Dr. Weizheng Yan

AI/ML • Medical Imaging • HealthTech

AI-driven neuroimaging researcher with high-impact publications and patents, committed to turning AI breakthroughs into real-world healthcare solutions.

AI/ML • GenAIsys • LLMCNN • RNN • GAN • uNetPython • PyTorch • Tensorflow • MATLAB • C • R Studio PET • sMRI • fMRI • EEG • EHRClassification • Prediction • Subtype • Biomarker • Synthesis • Domain Adaptation • VisualizationTime Sequence Modeling
Headshot

About

I am a Senior Research Scientist working at Stony Brook University, where I develop advanced AI/ML methods for biomarker discovery and subtyping of mental disorders using MRI, PET, and EEG. Before joining Stony Brook Medicine, I was a postdoctoral fellow at the National Institutes of Health, where I conducted research on multimodal neuroimaging, brain dynamics, and AI-based biomarker discovery for mental disorders. My research has been published in leading journals and conferences, including IEEE Signal Processing Magazine, Biological Psychiatry, EBioMedicine, IEEE Transactions on Biomedical Engineering, Journal of Neuroscience, and MICCAI.

I received my Ph.D. in Pattern Recognition & Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences in 2020, focusing on deep learning models for medical imaging. As part of my doctoral training, I studied at the University of North Carolina at Chapel Hill, developing deep learning methodologies for Alzheimer’s disease diagnosis.

Experiences

Stony Brook Medicine

Senior Research Scientist — Stony Brook Medicine

May 2026 – Present · Stony Brook, NY
    National Institutes of Health

    Postdoctoral Fellow — National Institutes of Health

    Dec 2022 – Dec 2025 · Bethesda, MD
    • Led a cross-radiotracer project by developing deep generative and interpretable AI models for multi-tracer PET image synthesis, leveraging GPU-based NIH High-performance computing (HPC) systems for large-scale neuroimaging analysis.
    • Led a multimodal neuroimaging project integrating MRI, PET, and behavioral data to study drug-induced brain dynamics.
    • Independently developed and deployed a GUI-based PET kinetic modeling toolbox, streamlining data processing and enabling efficient large-scale PET studies.
    • Mentored postbaccalaureate fellows and interns in statistical programming (Python, R, MATLAB) and applied AI methods for biomedical research.
    Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)

    Postdoctoral Researcher — Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)

    Jun 2021 – Nov 2022 · Atlanta, GA
    • Developed domain-adaptive generative adversarial network (GAN) frameworks for multi-site magnetic resonance imaging (MRI) data harmonization, with validation conducted on a large-scale cohort of 10,000+ scans.
    • Proposed and validated the Brain-Wide Risk Score (BRS), a data-driven predictive metric for quantifying psychiatric risk in adolescent populations.
    • Developed interpretable deep learning pipelines for medical image classification, subtype clustering, and biomarker discovery in psychiatric disorders.
    • Developed and deployed a AI-system for mental disorder diagnosis based on EEG.

    Selected Projects

    Brain-Wide Risk Score (BRS): Predicting Psychiatric Risks in Adolescents

    Brain-Wide Risk Score (BRS): Predicting Psychiatric Risks in Adolescents

    Docs
    Drug-Effects Study: How 'Smart Drugs' Affect Our Brain

    Drug-Effects Study: How 'Smart Drugs' Affect Our Brain

    CodeDocs
    Generative Adversarial Networks for Domain Adaptation

    Generative Adversarial Networks for Domain Adaptation

    CodeDocs
    Recurrent Neural Network for Time Series Classification and Biomarker Identification

    Recurrent Neural Network for Time Series Classification and Biomarker Identification

    Docs
    Deep Clustering for Mental Disorder Subtype Discovery

    Deep Clustering for Mental Disorder Subtype Discovery

    Docs
    Deep Generative Model for Cross-Radiotracer PET synthesis

    Deep Generative Model for Cross-Radiotracer PET synthesis

    Docs
    PET Kinetic Modeling Toolbox with GUI

    PET Kinetic Modeling Toolbox with GUI

    CodeDocs

    Publications & Patents (Chronological Order)

