 2026 Student Abstract Track) (AAAI'26). 


      13. 2025

    13. Efficient proton-photon patient selection via dose and NTCP prediction for head-and-neck cancer patients [PDF]
      Jiaxin Li, Yanan Zhu, Li Lyu, Zhong Chen#, Fazal Khan, Wangyao Li, Chao Wang, Gregory N Gan, Christoper Lominska, Qiang Li, Weiqiang Chen, Hao Gao, and Yuting Lin
      The International Journal of Medical Physics Research and Practice (Medical Physics), 52(12), p.e70183, 2025. 

    14. A few-shot U-net learning framework for fast and accurate three-dimensional dose prediction in radiotherapy [PDF]
      Zhong Chen*, Wangyao Li, Xinglei Shen, Ronald Chen, Yuting Lin, and Hao Gao
      European Journal of Medical Physics (Physica Medica), 2025. 

    15. SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search [PDF]
      Dong Li, Xujiang Zhao, Linlin Yu, Yanchi Liu, Wei Cheng, Zhengzhang Chen, Zhong Chen#, Feng Chen, Chen Zhao, and Haifeng Chen
      The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS'25), San Diego, CA, 2025. (Acceptance Rate 5,290/21,575=24.52%)

    16. Decoding Ancestry-Specific Genetic Risk: Interpretable Deep Feature Selection Reveals Prostate Cancer SNP Disparities in Diverse Populations [PDF]
      Zhong Chen*, Zichen Lao, You Lu, Wensheng Zhang, Andrea Edwards, and Kun Zhang
      BioData Mining, 2025. 

    17. Online outlier detection in open feature spaces [PDF]
      Heng Lian, Yi He, Di Wu, Zhong Chen#, Xingquan Zhu, and Xindong Wu
      IEEE Transactions on Knowledge and Data Engineering (TKDE), 37(10), pp. 6091-6106, 2025. 

    18. Short- and Long-term Weekly Patient-reported Outcomes Prediction Undergoing Radiotherapy: Single-patient Time Series Model vs. Transformers-based Multi-patient Time Series Model [PDF]
      Yang Yan, Zhong Chen#, Xinglei Shen, Ronald C Chen, and Hao Gao
      BioData Mining, 18(1), p.53, 2025. 

    19. A Novel Sparse Active Online Learning Framework for Fast and Accurate Streaming Anomaly Detection over Data Streams [PDF]
      Zhong Chen*, Yi He, Di Wu, Chen Zhao, and Meikang Qiu
      Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25), pp. 2740-2748, 2025. (Regular Paper, Acceptance Rate 1,023/5,806=17.62%)

    20. Distributed Collaborative Learning with Representative Knowledge Sharing [PDF]
      Joseph Casey, Qianjiao Chen, Mengchen Fan, Baocheng Geng, Roman Shterenberg, Zhong Chen#, and Keren Li
      Mathematics, 13(6): 1004, 2025. 

    21. $\ell_{1,\infty}$-Mixed Norm Promoted Row Sparsity for Fast Online CUR Decomposition Learning in Varying Feature Spaces [PDF]
      Zhong Chen*, Yi He, Di Wu, Wenbin Zhang, and Zhiqiang Deng
      Proceedings of the 2025 SIAM International Conference on Data Mining (SDM'25), Alexandria, Virginia, 2025. (Regular Paper, Acceptance Rate 61/228=26.7%)

    22. Metric-Agnostic Continual Learning for Sustainable Group Fairness [PDF]
      Heng Lian, Chen Zhao, Zhong Chen#, Xingquan Zhu, My T. Thai, and Yi He
      The 39th AAAI Conference on Artificial Intelligence (AAAI'25), Philadelphia, PA, 2025. (Regular Paper, Acceptance Rate 3,032/12,957=23.4%)


      23. 2024

    23. Accounting for Cancer Patients with Severe Outcomes: An Anomaly Detection Perspective [PDF]
      Yang Yan, Christopher Lominska, Gregory N Gan, Hao Gao, and Zhong Chen#
      IEEE International Conference on Big Data (IEEE BigData'24), pp. 8225-8227, 2024. 

