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 challenges brought forward by data in science, medicine, and health.
Dr. Zitnik joined Harvard in December 2019. Before joining Harvard, she was a postdoctoral scholar in Computer Science at Stanford University. She was also a member of the Chan Zuckerberg Biohub at Stanford. She received her bachelor’s degree, double majoring in computer science and mathematics, and then graduated with a Ph.D. in Computer Science from University of Ljubljana just three years later while also researching at Imperial College London, University of Toronto, and Baylor College of Medicine. Her work received several best paper, poster, and research awards from the International Society for Computational Biology. Her research recently won the Bayer Early Excellence in Science Award. 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.
Zitnik Lab (zitniklab.hms.harvard.edu)'s overarching goal is to develop the next generation of machine learning for data in medicine and science. Our research realizes an end-to-end scientific approach in which we: Invent ways to combine rich, heterogeneous data in their broadest sense to reduce redundancy and uncertainty and to make them amenable to comprehensive analyses; Develop methods for reasoning over rich, interconnected data, and design architectures for learning actionable representations; and Translate machine learning research into innovative applications and solutions for open biomedical questions. Our research proves that this approach not only opens up new avenues for understanding nature, analyzing health, and developing new medicines to help people but can impact the way predictive modeling is performed today at the fundamental level.
Our 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 interaconnected 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 and reason about never-before-seen systems as they encounter new tasks and situations (e.g., new patients, diseases, or cell types); and developing open biomedical discovery infrastructure for safer and more effective therapeutics.