    Book Chapters & Review Papers in AI/ML and Neuroimagingclick to expand
    1. 1.Deep learning-based approaches in MRI: From discriminative to generative (Chapter 8)
      Yan, W.
      The Chronnectome: Mapping the dynamics of brain function. Elsevier, in press
      PDF
    2. 2.Deep learning in neuroimaging: Promises and challenges
      Yan, W., Qu, G., Hu, W., Abrol, A., Cai, B., Qiao, C., Plis, S. M., Wang, Y. P., Sui, J., & Calhoun, V. D.
      IEEE Signal Processing Magazine, 2022
      DOIPDF
    3. 3.Brain imaging-based machine learning in autism spectrum disorder: methods and applications
      Xu, M., Calhoun, V., Jiang, R., Yan, W., & Sui, J.
      Journal of Neuroscience Methods, 2021
      DOIPDF
    AI/ML Methods for Classification, Subtype and Biomarker Discoveryclick to expand
    1. 1.MVF-XT: An interpretable multi-view fusion network based on cross-attention for fMRI analysis
      Fan, X., Li, J., Huang, H., Li, G., Zhang, W., Hu, Y., Sun, W., Yan, W., Manza, P., Volkow, N. D., Wang, G.-J., & Zhang, Y.
      Neurocomputing, 2026
      DOI
    2. 2.Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG
      Yan, W., Yu, L., Liu, D., Sui, J., Calhoun, V. D., & Lin, Z.
      Frontiers in Psychiatry, 2023
      DOIPDF
    3. 3.Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series
      Yan, W., Zhao, M., Fu, Z., Pearlson, G. D., Sui, J., & Calhoun, V. D.
      Schizophrenia Research, 2022
      DOIPDF
    4. 4.An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data
      Zhao, M., Yan, W., Luo, N., Zhi, D., Fu, Z., Du, Y., Yu, S., Jiang, T., Calhoun, V. D., & Sui, J.
      Medical Image Analysis, 2022
      DOIPDF
    5. 5.An attention-based hybrid deep learning framework integrating temporal coherence and dynamics for discriminating schizophrenia
      Zhao, M., Yan, W., Xu, R., Zhi, D., Jiang, R., Jiang, T., Calhoun, V. D., & Sui, J.
      IEEE ISBI, 2021
      DOIPDF
    6. 6.Functional network connectivity (FNC)-based GAN and its applications in classification of mental disorders
      Zhao, J., Huang, J., Zhi, D., Yan, W., Ma, X., Yang, X., Li, X., Ke, Q., Jiang, T., Calhoun, V. D., & Sui, J.
      Journal of Neuroscience Methods, 2020
      DOIPDF
    7. 7.Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site fMRI data
      Yan, W., Calhoun, V., Song, M., Cui, Y., Yan, H., Liu, S., Fan, L., Zuo, N., Yang, Z., Xu, K., Yan, J., Lv, L., Chen, J., Chen, Y., Guo, H., Li, P., Lu, L., Wan, P., Wang, H., ... Sui, J.
      EBioMedicine, 2019
      DOIPDF
    8. 8.Deep chronnectome learning via full bidirectional long short-term memory networks for MCI diagnosis
      Yan, W., Zhang, H., Sui, J., Shen, D.
      MICCAI, 2018
      DOIPDF
    9. 9.Discriminating schizophrenia from normal controls using resting state functional network connectivity
      Yan, W., Plis, S., Calhoun, V., Liu, S., Jiang, R., Jiang, T. Z., & Sui, J.
      IEEE MLSP, 2017
      DOIPDF
    Domain Adaptation Methods for Large-Scale Dataset Analysisclick to expand
    1. 1.CGDM-GAN: An adversarial network approach with self-supervised learning for site effect removal
      Cui, X., Zhi, D., Yan, W., Calhoun, V. D., Zhuo, C., & Sui, J.
      IEEE EMBC, 2024
      DOIPDF
    2. 2.Maximum classifier discrepancy generative adversarial network for jointly harmonizing scanner effects and improving reproducibility
      Yan, W., Fu, Z., Jiang, R., Sui, J., & Calhoun, V. D.
      IEEE Transactions on Biomedical Engineering, 2023
      DOIPDF
