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Adam Yala

PhD Candidate at MIT CSAIL

My research interests lie in the intersection of Machine Learning and Oncology. I believe that algorithmic innovation can create more precise and equitable healthcare. On the machine learning side, I’m particularly interested in developing neural models that can leverage alternative forms of supervision and produce interpretable rationales for their predictions.  On the oncology side, I’m passionate about developing algorithms that can improve early detection and reduce overtreatment.  So far, I’ve focused on developing algorithms for future cancer risk and personalized screening. My mammography based models for cancer risk have been clinically implemented at MGH and have been used to interpret hundreds of thousands of mammograms. 

Advised by: Regina Barzilay 

Member of: MIT NLP Group, Learning to Cure

Supported by: NSF Graduate Fellowship, MIT EECS Fellowship

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Featured Publications

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 2020.

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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 2018. Top 10 RSNA Radiology papers by Altmetric 2018.

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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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Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone

Tally Portnoi, Adam Yala, Tal Schuster, Regina Barzilay, Brian Dontchos, Leslie Lamb, Constance Lehman
American Journal of Roentgenology 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 2018.
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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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View All

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

  • RSNA 2018, Top 10 Radiology papers by Downloads x2

  • 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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