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Adam Yala

Assistant Professor  

Computational Precision Health and EECS

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. 

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Featured Publications

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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NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training

Adam Yala, Homa Esfahanizadeh, Rafael G. L. D' Oliveira, Ken R. Duffy, Manya Ghobadi, Tommi S. Jaakkola, Vinod Vaikuntanathan, Regina Barzilay, Muriel Medard
Preprint, Arxiv

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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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Do Neural Information Extraction Algorithms Generalize Across Institutions?

Enrico Santus, Adam Yala, Donald Peck, Rufina Soomro, Naveen Faridi, Isra Mamshad, Rong Tang, Conor R. Lanahan, Regina Barzilay, and Kevin Hughes

JCO Clinical Informatics 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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Using machine learning to parse breast pathology reports
Adam Yala, Regina Barzilay, Laura Salama, Molly Griffin, Grace Sollender, Constance Lehman, Alphonse Taghian, Kevin S. Hughes, et al
Breast Cancer Research and Treatment 2016
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Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

Karthik Narasimhan, Adam Yala, Regina Barzilay

Proceedings of EMNLP 2016

Best Paper Award

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Recent Talks

APR 2021

MAR 2021

NOV 2020

OCT 2020

JUNE 2020

APRIL 2020

FEB 2020

JAN 2020

NOV 2019

OCT 2019

AAPM AI for Mammography Invited Lecture

MIT AI for Healthcare Equity Panelist

Bristol Myers Squibb Oncology Invited Lecture

HESAV SwissNex Invited Lecture

MIT Horizons

Harvard EPI 257: Guest Lecture

APA and Kenner Foundation: AI And Early Detection of Pancreatic Cancer Summit

American Association for Cancer Research: Educational Session Speaker

British Columbia Breast Cancer Screening Forum 

Sanofi OncoXChange Lecture

MIT 6.883: Guest Lecture

Stand Up To Cancer

Henry Ford Pancreas Symposium

Weill Cornell Machine Learning in Medicine Seminar

Bayer Invited Lecture

ECOG-ACRIN Translational Science Symposium 

 
 

Awards

  • 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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