Mauricio Santillana


Mauricio Santillana, Ph.D.
Professor of Physics, Physics Department, Northeastern University
Professor of Electrical and Computer Engineering, ECE DepartmentNortheastern University
Adjunct Professor of Epidemiology, Harvard T.H. Chan School of Public Health
Affiliate Faculty, Center for Communicable Disease Dynamics, HSPH
Associate, Institute for Applied Computational Sciences, Harvard Shool of Engineering and Applied Sciences.

Short Bio

Mauricio Santillana, PhD, MSc is the director of the Machine Intelligence Group for the betterment of Health and the Environment (MIGHTE) at the Network Science Institute at Northeastern University. He is a Professor at both the Physics and Electrical and Computer Engineering Departments at Northeastern University, and an Adjunct Professor at the Department of Epidemiology, T.H. Chan Harvard School of Public Health. Dr. Santillana’s research areas  include the modeling of geographic patterns of population growth, modeling fluid flow to inform coastal floods simulations and atmospheric global pollution transport models, and most recently, the design and implementation of disease outbreaks prediction platforms and mathematical solutions to healthcare. His research has shown that machine learning techniques can be used to effectively monitor and predict the dynamics of disease outbreaks using novel data sources not designed for these purposes such as: Internet search activity, social media posts, clinician’s searches, human mobility, weather, etc.

His original research and perspectives have appeared in journals such as Nature, Science, Proceedings of the National Academy of Science, Science Advances, Nature Communications, and Nature Climate Change, among others. His work has been funded by the National Institute of General Medical Sciences (National Institutes of Health, NIH), the U.S. Centers for Disease Control and Prevention, the Bill and Melinda Gates Foundation, and multiple foundations such as: the Johnson and Johnson Foundation, Ending Pandemics Fund, Skoll Global Threats Fund. Dr. Santillana has advised the US CDC, Africa CDC, and the White House on the development of population-wide disease forecasting tools. His original research and perspectives have been featured in a diverse array of national and international news outlets such as The New York Times, The Washington Post, The Wall Street Journal,, Politico, National Public Radio, CNN, CNN Espanol, Fox, BBC, among others.  

In recent years, Dr. Santillana’s main interest has been to design and implement mathematical solutions to healthcare. Specifically, his ongoing research activities can be summarized as follows:

1. He is interested in leveraging information from big data sets from Internet-based services (such as Google, Twitter, Weather, Human mobility/migration) and electronic health records (EHR) to predict disease incidence in multiple locations worldwide

2. He works in close collaboration with Intensive Care (ICU) physicians, mathematicians, computer and data scientists to monitor health indicators and predict outcomes in hospitalized patients in critical care.

3. Dr. Santillana has advised the CDC, Africa CDC, and the White House on the development of population-wide disease forecasting tools.

4. Dr. Santillana and his colleagues have identified previously unknown associations between Antibiotic Resistance incidence in humans and local ambient temperature in the USA and Europe.

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.