Jay Shah

Machine Learning Engineer at PathAI · Ph.D., ASU

Jay Shah — Machine Learning Engineer and AI Researcher jgshah1@asu.edu
in/shahjay22
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Curriculum Vitae

I'm a Machine Learning Engineer III at PathAI, developing AI-powered pathology solutions to improve patient outcomes. I earned my Ph.D. in Computer Science from the Wu Lab at Arizona State University, co-advised by Dr. Teresa Wu and Dr. Baoxin Li.

My research spans Generative AI, Deep Learning, and Medical Imaging. During my Ph.D., I developed AI methods for the early detection of brain disorders — Alzheimer's Disease and headache — including:

This work was conducted in collaboration with Mayo Clinic, Banner Alzheimer's Institute, and Barrow Neurological Institute in Arizona.

I also host the Jay Shah Podcast, where I interview AI engineers, researchers, and practitioners about their journeys and advice for newcomers to the field.

Game of Life ·

Research

Publications

Selected work; see Google Scholar for the full and most current list. Bold denotes my name.

  1. Ordinal Classification with Distance Regularization for Robust Brain Age Prediction

    J. Shah, M. M. R. Siddiquee, Y. Su, T. Wu, B. Li. Proc. IEEE/CVF WACV, 2024. paper arXiv pdf code

  2. AUCp: Pseudo-AUC for Inference Model Selection with Unlabeled Validation Data in Abnormality Detection

    M. M. R. Siddiquee, F. Rafsani, J. Shah, T. Wu, C. D. Chong, T. J. Schwedt, B. Li. IEEE Trans. Medical Imaging, 2026. paper pdf code

  3. Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images

    M. M. R. Siddiquee, J. Shah, T. Wu, C. D. Chong, T. J. Schwedt, G. Dumkrieger, S. Nikolova, B. Li. Proc. IEEE/CVF WACV, 2024. paper arXiv pdf code

  4. Leveraging Multi-modal Foundation Model Image Encoders to Enhance Brain MRI-based Headache Classification

    F. Rafsani, D. Sheth, Y. Che, J. Shah, M. M. R. Siddiquee, C. D. Chong, S. Nikolova, K. Ross, G. Dumkrieger, B. Li, T. Wu, T. J. Schwedt. Scientific Reports, 2025. paper pdf

  5. Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models

    J. Shah, Y. Che, J. Sohankar, B. Li, Y. Su, T. Wu. Life, 2024. paper preprint pdf code

  6. AnoFPDM: Anomaly Detection with Forward Process of Diffusion Models for Brain MRI

    Y. Che, F. Rafsani, J. Shah, M. M. R. Siddiquee, T. Wu. Proc. IEEE/CVF WACV Workshops, 2025. paper arXiv pdf code

    7. Show all publications

  7. Traumatic Brain Injury Recovery Prediction by Harmonizing Real Brain CT and Synthetic Brain MRI: A Pilot Study

    Y. Che, A. Joshi, J. Shah, M. M. R. Siddiquee, C. D. Chong, S. Nikolova, G. Dumkrieger, B. Li, T. Wu, T. J. Schwedt. Brain Communications, 2026. paper pdf

  8. DinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classification

    F. Rafsani, J. Shah, C. D. Chong, T. J. Schwedt, T. Wu. Proc. IEEE/CVF ICCV Workshops, 2025. arXiv pdf

  9. HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease

    M. M. R. Siddiquee, J. Shah, T. Wu, C. D. Chong, T. J. Schwedt, B. Li. SASHIMI Workshop, MICCAI, 2022. paper arXiv pdf code

  10. Headache Classification and Automatic Biomarker Extraction from Structural MRIs Using Deep Learning

    M. M. R. Siddiquee, J. Shah, C. D. Chong, S. Nikolova, G. Dumkrieger, B. Li, T. Wu, T. J. Schwedt. Brain Communications, 2023. paper pdf

  11. Interpretable Deep Learning Framework for Understanding Molecular Changes in Human Brains with Alzheimer's Disease: Implications for Microglia Activation and Sex Differences

    M. R. Trivedi, A. Joshi, J. Shah, B. P. Readhead, M. A. Wilson, Y. Su, E. M. Reiman, T. Wu, Q. Wang. npj Aging, 2025. paper bioRxiv pdf

  12. Predicting Cognitive Decline from Neuropsychiatric Symptoms and Alzheimer's Disease Biomarkers: A Machine Learning Approach to Population-Based Data

    J. Shah, J. Krell-Roesch, E. Forzani, D. S. Knopman, C. R. Jack Jr., R. C. Petersen, Y. Che, T. Wu, Y. E. Geda. J. Alzheimer's Disease, 2025. paper pdf

