Jay Shah



Contact

jgshah1@asu.edu

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 jaygshah22
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JayShahML


I am a PhD student in Wu-Lab, at Arizona State University, co-advised by Dr. Teresa Wu and Dr. Baoxin Li

Currently focused on developing novel models and algorithms using Deep Learning for biomarker discovery, using multi-modal data and tackling issue of small datasets. Broadly my research interests lie in Computer Vision, Medical Imaging and Deep Learning

Specifically working on solving following research questions using Machine Learning:

  1. Multidisciplinary translational approach investigating mechanisms, and prevention of Persistent Post-Traumatic Headache
  2. Capturing Imaging signatures of Brain-Age in Alzheimer's Disease
  3. Pathways linking NeuroPsychiatric symptoms with Alzheimer’s Disease neuroimaging biomarkers and the outcome of incident mild cognitive impairment/dementia

These research works are in joint collaboration with Mayo Clinic, Banner Alzheimer's Institute and Barrow Neurological Institute in Arizona respectively. My CV

Also the host of Jay Shah Podcast on YouTube where I invite Machine Learning engineers, researchers and practitioners to talk more their jounrney, insights from their experience and tips on getting started.

Research

Publications

  1. Deep Residual Inception Encoder-Decoder Network for Amyloid PET Harmonization
    Jay Shah, Fei Gao, Valentina Ghisays, Ji Luo, Yinghua Chen, Wendy Lee, Yuxiang Zhou, Tammie Benzinger, Eric Reiman, Kewei Chen, Yi Su, Teresa Wu
    Alzheimer's & Dementia, the Journal of Alzheimer's Association, 2022
    link pdf patent (PCT/US22/51243)

  2. Neuropsychiatric symptoms and commonly used biomarkers of Alzheimer’s disease: A literature review from a Machine Learning perspective
    Jay Shah, Md Mahfuzur Rahman Siddiquee, Janina Krell-Roesch, Jeremy Syrjanen, Walter Kremers, Maria Vassilaki, Erica Forzani, Teresa Wu, Yonas Geda
    Journal of Alzheimer's Disease, 2023
    link pdf

  3. Headache Classification and Automatic Biomarker Extraction from structural MRIs using Deep Learning
    Md Mahfuzur Rahman Siddiquee, Jay Shah, Catherine Chong, Simona Nikolova, Gina Dumkrieger, Baoxin Li, Teresa Wu, Todd Schwedt
    Brain Communications, 2022
    link pdf

  4. HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease
    Md Mahfuzur Rahman Siddiquee, Jay Shah, Teresa Wu, Catherine Chong, Todd Schwedt, Baoxin Li
    Simulation and Synthesis in Medical Imaging (SASHIMI), 2022 [MICCAI workshop]
    link arxiv pdf code

  5. Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images
    Md Mahfuzur Rahman Siddiquee, Jay Shah, Teresa Wu, Catherine Chong, Todd Schwedt, Baoxin Li
    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2024.
    link pdf code

Conference Abstracts

  1. A multi-class deep learning model to estimate brain age while addressing systematic bias of regression to the mean
    Jay Shah, Ji Luo, Javad Sohankar, Eric Reiman, Kewei Chen, Yi Su, Baoxin Li, Teresa Wu
    Alzheimer's Association International Conference, 2023 | Arizona Alzheimer’s Consortium, 2023
    link

  2. A 2.5D residual U-Net for improved amyloid harmonization preserving spatial information
    Jay Shah, Javad Sohankar, Ji Luo, Yinghua Chen, Shan Li, Hillary Protas, Kewei Chen, Eric Reiman, Baoxin Li, Teresa Wu, Yi Su
    Alzheimer's Association International Conference, 2023 | Arizona Alzheimer’s Consortium, 2023
    link

  3. Interpretable deep learning framework towards understanding molecular changes associated with neuropathology in human brains with Alzheimer’s disease
    Amogh Joshi, Jay Shah, Benjamin Readhead, Yi Su, Teresa Wu, Qi Wang
    Alzheimer's Association International Conference, 2023 | Arizona Alzheimer’s Consortium, 2023
    link

  4. Classification and Biomarker Discovery of Persistent Post-traumatic Headache (PPTH) Using Deep Learning on Structural Brain MRI Data
    Md Mahfuzur Rahman Siddiquee, Jay Shah, Todd Schwedt, Catherine Chong, Simona Nikolova, Gina Dumkrieger, Katherine Ross, Visar Berisha, Jing Li, Teresa Wu
    OR/MS/Analytics in the Diagnosis and Treatment of Neurological Diseases, INFORMS Annual Meeting, 2022
    link