    24. $\ell_{1,2}$-Norm and CUR Decomposition based Sparse Active Online Learning for Data Streams with Streaming Features [PDF]
      Zhong Chen*, Yi He, Di Wu, Liudong Zuo, Keren Li, Wenbin Zhang, and Zhiqiang Deng
      IEEE International Conference on Big Data (IEEE BigData'24), pp. 374-383, 2024. (Regular Paper, Acceptance Rate 124/660=18.8%)

    25. Advancing Graph Counterfactual Fairness through Fair Disentangled Representation [PDF]
      Zichong Wang, Zhibo Chu, Ronald Blanco, Zhong Chen#, Shu-Ching Chen, and Wenbin Zhang
      European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'24), pp. 40-58, 2024. (Regular Paper, Acceptance Rate 198/826=24.0%)

    26. Individual Fairness with Group Awareness under Uncertainty [PDF]
      Zichong Wang, Xiaoyong Yuan, Zhong Chen#, Yanzhao Wu, Xin Yao, and Wenbin Zhang
      European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'24), pp. 89-106, 2024. (Regular Paper, Acceptance Rate 198/826=24.0%)

    27. Robust Sparse Online Learning for Data Streams with Streaming Features [PDF]
      Zhong Chen*, Yi He, Di Wu, Huixin Zhan, Victor Sheng, and Kun Zhang
      Proceedings of the 2024 SIAM International Conference on Data Mining (SDM'24), Houston, United States, pp. 181-189, 2024. (Regular Paper, Acceptance Rate 98/416=23.6%)

    28. Cost-sensitive Sparse Group Online Learning for Imbalanced Data Streams [PDF]
      Zhong Chen*, Victor Sheng, Andrea Edwards, and Kun Zhang
      Machine Learning (MLJ), 113, pp. 4407-4444, 2024. 


      29. 2023

    29. Defending the Graph Reconstruction Attacks for Simplicial Neural Networks [PDF]
      Huixin Zhan, Kun Zhang, Zhong Chen#, and Victor Sheng
      The 10th IEEE International Conference on Data Science and Advanced Analytics (DSAA'23), Thessaloniki, Greece, 2023. (Special Session Paper, Acceptance Rate 12/35=34.3%)

    30. Online Semi-supervised Learning with Mix-Typed Streaming Features [PDF]
      Di Wu, Shengda Zhuo, Yu Wang, Zhong Chen#, and Yi He
      The 37th AAAI Conference on Artificial Intelligence (AAAI'23), Washington DC, United States, 2023. (Regular Paper, Acceptance Rate 1,721/8,777=19.6%)

    31. An Effective Cost-sensitive Sparse Online Learning Framework for Imbalanced Streaming Data Classification and Its Application to Online Anomaly Detection [PDF]
      Zhong Chen*, Victor Sheng, Andrea Edwards, and Kun Zhang
      Knowledge and Information Systems (KAIS), 65(1), pp. 59–87, 2023. 


      32. 2022

    32. Proximal Cost-sensitive Sparse Group Online Learning [PDF]
      Zhong Chen*, Huixin Zhan, Victor Sheng, Andrea Edwards, and Kun Zhang
      The 2022 IEEE International Conference on Big Data (IEEE Big Data'22), pp. 495-504, Osaka, Japan, 2022. (Regular Paper, Acceptance Rate 122/633=19.3%)

    33. Projection Dual Averaging based Second-order Online Learning [PDF]
      Zhong Chen*, Huixin Zhan, Victor Sheng, Andrea Edwards, and Kun Zhang
      The 22nd IEEE International Conference on Data Mining (ICDM'22), pp. 51-60, Orlando, Florida, United States, 2022. (Regular Paper, Acceptance Rate 85/885=9.6%)

    34. Driver Gene Detection through Bayesian Network Integration of Mutation and Expression Profiles [PDF]
      Zhong Chen*, You Lu, Bo Cao, Wensheng Zhang, Andrea Edwards, and Kun Zhang
      Bioinformatics, 38(10), pp. 2781–2790, 2022. 

