Faculty Recruiting Support CICS

Machine Learning and Friends Lunch (Online)

11 Mar
Thursday, 03/11/2021 11:45am to 1:15pm
Virtual via Zoom
Machine Learning and Friends Lunch
Speaker: Marinka Zitnik

Abstract: The success of machine learning depends heavily on the choice of representations used for downstream tasks. Graph neural networks have emerged as a predominant choice for learning representations of networked data. In this talk, I describe our efforts to expand the scope and ease the applicability of graph representation learning. First, I outline SubGNN, a subgraph neural network for learning disentangled subgraph representations. Second, I will describe G-Meta, a novel meta-learning approach for graphs. G-Meta uses subgraphs to generalize to completely new graphs and never-before-seen labels using only a handful of nodes or edges. G-Meta is theoretically justified and scales to orders of magnitude larger datasets than prior work. Finally, I will discuss applications in the development of safe and effective therapeutics. The new methods have enabled the repurposing of drugs for emerging diseases, including COVID-19, where our predictions were experimentally verified in the wet laboratory. Further, our knowledge graph methods enabled discovering dozens of combinations of drugs safe for 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 receive predictions that can be interpreted meaningfully.

Bio: Marinka Zitnik is an Assistant Professor at Harvard University with appointments in the Department of Biomedical Informatics, Broad Institute of MIT and Harvard, and Harvard Data Science. Dr. Zitnik is a computer scientist studying machine learning, focusing on challenges brought forward by data in science, medicine, and health. Before Harvard, she was a postdoctoral fellow in Computer Science at Stanford and also a member of the Chan Zuckerberg Biohub. Dr. Zitnik has published extensively in top ML venues (e.g., NeurIPS, ICLR, ICML) and leading interdisciplinary journals (e.g., Nature Methods, Nature Communications, PNAS). She has organized numerous workshops and tutorials in the nexus of AI, deep learning, drug discovery, and medical AI at leading conferences (NeurIPS, ICLR, ICML, ISMB, AAAI, WWW), where she is also in the organizing committees. She also organized the National Symposium on drugs for future pandemics on behalf of the NSF. Her research won Bayer Early Excellence in Science Award and numerous best paper and research awards from the International Society for Computational Biology. She was named a Rising Star in Electrical Engineering and Computer Science (EECS) by MIT and also a Next Generation in Biomedicine by Broad Institute of MIT and Harvard, being the only young scientist who received such recognition in both EECS and Biomedicine.

To obtain the Zoom link for this event, please see the event announcements from MLFL on the college email lists or contact Kalpesh Krishna.