Adam Yala
Assistant Professor of
UC Berkeley and UCSF
My research develops machine learning methods for personalized cancer care and to translate them to clinical practice; our overarching goal is to offer each patient the right intervention (e.g. screening exam or particular treatment choice) at the right time according to their individual risks and preferences. To this end, our lab focuses on three major themes: 1) modeling full patient records (e.g. multi-modal imaging, pathology, etc) to better understand patient outcomes, 2) deriving better decisions from AI-driven predictors (e.g. screening and treatment policies, choosing therapeutic targets, providing decision guarantees, etc.) and 3) clinical translation. Our tools are implemented at multiple hospital systems around the world, and underlie prospective clinical trials.
Contact:
Featured Publications
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
Highlighted as Best of JCO 2023 Thoracic Oncology
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
Featured News
Awards
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Eppy Award: Investigative Reporting 2022
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Falling Walls Finalist: Life Science 2022
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Top 10 Radiology 2019 papers by Downloads (#3)
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Top 10 Radiology 2019 papers by Downloads (#7)
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Top 10 Radiology 2019 papers by Altmetric (#5)
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Best Paper Award, EMNLP 2016
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NSF Fellowship, 2016
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MIT EECS Fellowship, 2016