    3. 3.'Harmless' adversarial network harmonization approach for removing site effects and improving reproducibility
      Yan, W., Fu, Z., Sui, J., & Calhoun, V. D.
      IEEE EMBC, 2022
      DOIPDF
    Disease-Risk Prediction in Adolescentsclick to expand
    1. 1.A brainwide risk score for psychiatric disorder evaluated in a large adolescent population
      Yan, W., Pearlson, G. D., Fu, Z., Li, X., Iraji, A., Chen, J., Sui, J., Volkow, N. D., & Calhoun, V. D.
      Biological Psychiatry, 2024
      DOIPDF
    2. 2.Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study
      Zhi, D., Jiang, R., Pearlson, G., Fu, Z., Qi, S., Yan, W., Feng, A., Xu, M., Calhoun, V. D., & Sui, J.
      Biological Psychiatry, 2024
      DOIPDF
    Psychopharmacology & Clinical Studiesclick to expand
    1. 1.Methylphenidate reorganizes cortical hierarchy through dopaminergic modulation
      Tomasi, D., Manza, P., Demiral, Ş. B., Yan, W., Miller, K. B., Veenker, F., Zhao, J., Lildharrie, C., Yonga, M.-V., Abey, S., VanDine, M., Wang, G.-J., & Volkow, N. D.
      Nature Communications, 2025
      DOIPDF
    2. 2.Methylphenidate promotes a frontoparietal-dominant brain state improving cognitive performance: a randomized trial
      Yan, W., Demiral, Ş. B., Tomasi, D., Zhang, R., Manza, P., Wang, G. J., & Volkow, N. D.
      Journal of Neuroscience, 2025
      DOIPDF
    3. 3.Rest-Activity Rhythms, Their Modulators, and Brain-Clinical Correlates in Opioid Use Disorder
      Zhang, R., Manza, P., Demiral, S. B., Tomasi, D., Yonga, M.-V., Yan, W., Shokri-Kojori, E., Schwandt, M., Vines, L., Sotelo, D., Lildharrie, C., Lin, E., Giddens, N. T., Wang, G.-J., & Volkow, N. D.
      JAMA Network Open, 2025
      DOIPDF
    4. 4.Sleep deprivation effects on brain state dynamics are associated with dopamine d2 receptor availability via network control theory
      Zhang, R., Demiral, S. B., Tomasi, D., Yan, W., Manza, P., Wang, G. J., & Volkow, N. D.
      Biological Psychiatry, 2025
      DOIPDF
    5. 5.Disrupted brain state dynamics in opioid and alcohol use disorder: attenuation by nicotine use
      Zhang, R., Yan, W., Manza, P., Shokri-Kojori, E., Demiral, S. B., Schwandt, M., ... Volkow, N. D.
      Neuropsychopharmacology, 2024
      DOIPDF
    6. 6.Examining the role of dopamine in methylphenidate's effects on resting brain function
      Tomasi, D., Manza, P., Yan, W., Shokri-Kojori, E., Demiral, Ş. B., Yonga, M. V., ... Volkow, N. D.
      Proceedings of the National Academy of Sciences, 2023
      DOIPDF
    7. 7.A fast online questionnaire for screening mental illness symptoms during the COVID-19 pandemic
      Chen, F., Yan, W. (co-first author), Calhoun, V. D., Yu, L., Chen, L., Hao, X., & Zheng, L.
      Translational Psychiatry, 2022
      DOIPDF
    Patentsclick to expand
    1. 1.A method and device for predicting the therapeutic effect of transcranial magnetic stimulation based on deep learning EEG signals
      Leilei Zheng, Jihan Fu, Zheng Lin, Weizheng Yan
      China Patent, 2024
      PDF
    2. 2.System and method for mental diagnosis using EEG
      Weizheng Yan, Vince D. Calhoun, Jing Sui
      US Patent, 2022
      PDF
    3. 3.A classification method/system based on functional magnetic resonance imaging
      Weizheng Yan, Jing Sui
      China Patent, 2019
      PDF
    4. 4.Method and system for predicting infant brain age based on resting-state functional magnetic resonance imaging
      Weizheng Yan, Jing Sui
      China Patent, 2019
      PDF

    Contact

    Email: conanywz@gmail.com · Based in Stony Brook, NY