Dr. Weizheng Yan

AI/ML • Neuroimaging • HealthTech

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

Deep Learning • GenAIMachine LearningLLM • CNN • RNN • GAN • uNetPython • PyTorch • Tensorflow • MATLAB • C • R PET • sMRI • fMRI • EEG • EHRClassification • Prediction • Subtype • Biomarker • Synthesis • Domain Adaptation • VisualizationTime Sequence Modeling
Headshot

About

I am a postdoctoral fellow at the National Institutes of Health , where I develop advanced AI/ML methods for discovering biomarkers of mental disorders using MRI, PET, and EEG data. My research has been published in leading venues such as Biological Psychiatry, IEEE Transactions on Biomedical Engineering, IEEE Signal Processing Magazine , Journal of Neuroscience, EBioMedicine, and MICCAI.

I earned my PhD in Pattern Recognition & Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences in 2020, where I focused on deep learning models for brain imaging . As part of my doctoral training, I studied at the University of North Carolina at Chapel Hill , concentrating on deep learning methodologies for Alzheimer’s disease diagnosis.

Experiences

National Institutes of Health

Postdoctoral Fellow — National Institutes of Health

Dec 2022 – Present · Bethesda, MD
  • Developing Health Large Language Model (H-LLM) based on DeepSeek 8B model for personalized sleep and fitness coaching.
  • 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 Research Associate — 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.

Selected Projects

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

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

Docs
PET Kinetic Modeling Toolbox with GUI

PET Kinetic Modeling Toolbox with GUI

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

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

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 Discovery (fMRI and EEG)click to expand
  1. 1.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
  2. 2.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
  3. 3.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
  4. 4.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
  5. 5.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
  6. 6.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
  7. 7.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
  8. 8.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 Analysis (sMRI)click 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 Adolescents (fMRI)click 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 Studies (PET & MRI)click to expand
  1. 1.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
  2. 2.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
  3. 3.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
  4. 4.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
  5. 5.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
    DOI
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 Bethesda, MD