Jay Shah



jgshah1@asu.edu

jaygshah
jaygshah22
jaygshah

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 deep learning models 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:

  • Capturing Imaging signatures of Brain-Age in Alzheimer's Disease
  • Diffusion model based image super-resolution to improve quantification in Amyloid PET
  • Multi-modal AI to investigate mechanisms and prevention of Persistent Post-Traumatic Headache

These research projects 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 AI engineers, researchers and practitioners to talk more their jounrney, insights from their experience and tips on getting started.

Publications

Google Scholar profile
  1. Ordinal Classification with Distance Regularization for Robust Brain Age Prediction
    Jay Shah, Md Mahfuzur R. Siddiquee, Yi Su, Teresa Wu, Baoxin Li
    In Proceedings of the IEEE/CVF WACV 2024.
    link arxiv pdf code

  2. 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 code

  3. Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images
    Md Mahfuzur R. Siddiquee, Jay Shah, Teresa Wu, Catherine Chong, Todd Schwedt, Baoxin Li
    In Proceedings of the IEEE/CVF WACV 2024.
    link arxiv pdf code

  4. HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease
    Md Mahfuzur R. 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. AnoFPDM: Anomaly Segmentation with Forward Process of Diffusion Models for Brain MRI
    Yiming Che, Fazle Rafsani, Jay Shah, Md Mahfuzur R. Siddiquee, Teresa Wu
    arxiv pdf code

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

  7. Interpretable deep learning framework towards understanding molecular changes in human brains with Alzheimer’s disease: implication for microglia activation and sex differences in AD
    Maitry Ronakbhai Trivedi, Amogh Joshi, Jay Shah, Benjamin Readhead, Melissa Wilson, Yi Su, Eric Reiman, Teresa Wu, Qi Wang
    bioarxiv pdf

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

  9. Physical activity and the outcome of cognitive trajectory: a machine learning approach
    Bettina Barisch-Fritz, Jay Shah, Jelena Krafft, Yonas Geda, Teresa Wu, Alexander Woll, Janina Krell-Roesch
    European Reviews of Aging and Physical Activity, 2024

Conference Abstracts

  1. Capturing MRI Signatures of Brain Age as a Potential Biomarker to Predict Persistence of Post-traumatic Headache
    Jay Shah, Md Mahfuzur R. Siddiquee, Catherine Chong, Todd Schwedt, Jing Li, Visar Berisha, Katherine Ross, Teresa Wu
    American Academy of Neurology, Annual Meeting, 2024
    link

  2. Applying Generative Adversarial Network on Structural Brain MRI for Unsupervised Classification of Headache
    Md Mahfuzur R. Siddiquee, Jay Shah, Todd Schwedt, Catherine Chong, Baoxin Li, Teresa Wu
    American Academy of Neurology, Annual Meeting, 2024
    link

  3. Prediction of Headache Improvement Using Multimodal Machine Learning in Patients with Acute Post-traumatic Headache
    Amogh Joshi, Md Mahfuzur R. Siddiquee, Jay Shah, Todd Schwedt, Catherine Chong, Baoxin Li, Teresa Wu
    American Academy of Neurology, Annual Meeting, 2024
    link

  4. 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
    link pdf

  5. 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
    link pdf

  6. 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
    link pdf

  7. some other abstracts...

Patents

  • User-guided context-aware music recommendations
    Inventors: Jay Shah, Shanti Stewart, Gauri Jagatap, Gouthaman KV, Andrea Fanelli
    Dolby Laboratories, 2024

  • Deep Residual Inception Encoder-Decoder Network for Amyloid PET Harmonization
    Inventors: Fei Gao, Yi Su, Jay Shah, Teresa Wu
    US20240285244A1 WO2023101959A1 pdf

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

  • Ph.D. Research Intern
    Dolby Laboratories, San Francisco [Multimodal Processing Team]
    05.2024 - 08.2024

  • 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

  • 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
  • Speaking at Emerging Research Topics in Engineering(ERTE)  link
    • IEEE Gujarat Section
  • 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:
  • 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