
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
Announcements

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
Featured News



Awards
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RSNA 2018, Top 10 Radiology papers by Downloads x2
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Best Paper Award, EMNLP 2016
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NSF Fellowship, 2016
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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


