Learning Actionable Representations of Biomedical Data
Visiting Speaker
Past Event
Marinka Zitnik
Assistant Professor, Harvard University
Feb 7, 2020
Watch video
2:00 pm
177 Huntington Ave
11th floor

The success of machine learning is heavily dependent on the choice of data features on which the methods are applied. For that reason, much of the actual efforts in deploying algorithms go into the design of features that support effective machine learning. In this talk, I describe our efforts to expand the scope and ease the applicability of machine learning on complex, interconnected datasets. First, I outline our methods for graph representation learning. The methods specify deep graph neural functions that map nodes in a graph to points in a compact vector space, termed embeddings. Importantly, these graph neural methods are optimized to embed the graph such that performing algebraic operations in the learned embedding space reflects topology of the graph. We show how embeddings enable repurposing of drugs for new indications and the discovery of dozens of drug combinations that are safe in patients with considerably fewer unwanted side effects than today's treatments. Lastly, I describe our efforts in learning actionable representations that allow users of our models to ask what-if questions and receive predictions that are accurate and can be interpreted meaningfully.

About the speaker
Marinka Zitnik is an Assistant Professor at Harvard University and Associate Member at the Broad Institute of MIT and Harvard. Before joining Harvard in 2019, she was a postdoctoral scholar in Computer Science at Stanford University working with Jure Leskovec. Her research investigates machine learning focusing on problems brought forward by data in sciences, medicine, and health. She was recently named a Rising Star in EECS by MIT and also a Next Generation in Biomedicine by the Broad Institute, being the only young scientist who received such recognition in both EECS and Biomedicine.