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Adam Yala

Assistant Professor  

Computational Precision Health and

UC Berkeley and UCSF

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. 

EECS

Contact:

 yala@berkeley.edu

 

Awards

  • 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

6.883 Modeling with Machine Learning: From Algorithms to Applications

Teaching Assistant, Spring 2020 

MIT Machine Learning for Big Data and Text Processing: Foundations (x4)

Teaching Assistant, Summer 2017 - Spring 2020

 
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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, In Press 

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Syfer: Neural Obfuscation for Private Data Release

Adam Yala, Victor Quach, Homa Esfahanizadeh, Rafael G. L. D'Oliviera, Ken R Duffy, Muriel Médard, Tommi S Jaakkola, Regina Barzilay
Preprint, Arxiv 

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Optimizing risk-based breast cancer screening policies with reinforcement learning

Adam Yala, Peter Mikhael, Constance Lehman, Gigin Lin, Fredrik Strand, Yung-Liang Wang, Kevin Hughes, Siddharth Satuluru, Thomas Kim, Imon Banerjee, Judy Gichoya, Hari Trivedi, Regina Barzilay
Nature Medicine, 2022.

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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

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Towards Robust Mammography-Based Models for Breast Cancer Risk

Adam Yala, Peter G Mikhael, Fredrik Strand, Gigin Lin, Kevin Smith, Yung-Liang Wan, Leslie Lamb, Kevin Hughes, Constance Lehman, Regina Barzilay
Science Translational Medicine 2021.

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A Deep Learning Model to Triage Screening Mammograms: A Simulation Study

Adam Yala, Tal Schuster, Randy Miles, Regina Barzilay, Constance Lehman

RSNA Radiology, 2019

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A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction

Adam Yala , Constance Lehman, Tal Schuster, Tally Portnoi, Regina Barzilay 

RSNA Radiology 2019.

Top 10 RSNA Radiology papers by Downloads 2019. Top 10 RSNA Radiology papers by Altmetric 2019.

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Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation

Constance D. Lehman , Adam Yala, Tal Schuster, Brian Dontchos, Manisha Bahl, Kyle Swanson, Regina Barzilay
RSNA Radiology 2018.
Top 10 RSNA Radiology papers by Downloads 2019.
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