My interests lie in the intersection of Machine Learning and Precision Medicine. I believe that algorithmic innovation can create more precise and equitable healthcare. Towards this goal, my research focuses on designing modeling approaches that are robust to data-generation biases, offer safe-guards for clinical deployment and can adapt to diverse clinical requirements. My previous work has contributed to three areas: 1) predicting future cancer risk, 2) designing personalized screening policies and 3) private data sharing through neural obfuscation. My tools are implemented at multiple hospital systems around the world, and underlie prospective clinical trials. The ultimate goal of these efforts is to change the standard of care.
Prospective PhD Students: I'm recruiting! Please reach out to me over email with your CV if you're interested in joining my lab at UC Berkeley and UCSF.
UC Berkeley and UCSF
Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk from a Single Low-dose Chest Computed Tomography
Peter Mikhael, Jeremy Wohlwend, Adam Yala, Ludvig Karstens, Justin Xiang, Angelo Takigami, Patrick Bourgouin, PuiTee Chan, Sofiane Mrah, Wael Amayri, Yu-Hsiang Juan, Cheng-Ta Yang, Yung-Liang Wan, Gigin Lin, et al.
Journal of Clinical Oncology, 2023
Multi-Institutional Validation of a Mammography-based Breast Cancer Risk Model
Adam Yala, Peter G Mikhael, Fredrik Strand, Gigin Lin, Siddharth Satuluru, Thomas Kim, Imon Banerjee, Judy Gichoya , Hari Trivedi , Constance Lehman , Kevin Hughes, David J Sheedy, Lisa M Matthis, et al.
Journal of Clinical Oncology, 2021
Eppy Award: Investigative Reporting 2022
Falling Walls Finalist: Life Science 2022
Top 10 Radiology 2019 papers by Downloads (#3)
Top 10 Radiology 2019 papers by Downloads (#7)
Top 10 Radiology 2019 papers by Altmetric (#5)
Best Paper Award, EMNLP 2016
NSF Fellowship, 2016
MIT EECS Fellowship, 2016
Teaching Assistant, Spring 2020
MIT Machine Learning for Big Data and Text Processing: Foundations (x4)
Teaching Assistant, Summer 2017 - Spring 2020