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Lab Director
Biography
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, at the Harvard T.H. Chan 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, and multiple foundations such as: the Bill and Melinda Gates Foundation, 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 Atlantic, The Wall Street Journal, Vox.com, Politico, National Public Radio, CNN, CNN Espanol, Fox, BBC, among others. Mauricio received a B.S. in Physics with highest honors from Universidad Nacional Autónoma de México in Mexico City, and a Master’s and PhD in Computational and Applied Mathematics from the University of Texas at Austin. Mauricio was a Postdoctoral fellow at the Harvard Center for the Environment and later became a lecturer in applied mathematics at the Harvard School of Engineering and Applied Sciences, receiving two awards for excellence in teaching. He became a tenure-track faculty member at Boston Children's Hospital, Harvard Medical School, and the Harvard T.H. Chan School of Public Health. He recently joined the faculty at Northeastern University. Works on: Using social media, Internet searches, and electronic health records to predict incidence of influenza, dengue fever, malaria, COVID-19, antibiotic resistance, in multiple locations worldwide. Using electronic health records to predict outcomes in pediatric intensive care units. Developing approaches to improve resource allocation in Hospitals. Characterizing epidemic outbreaks in real-time. Pandemic Preparedness. Numerical Solutions of Partial Differential Equations. Scientific Computing. |
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Team Members
Faculty
Biography
In the pursuit of innovative solutions to some of the world's most pressing health challenges, my research revolves around constructing advanced statistical models for predicting infection outbreaks. My PhD and post-doctoral research focused primarily on understanding the evolution of epidemic and pandemic viruses. Currently, these models are tailored to forecast Dengue outbreaks in tropical countries and Influenza outbreaks in the US. My work is not just theoretical; along with the team in the Santillana lab, the Dengue forecasts I produce play a crucial role in supporting clinical trial programs conducted by Johnson and Johnson. Similarly, the Influenza models I've helped to develop are instrumental in contributing to the CDC's Flusight project. The complexities of this research demand a multifaceted approach. I employ a diverse range of methods, bridging the domains of machine learning, infectious disease epidemiology, and time series forecasting. I use classic time series and multivariate methods as well as both custom-produced supervised and unsupervised learning techniques. Subsequent stages involve error analysis and the visualization of our findings. Collaboration is central to the success of these projects. I routinely interact with both governmental and non-governmental stakeholders. In addition, I am a physician and routinely communicate with clinical teams regarding the application of public health data to clinical problems. |
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Postdoctoral Fellows
Biography
Dr. Binod Pant completed his PhD in Applied Mathematics at the University of Maryland, College Park (UMD), where his research focused on the intersection of mathematics and biology. His work involved utilizing mathematical theory, data analytics, and computational methods to gain insight into the transmission dynamics and control of emerging and re-emerging infectious diseases of public health importance. During the COVID-19 pandemic, Dr. Pant's research addressed several critical areas, including the impact of heterogeneity on herd immunity threshold, the impact of human behavior on disease transmission, and predicting hospitalization using wastewater surveillance data. |
Biography
Dr. Dewey is an incoming postdoc with the MIGHTE group. He completed my PhD in Epidemiology at UCLA, using network methods to assess questions from behavioral science, public health, and scientometrics. He hopes to use his background in network science and epidemiology to complement the ongoing outbreak forecasting efforts using non-traditional data. He’s also interested in the collection of high-quality data that could be used as a platform for future network research. |
Biography
I am a Postdoctoral Fellow at the Broad Institute of MIT/Harvard in the Eric and Wendy Schmidt Center. I am broadly interested in intersections of machine learning, dynamical systems, and biomedical sciences. My work focuses on improving the prediction and inference of biological and physical systems by blending machine learning, mechanistic modeling, and data assimilation techniques. I studied biophysics as an undergraduate at Columbia University, and did a PhD in Computing and Mathematical Sciences at Caltech under the supervision of Andrew Stuart. Outside of work, I enjoy playing music, going to concerts, playing tennis, skiing, and camping. |
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PhD Students
Biography
Nicole received her PhD in Population Health Sciences (Epidemiology) from the Harvard T. H. Chan School of Public Health in 2024, with Dr. Mauricio Santillana as her advisor. Her research centered on digital epidemiology, particularly the creation and application of machine intelligence methods that leverage disparate digital data sources to improve infectious disease surveillance. Nicole has also held a number of roles in biotech, biopharma, and venture capital. She earned her S.B. in biological engineering from MIT in 2018. |
Biography
Raúl is a second-year Physics PhD student working with Professor Mauricio Santillana. He is interested in the application of physics and mathematical modeling in the study of epidemics and human behavior. Prior to joining NetSI, he received a B.S. in Physics and a minor in Applied Mathematics from Purdue Univeristy Northwest. |
Biography
Xiyu is an incoming PhD student in Network Science. She graduated from Harvard Data Science Master’s program in 2023 and started working with Prof. Mauricio Santillana as a research assistant. Her research project was about transforming disease images into low-dimensional vector embeddings. During her PhD, she would like to cultivate her research interests in graph machine learning and computer vision with an application in medicine. Outside work, she loves practicing yoga and pilates, bouldering, reading books, and spending time with her cats. |
Biography
Daniel is a first-year Ph.D. student in Network Science. He has a background in Engineering Physics from the Autonomous University of Ciudad Juarez, in Mexico, where he worked on modeling evolutionary processes of pathogens using stochastic models for his undergraduate thesis. His research interests include developing mathematical and computational models that integrate complex sociotechnical structures with the evolutionary nature of pathogens to better understand the effects of evolution on the emergence and prevalence of epidemic outbreaks. In his free time, Daniel is an enthusiast of specialty coffee and competitive video games. |