  5. Participant-specific interrogation of population-based data to predict cognitive decline from neuropsychiatric symptoms and neuroimaging biomarkers: A machine learning approach
    Jay Shah, Jeremy Syrjanen, Janina Krell-Roesch, Walter Kremers, Prashanthi Vemuri, Maria Vassilaki, Ronald Petersen, Erica Forzani, Teresa Wu, Yonas Geda
    American Academy of Neurology, Annual Meeting, 2023
    link pdf

  6. MRI signatures of Brain Age in the Alzheimer’s Disease continuum
    Jay Shah, Valentina Ghisays, Yinghua Chen, Ji Luo, Baoxin Li, Eric Reiman, Kewei Chen, Teresa Wu, Yi Su
    Alzheimer's Association International Conference, 2022
    link pdf

  7. Transfer Learning based Deep Encoder Decoder Network for Amyloid PET Harmonization with Small Datasets
    Jay Shah, Kewei Chen, Eric Reiman, Baoxin Li, Teresa Wu, Yi Su
    Alzheimer's Association International Conference, 2022
    link pdf

  8. Classification of Post-Traumatic Headache (PTH) using Deep Learning on Structural Brain MRI data
    Md Mahfuzur Rahman Siddiquee, Jay Shah, Todd Schwedt, Catherine Chong, Simona Nikolova, Gina Dumkrieger, Katherine Ross, Visar Berisha, Jing Li, Teresa Wu
    American Headache Society 64th Annual Scientific Meeting June 9–12, 2022 Denver, Colorado. Headache, 62: 91
    link pdf

  9. Migraine Classification using Deep Learning on Structural Brain MRI data
    Md Mahfuzur Rahman Siddiquee, Jay Shah, Todd Schwedt, Catherine Chong, Simona Nikolova, Gina Dumkrieger, Katherine Ross, Visar Berisha, Jing Li, Teresa Wu
    American Headache Society 64th Annual Scientific Meeting June 9–12, 2022 Denver, Colorado. Headache, 62: 91-92
    link pdf

  10. Interpreting Deep Learning Model Predictions using Shapley Values
    Jay Shah, Catherine Chong, Todd Schwedt, Visar Berisha, Jing Li, Katherine Ross, Gina Dumkrieger, Jianwei Zhang, Nathan Gaw, Simona Nikolova, Teresa Wu
    INFORMS Annual Meeting, 2021

  11. Deep Residual Inception Encoder-Decoder Network for Amyloid PET Harmonization
    Jay Shah, Valentina Ghisays, Ji Luo, Yinghua Chen, Wendy Lee, Baoxin Li, Tammie Benzinger, Eric Reiman, Kewei Chen, Yi Su, Teresa Wu
    Alzheimer’s Association International Conference, 2021 | Arizona Alzheimer’s Consortium, 2021
    link pdf

Patents

  • (Provisional, #63285002) Deep Residual Inception Encoder-Decoder Network for Amyloid PET Harmonization, 12/01/2021,
    Fei Gao, Yi Su, Jay Shah, Teresa Wu.

Work Experience

  • Research Assistant, Ph.D. Student
    Arizona State University, Tempe
    05.2020 - Present

  • Research Scientist Intern
    Amazon, Seattle (Health Halo Computer Vision)
    05.2022 - 08.2022

  • Graduate Teaching Assistant
    Arizona State University, Tempe
    10.2019 - 05.2020

  • Research Intern - Computer Vision
    Philips Research Labs, Cambridge
    06.2019 - 08.2019

  • Graduate Research Assistant
    Arizona State University, Tempe
    11.2018 - 06.2019

  • Machine Learning Engineer Intern
    HackerRank, Bengaluru
    01.2018 - 05.2018

  • Visiting Research Assistant
    Nanyang Technological University, Bengaluru
    05.2017 - 08.2017

  • Undergraduate Research Assistant
    Dhirubhai Ambani Institute of Info. & Comm. Technology, Gandhinagar
    05.2016 - 08.2016

News and Highlights

  • Invited Young Professionals (YP) speaker at CMD Workshop, IEEE IAS Annual Meeting, 2022 link
  • 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
  • Podcast mentions:
    • 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
  • Speaking at Emerging Research Topics in Engineering(ERTE)  link
    • IEEE Gujarat Section
  • Three Ways Deep Learning Yields New Insights for Medical Researchers  link
    • IEEE Transmitter
  • 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
  • How AI could revolutionize biology — and vice versa  link
    • Axios
  • 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