  13. Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective

    J. Shah, M. M. R. Siddiquee, J. Krell-Roesch, J. A. Syrjanen, W. Kremers, M. Vassilaki, E. Forzani, T. Wu, Y. E. Geda. J. Alzheimer's Disease, 2023. paper pdf

  14. Physical Activity and the Outcome of Cognitive Trajectory: A Machine Learning Approach

    B. Barisch-Fritz, J. Shah, J. Krafft, Y. E. Geda, T. Wu, A. Woll, J. Krell-Roesch. Eur. Review of Aging and Physical Activity, 2025. paper pdf code

  15. Deep Residual Inception Encoder-Decoder Network for Amyloid PET Harmonization

    J. Shah, F. Gao, V. Ghisays, J. Luo, Y. Chen, W. Lee, Y. Zhou, T. Benzinger, E. M. Reiman, K. Chen, Y. Su, T. Wu. Alzheimer's & Dementia, 2022. paper pdf code

Conference Abstracts

  1. Using Large-scale Contrastive Language-Image Pre-training to Maximize Brain MRI-based Headache Classification

    F. Rafsani, D. Sheth, Y. Che, J. Shah, M. M. R. Siddiquee, C. D. Chong, S. Nikolova, G. Dumkrieger, B. Li, T. Wu, T. J. Schwedt. American Academy of Neurology Annual Meeting, 2025. paper

  2. Capturing MRI Signatures of Brain Age as a Potential Biomarker to Predict Persistence of Post-traumatic Headache

    J. Shah, M. M. R. Siddiquee, C. D. Chong, T. J. Schwedt, J. Li, V. Berisha, K. Ross, T. Wu. American Academy of Neurology Annual Meeting, 2024. paper

  3. Applying Generative Adversarial Networks on Structural Brain MRI for Unsupervised Classification of Headache

    M. M. R. Siddiquee, J. Shah, T. J. Schwedt, C. D. Chong, B. Li, T. Wu. American Academy of Neurology Annual Meeting, 2024. paper

  4. Prediction of Headache Improvement Using Multimodal Machine Learning in Patients with Acute Post-traumatic Headache

    A. Joshi, M. M. R. Siddiquee, J. Shah, T. J. Schwedt, C. D. Chong, B. Li, T. Wu. American Academy of Neurology Annual Meeting, 2024. paper

  5. A Multi-class Deep Learning Model to Estimate Brain Age While Addressing Systematic Bias of Regression to the Mean

    J. Shah, J. Luo, J. Sohankar, E. M. Reiman, K. Chen, Y. Su, B. Li, T. Wu. Alzheimer's Association International Conference, 2023. paper pdf

  6. Interpretable Deep Learning Framework Towards Understanding Molecular Changes Associated with Neuropathology in Human Brains with Alzheimer's Disease

    A. Joshi, J. Shah, B. P. Readhead, Y. Su, T. Wu, Q. Wang. Alzheimer's Association International Conference, 2023. paper pdf

    7. Show all abstracts

  7. A 2.5D Residual U-Net for Improved Amyloid Harmonization Preserving Spatial Information

    J. Shah, J. Sohankar, J. Luo, Y. Chen, S. Li, H. D. Protas, K. Chen, E. M. Reiman, B. Li, T. Wu, Y. Su. Alzheimer's Association International Conference, 2023. paper pdf

  8. End-to-End 3D CycleGAN Model for Amyloid PET Harmonization

    X. Dong, Y. Wang, J. Shah, V. Ghisays, J. Luo, Y. Chen, W. Lee, B. Li, K. Chen, E. M. Reiman, T. Wu, Y. Su. Alzheimer's Association International Conference, 2024. paper pdf

  9. Classification and Biomarker Discovery of Persistent Post-traumatic Headache (PPTH) Using Deep Learning on Structural Brain MRI Data

    M. M. R. Siddiquee, J. Shah, T. J. Schwedt, C. D. Chong, S. Nikolova, G. Dumkrieger, K. Ross, V. Berisha, J. Li, T. Wu. INFORMS Annual Meeting, 2022. paper

  10. Participant-specific Interrogation of Population-based Data to Predict Cognitive Decline from Neuropsychiatric Symptoms and Neuroimaging Biomarkers: A Machine Learning Approach

    J. Shah, J. A. Syrjanen, J. Krell-Roesch, W. Kremers, P. Vemuri, M. Vassilaki, R. C. Petersen, E. Forzani, T. Wu, Y. E. Geda. American Academy of Neurology Annual Meeting, 2023. paper pdf

  11. MRI Signatures of Brain Age in the Alzheimer's Disease Continuum

    J. Shah, V. Ghisays, Y. Chen, J. Luo, B. Li, E. M. Reiman, K. Chen, T. Wu, Y. Su. Alzheimer's Association International Conference, 2022. paper pdf