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Biography
Tamanna is a 3rd year Ph.D. student in Network Science working with Prof. Samuel Scarpino on biological networks and Prof. Mauricio Santillana on network epidemiology. Her research focus is genomics to study diseases in a complex systems approach. She also applies network science for studying the spread of diseases. Tamanna completed her bachelor’s in Mechanical Engineering from MIT and went on to work in the renewable energy field for 2 years before realizing her passion in data science and computational research. She spent two years working as a data scientist in large ride-hailing and food delivery start-ups in Bangladesh (Pathao) and Indonesia (Gojek) before returning to academia as a Ph.D. student. She wants to do computational research in the space of biological systems, disease management and prevention. Apart from that, Tamanna enjoys singing semi-professionally, swimming outdoors, playing boardgames and doing cardio exercises. |
Biography
I am a machine learning researcher with multiple projects working on applying machine learning methods to time series forecasting. Some of my past works involved designing methods for epidemic tracking and prediction in diseases such as influenza, dengue, and COVID-19, in collaboration with Professor Mauricio Santillana. Last year I helped run the MIGHTE Group’s contribution to the CDC’s flu modeling prediction initiative, including designing the overall ensemble methodology. This led to a very strong result, placing among 2nd or 3rd in many tasks. At Northeastern University I will continue developing our methods for influenza forecasting, with a par1cular interest in improving real-time prediction of hospitalizations at the state level across the United States. In addition, I will conduct research on developing new techniques for multiple time series modeling with network interactions, extending state-of-the-art approaches such as vector autoregression and gradient boosting. Another aspect that will be of interest is speeding up the training and validation of sequential models using parallelization and novel optimization methods. |
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Staff
Biography
Leonardo Clemente is a Research Scientist and Data Infrastructure Manager at MIGHTE. Leo studied Physics Engineering where he worked with experimental optics and quantum mechanics. Leo pursued a Master's degree in Computer Science where he worked in Digital Epidemiology. He did research predicting diseases and their challenges in Latin America. Leonardo joined the MIGHTE Lab in 2017 where he mainly focuses on event prediction tasks such as epidemic outbreaks from different diseases including the flu, zika, dengue, Influenza, and malaria. |
Biography
Gemma has been the MIGHTE Lab Operations Manager since 2023. She supervises and coordinates all administrative activities and operations in our lab. Gemma supports human resources, financial activities, academic, and office matters, ensuring our lab's needs are met efficiently. Gemma holds a bachelor’s degree in business administration from the Catholic University of Bolivia and a master's degree in Quality Management of Health Services from National Institute of Public Health of Mexico. Before joining NetSI at Northeastern University, she held various positions there, including Project Manager, Head of the Planning Department, and Administrative Coordinator of the Doctor of Public Health Program. |
Co-op Students
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Currently pursuing Master's in Data Science from Harvard University, my research interests lie broadly around bayesian and time series modeling, causal inference and Explainable AI. I graduated in May 2023 from the Indian Institute of Technology Bombay and have previously worked on Machine learning applications in the life sciences and healthcare sectors. At the MIGHTE lab, I will be conducting research in computer vision using retinal images. |
Visiting Students
Biography
Niti Mishra is a Data Scientist with seven years experience in applying machine learning and natural language processing techniques to real-world problems in banking, retail and research. In 2022, she joined the EARLY-ADAPT (ERC-CoG) team at ISGlobal as a PhD candidate to investigate the application of language models and deep learning techniques for influenza-surveillance using digital data including multilingual texts from social media. Her research interests lie in implementation of machine learning techniques to monitor health related outcomes using weather and digital data sources. She obtained a M.Sc. in Data Science from Barcelona School of Economics in 2016. She has a B.Com in Economics and Management Science and a Minor in Sociology from Ryerson University (2013). Lines of research: Natural Language Processing Deep Learning Machine Learning Climate and Health |
Undergraduate Research Assistants
Biography
Yash R. Bhora is an undergraduate Physics & Data Science major at Northeastern University with aspirations for graduate study in Computational Physics. Born in India and raised in Thailand, Yash's interests lie in using computers to understand physical phenomena around us. His work with the MIGHTE lab involves understanding the coarsening effects of the SIR compartmental model for infectious diseases. Outside of this, he enjoys hip hop and breaking. |
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Affiliate Members
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Alumni
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David Castiñeira, PhD. Chemical Engineering, Postdoctoral Fellow, Massachusetts Institute of Technology
Team member: 2016 - 2017 Now: Principal Advisor for Emerging Technologies, Hess Corporation Gaston Fiore, M.S. Computer Science, Massachusetts Institute of Technology Team member: 2016 - 2017 Now: Entrepreneur Kaycie Schlosser, Medical Doctor, Chief Fellow, Boston Children's Hospital Team member: 2016 - 2017 Now: Pediatric Intensivist, Columbia University Irving Medical Center Gal Wachtel, B.S. Molecular & Cellular Biology, Harvard University Team member: 2017 Now: Data Scientist, Palantir Sam Tideman, M.S. Epidemiology, Harvard School of Public Health Team member: 2017 Now: Data Scientist, Northshore University Healthsystem Nick Generous, M.S. Epidemiology, Harvard School of Public Health Team member: 2017 Now: Scientist, Los Alamos National Laboratory Suqin Hou, M.S. Biostatistics, Harvard School of Public Health Team member: 2016 Now: Data Scientist, KAYAK Andre Nguyen, B.S. Applied Math, Harvard University Team member: 2014 - 2016 Now: Director of Machine Learning, JURA Bio, Inc. |
