Dr. Minghan Chen portrait

Dr. Minghan Chen is an Associate Professor of Computer Science at Wake Forest University. Her research focuses on science-guided machine learning and AI for health and biomedical applications. She welcomes interdisciplinary collaborations and is recruiting motivated students in computational biology and machine learning. Interested applicants should email a brief background statement, CV, and transcripts.

Contact Email: chenm@wfu.edu
Office: Manchester 242
Phone: 336-758-3732

Research Overview

Our group develops robust machine learning and statistical methods to connect multi‑modal biomedical data (omics, imaging) with clinical outcomes. Current directions include: disease trajectory modeling, multi‑omics integration, foundation models for biomedical systems, and trustworthy AI for healthcare.

Featured Projects

Self-Learning Tutorials →

Multi‑Modal Data Integration

Predict Alzheimer’s disease onset and progression by combining neuroimaging, omics, and clinical features with deep learning algorithms.

Open project →

Graph Neural Operator

Design graph neural operators to model tau transport across brain and forecast tau spatio-temporal dynamics under different seeding conditions and biophysical parameters.

Explore the Tool →

MAMBA Brain Fingerprinting

Develop MAMBAxBrain framework to integrate Mamba with functional connectivity analysis for multi-task fMRI modeling, including brain fingerprinting, cognitive decoding, reaction-time prediction, and schizophrenia diagnosis.

AI Surrogate for Systems Biology

We build AI-based surrogate models to accelerate complex systems biology simulations. These surrogates reduce computational cost while maintaining predictive accuracy for pathway and regulatory network analysis.

Research Activities

Highlights from our students’ research, presentations, and summer workshops.

Student Presentations

Jason giving presentation at AAAI'25
Jason giving presentation at AAAI'25
Undergraduate presenting poster
Undergraduate presenting poster
Jingwen got BCB'22 Best Poster Award
Jingwen got BCB'22 Best Poster Award

Summer Workshops Organizer

workshop2024
Deep Learning and Applications (June, 2024)
workshop2024
Virtual Deep Learning Bootcamp (June, 2023)
workshop2022
Virtual Deep Learning Bootcamp (May, 2022)

Teaching

Selected Publications

  1. Rahman M., Wang G., Zhou K., Chen, M., Fan, Y. (2025). Catastrophic Forgetting in KANs. Proceedings of AAAI Conference on Artificial Intelligence.
  2. Chen, C., Xu, E., Yang, D., Yan, C., Wei, T., Chen, H., Wei, Y., & Chen, M. (2025). Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets. Neural Computing and Applications.
  3. Xu, C., Xu, E., Xiao, Y., Yang, D., Wu, G., & Chen, M. (2025). A multiscale model to explain the spatiotemporal progression of amyloid beta and tau pathology in Alzheimer's disease. International Journal of Biological Macromolecules.
  4. Rao, H., Gu, Y., Zhang, J.Z., Yu, G., Cao, Y., & Chen, M. (2025). Hierarchical Gradient-Based Genetic Sampling for Accurate Prediction of Biological Oscillations. Proceedings of AAAI Conference on Artificial Intelligence.
  5. Liu, S., Chen, M., Cao, X., Sheng, Z., Sun, J., Zhan, J., Wang, S., Wu, G., & Yang, D. (2025). A Joint Multi-Graph Learning Framework for Dynamic Functional Connectivity Network Estimation Across Multiple Time Scales. IEEE ISBI 2025.
  6. Yang, D., Shen, H., Chen, M., Wang, S., Chen, J., Cai, H., Chen, X., Wu, G., & Zhu, W. (2024). A novel spatio-temporal hub identification in brain networks by learning dynamic graph embedding on Grassmannian manifolds. IEEE Transactions on Medical Imaging.
  7. Zhu, W., Du, Z., Xu, Z., Yang, D., Chen, M., & Song, Q. (2024). SCRN: Single-cell Gene Regulatory Network Identification in Alzheimer’s Disease. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
  8. Yang, D., Shen, H., Chen, M., Xue, Y., Wang, S., Wu, G., & Zhu, W. (2023). Spatiotemporal Hub Identification in Brain Network by Learning Dynamic Graph Embedding on Grassmannian Manifold. MICCAI (Medical Image Computing and Computer-Assisted Intervention).
  9. Chen, M., Xu, C., Xu, Z., He, W., Zhang, H., Su, J., & Song, Q. (2022). Uncovering the dynamic effects of DEX treatment on lung cancer by integrating bioinformatic inference and multiscale modeling of scRNA-seq and proteomics data. Computers in Biology and Medicine.
  10. Xu, C., Hollis, H., Dai, M., Yao, X., Watson, L.T., Cao, Y., & Chen, M. (2022). Modeling the temporal dynamics of master regulators and CtrA proteolysis in Caulobacter crescentus cell cycle. PLoS Computational Biology.
  11. Chen, M., Ahmadian, M., Watson, L.T., & Cao, Y. (2020). Finding acceptable parameter regions of stochastic Hill functions for multisite phosphorylation mechanism. Journal of Chemical Physics.

Team

Current Group Members

  • Urmi Saha
  • Jason Zhang
  • Grayson Gooden
  • Ananya Rajgaria
  • Fiona Zhang

    • Former Students

    Yang Xiao, 2025 (Computer Science, University of Pennsylvania)
    Johnson Wang, 2024 (Computational Biology, Harvard University)
    Ruiwen Yang, 2024 (Data Science, University of Pennsylvania)
    Selina Zhang, 2023 (Computer Science, Harvard University)
    Ziang Xu, 2023 (Computer Science, Columbia University)
    Zhanyang Sun, 2023 (Data Science, University of Southern California)
    Enze Xu, 2023 (Computer Science, William & Mary College)
    Michelle Dai, 2022 (Computational Biology, Harvard University)
    Patrick Fan, 2022 (Computational Finance, Carnegie Mellon University)
    Laurent Zhang, 2022 (Data Science, New York University)
    Bess Morrell, 2022 (Independent Study)
    Hao Lin, 2022 (Summer research, Duke University)
    David Ding, 2022 (Computer Science, Rice University)
    Henry Hollis, 2021 (Biomedical Engineering, Drexel University)
    Alex Wei, 2020 (Computational Biology, Harvard University)