  12. Transfer Learning Based Deep Encoder-Decoder Network for Amyloid PET Harmonization with Small Datasets

    J. Shah, K. Chen, E. M. Reiman, B. Li, T. Wu, Y. Su. Alzheimer's Association International Conference, 2022. paper pdf

  13. Classification of Post-Traumatic Headache (PTH) Using Deep Learning on Structural Brain MRI Data

    M. M. R. Siddiquee, J. Shah, T. J. Schwedt, C. D. Chong, S. Nikolova, G. Dumkrieger, K. Ross, V. Berisha, J. Li, T. Wu. American Headache Society Annual Meeting, 2022. paper pdf

  14. Migraine Classification Using Deep Learning on Structural Brain MRI Data

    M. M. R. Siddiquee, J. Shah, T. J. Schwedt, C. D. Chong, S. Nikolova, G. Dumkrieger, K. Ross, V. Berisha, J. Li, T. Wu. American Headache Society Annual Meeting, 2022. paper pdf

  15. Interpreting Deep Learning Model Predictions Using Shapley Values

    J. Shah, C. D. Chong, T. J. Schwedt, V. Berisha, J. Li, K. Ross, G. Dumkrieger, J. Zhang, N. Gaw, S. Nikolova, T. Wu. INFORMS Annual Meeting, 2021. pdf

  16. Deep Residual Inception Encoder-Decoder Network for Amyloid PET Harmonization

    J. Shah, V. Ghisays, J. Luo, Y. Chen, W. Lee, B. Li, T. Benzinger, E. M. Reiman, K. Chen, Y. Su, T. Wu. Alzheimer's Association International Conference, 2021. paper pdf

Patents

Work Experience

News and Highlights

  • I will be joining PathAI as a Machine Learning Engineer III
  • Successfully defended my Ph.D. thesis on "Novel Deep Learning techniques for Early Detection of Neurological Disorders"  slides thesis
  • Invited lecture on "Novel Deep Learning techniques for Early Detection of Neurological Disorders"
    • Stephen and Denise Adams Center for Parkinson's Disease, Yale University
  • Invited talk on "AI for Early Detection of Alzheimer’s Disease"  link
    • AI Club, DAIICT
  • AI-powered medicine  article full magazine
    • Thrive magazine-summer 2024, Arizona State University
  • College Enrollment, Jobs, Medical Research, AGI and Consciousness with Dr. Jay Shah   link
  • Capturing MRI Signatures of Brain Age as a Potential Biomarker to Predict Persistence of Post-traumatic Headache  slides link
    • Oral presentation at American Academy of Neurology Annual Meeting, 2024
  • Invited speaker on PhD student Panel
    • SUmmer Research Initiative (SURI) 2023, Arizona State University
  • Invited Young Professionals (YP) speaker at CMD Workshop link
    • IEEE IAS Annual Meeting, 2022
  • Fulton Schools CS Doctoral student & researcher explores the quickly evolving world of AI and related smart tech advances on popular podcast  link
    • FullCircle, Arizona State University Newsletter
  • Using AI to battle Alzheimer’s  link asu news
    • FullCircle, Arizona State University Newsletter
  • Invited speaker at Emerging Research Topics in Engineering(ERTE)  link
    • IEEE Gujarat Section
  • Invited talk on "Landscape of Explainable AI, Interpreting Deep Learning predictions and my observations from hosting an ML Podcast"  link
    • 4th OnCV&AI workshop arranged by the Nordling Lab, National Cheng Kung University in Taiwan
  • From DA-IICT to Arizona State University and working with Nobel Laureate Frank Wilczek: Journey of Jay Shah  link
    • DA-IICT Blog
  • Interview on growing a technical podcast   link link
    • IEEE Spectrum and IEEE TV
  • Behind the scenes with a Machine Learning Expert : Jay Shah  link
    • Curryup Leadership Podcast
  • Python Workshop  2020 Convolutional Neural Networks   2020 2021
    • AI Club, Arizona State University

Podcast mentions

  • Best 100 Machine Learning Podcasts, Million Podcasts  link
  • Best 100 Research Podcasts, Million Podcasts  link
  • A hand-curated list of the best AI Podcasts, AI Depot  link
  • 8 of the best machine learning podcasts to listen to in 2022, Qwak MLOps  link
  • 5 Best Machine Learning & AI Podcasts, Unite[dot]AI, Futurist series  link
  • 20 best Machine Learning Podcasts of 2021, Welp Magazine  link

In the media

  • Heard on the Street – 2/15/2024  link
    • InsideBigData
  • Chip industry strains to meet AI-fueled demands-will smaller LLMs help?  link
    • ComputerWorld
  • Three Ways Deep Learning Yields New Insights for Medical Researchers  link
    • IEEE Transmitter
  • How AI could revolutionize biology — and vice versa  link
    • Axios