Dr. Zitnik is Assistant Professor at Harvard University in the Department of Biomedical Informatics, Associate Member at Broad Institute of MIT and Harvard, and Faculty at Harvard Data Science. Dr. Zitnik is a computer scientist studying machine learning and artificial intelligence with a focus on networked systems in biology and medicine that require infusing structure and knowledge.
Dr. Zitnik has published extensively in top ML venues and leading scientific journals. She has organized conferences and workshops in graph representation learning, drug discovery, and precision medicine at leading conferences, where she is also on the organizing committees. She is an ELLIS Scholar in the European Laboratory for Learning and Intelligent Systems Society. Her research won paper and research awards from the International Society for Computational Biology, Bayer Early Excellence in Science, Amazon Faculty Research, Roche Alliance with Distinguished Scientists, Rising Star Award in Electrical Engineering and Computer Science, and Next Generation in Biomedicine Recognition, being the only young scientist with such recognition in both EECS and Biomedicine. She co-founded Therapeutics Data Commons and also AI for Science initiatives.
Zitnik Lab (zitniklab.hms.harvard.edu)'s focus is on the next generation of artificial intelligence methods and scientific opportunities that build models from data and use them alone or in conjunction with laboratory experiments.
Current research directions include: fusing data modalities like genetic code, behavior, therapeutics, nutrients, and the environment into knowledge networks; pioneering graph neural networks and deep learning for networks to reason about interconnected biology and medicine; developing methods for learning representations that are actionable—lend themselves to actionable hypotheses—and allow users of our models to ask what-if questions and receive predictions that are accurate, precise, robust and can be interpreted meaningfully; advancing algorithms to train more with less data, exploit the ability of models to apply prediction prowess acquired from one data type to another type, and design contextually adaptive AI for classes of phenomena that can learn about never-before-seen phenomena (e.g., new patients, diseases, or cell types).
- AI for Medicine. The state of a person is described with increasing precision incorporating modalities like genetic code, behaviors, therapeutics, and the environment—the challenge is how to reason over these data to improve decision making. Our research creates new avenues for accelerating the development of therapeutics, fusing biomedical knowledge and patient data, and giving the right patient the right treatment at the right time to have medicinal effects that are consistent from person to person and with results in the laboratory.
- AI for Science. For centuries, the method of discovery—the fundamental practice of science that scientists use to explain the natural world systematically and logically—has remained largely the same. We are using artificial intelligence to change that. The natural world is interconnected, from all facets of genome regulation to molecular and organismal levels. These interactions at different levels give rise to a bewildering degree of complexity. Our research disentangles this complexity and develops artificial intelligence tools to guide discovery in biomedical sciences and produce interpretable outputs that lend themselves to actionable hypotheses.