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Data Opportunities: unsolved medical problems and where new data can help
Bin Yu · Regina Barzilay · Marzyeh Ghassemi · Emma Pierson

Tue Dec 14 11:15 AM -- 12:00 PM (PST) @
in Machine learning from ground truth: New medical imaging datasets for unsolved medical problems. »

This panel will begin by introducing three critical medical issues that drive mortality despite years of research: cancer, sudden cardiac death, maternal mortality. The moderator will facilitate a group conversation about how the panelists approach working on issues like these in their research, as well as challenges and opportunities in applying new data and ML tools to similar issues in medicine.

Featured panelists include Regina Barzilay, Distinguished Professor for AI and Health, EECS, MIT; Marzyeh Ghassemi, Assistant Professor, EECS and IMES, MIT, Faculty Member, The Vector Insitute Elaine Nsoesie, Assistant Professor, Boston University School of Public Health; Emma Pierson, Assistant Professor of Computer Science, Jacobs Technion-Cornell Institute at Cornell Tech

Author Information

Bin Yu (UC Berkeley)

Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Sciences at the University of California at Berkeley and a former chair of Statistics at UC Berkeley. Her research focuses on practice, algorithm, and theory of statistical machine learning and causal inference. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and precision medicine. In order to augment empirical evidence for decision-making, they are investigating methods/algorithms (and associated statistical inference problems) such as dictionary learning, non-negative matrix factorization (NMF), EM and deep learning (CNNs and LSTMs), and heterogeneous effect estimation in randomized experiments (X-learner). Their recent algorithms include staNMF for unsupervised learning, iterative Random Forests (iRF) and signed iRF (s-iRF) for discovering predictive and stable high-order interactions in supervised learning, contextual decomposition (CD) and aggregated contextual decomposition (ACD) for phrase or patch importance extraction from an LSTM or a CNN. She is a member of the U.S. National Academy of Sciences and Fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 2013-2014 and the Rietz Lecturer of IMS in 2016. She received the E. L. Scott Award from COPSS (Committee of Presidents of Statistical Societies) in 2018. Moreover, Yu was a founding co-director of the Microsoft Research Asia (MSR) Lab at Peking Univeristy and is a member of the scientific advisory board at the UK Alan Turning Institute for data science and AI.

Regina Barzilay (Massachusetts Institute of Technology)
Marzyeh Ghassemi (University of Toronto / Vector Institute)
Emma Pierson (Microsoft Research)

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