Harvard Medical School
Mauricio Santillana is an Assistant Professor at Harvard Medical School, a faculty member in the Computational Health Informatics Program at Boston Children’s Hospital, and an associate at the Harvard Institute for Applied and Computational Sciences. Mauricio enjoys working with clinicians in the design of decision-making support tools. Mauricio is a physicist and applied mathematician with expertise in mathematical modeling and scientific computing. He has worked in multiple research areas frequently analyzing big data sets to understand and predict the behavior of complex systems. His research modeling population growth patterns has informed policy makers in Mexico and Texas. His research in numerical analysis and computational fluid dynamics has been used to improve models of coastal floods due to hurricanes, and to improve the performance of global atmospheric chemistry models. In recent years, his main interest has been to develop mathematical models to improve healthcare. Specifically, he has leveraged information from big data sets from Internet-based services (such as Google, Twitter, Flu Near You, Weather) and electronic health records (EHR) to predict disease incidence in multiple locations worldwide and to predict outcomes in hospitalized patients. Dr. Santillana has advised the CDC and the White House on the development of population-wide disease forecasting tools.
Mauricio received a B.S. in physics with highest honors from the Universidad Nacional Autonoma de Mexico in Mexico City, and a master’s and PhD in computational and applied mathematics from the University of Texas at Austin. Mauricio first joined Harvard as a postdoctoral fellow at the Harvard Center for the Environment and has been a lecturer in applied mathematics at the Harvard SEAS, receiving two awards for excellence in teaching.
Works on: Using social media, Internet searches, and electronic health records to predict incidence of flu and dengue in multiple locations worldwide. Using electronic health records to predict outcomes in pediatric intensive care units.