    35. Effective Cancer Subtype and Stage Prediction via Dropfeature-DNNs [PDF]
      Zhong Chen*, Wensheng Zhang, Hongwen Deng, and Kun Zhang
      IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 19(1), pp. 107-120, 2022. 


      36. 2021

    36. CSRDA: Cost-sensitive Regularized Dual Averaging for Handling Imbalanced and High-dimensional Streaming Data [PDF]
      Zhong Chen*, Zhide Fang, Victor Sheng, Andrea Edwards, and Kun Zhang
      The 12th IEEE International Conference on Big Knowledge (ICBK'21), pp. 164-173, Auckland, New Zealand, 2021. (Regular Paper)

    37. A Deep Imputation and Inference Framework for Estimating Personalized and Race-specific Causal Effects of Genomic Alterations on PSA [PDF]
      Zhong Chen*, Bo Cao, Andrea Edwards, Hongwen Deng, and Kun Zhang
      Journal of Bioinformatics and Computational Biology (JBCB), 19(4), p.2150016, 2021. 

    38. Adaptive Robust Local Online Density Estimation for Streaming Data [PDF]
      Zhong Chen*, Zhide Fang, Victor S Sheng, Jiabin Zhao, Wei Fan, Andrea Edwards, and Kun Zhang
      International Journal of Machine Learning and Cybernetics (JMLC), 12(6), pp. 1803-1824, 2021. 


      39. 2020

    39. Fusion Lasso and Its Applications to Cancer Subtype and Stage Prediction [PDF]
      Zhong Chen*, Andrea Edwards, and Kun Zhang
      The 11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB'20), Virtual, pp. 1-8, 2020. (Short Paper, Acceptance Rate 17/130=13.1%)

    40. Inferring Personalized and Race-specific Causal Effects of Genomic Aberrations on Gleason Scores: A Deep Latent Variable Model [PDF]
      Zhong Chen*, Andrea Edwards, Chindo Hicks, and Kun Zhang
      Frontiers in Oncology (FIO), 10, pp. 272, 2020. 


      41. 2019

    41. Learning Discriminative Subregions and Pattern Orders for Facial Gender Classification [PDF]
      Zhong Chen*, Andrea Edwards, Yongsheng Gao, and Kun Zhang
      Image and Vision Computing (IVC), 89, pp. 144-157, 2019. 


      42. 2018

    42. Online Density Estimation over Streaming Data: A Local Adaptive Solution [PDF]
      Zhong Chen*, Zhide Fang, Jiabin Zhao, Wei Fan, Andrea Edwards, and Kun Zhang
      2018 IEEE International Conference on Big Data (BigData'18), pp. 201-210, Seattle, Washington, United States, 2018. (Regular Paper, Acceptance Rate 98/518=19.7%)


      43. 2017

    43. CSTG: An Effective Framework for Cost-sensitive Sparse Online Learning [PDF]
      Zhong Chen*, Zhide Fang, Wei Fan, Andrea Edwards, and Kun Zhang
      The 2017 SIAM International Conference on Data Mining (SDM'17), pp. 759-767, Houston, Texas, United States, 2017. (Regular Paper, Acceptance Rate 93/358=26.0%)

    Academic Activities

    Journal Review Experience

    Conference Review Experience (Reviewer/PC Member)

    Invited Talks

    Teaching in Computer Science at SIU

    Service of Computer Science Committee at SIU

    Meta-Reviewer/Area Chair

    Social Media Chair

    Professional Societies

    Research Team at SIU

    External Links