Principal Investigator
Mauricio Santillana, Ph.D.
Professor of Physics and Electrical and Computer Engineering
​Network Science Institute,
Northeastern University

The Machine Intelligence Group for the betterment of Health and the Environment (MIGHTE), now based at the Network Science Institute, at Northeastern University (from 2017 to 2022, our research lab, the Machine Intelligence Lab, was based at Boston Children’s Hospital) has a multidisciplinary research agenda. Our research involves the conception and implementation of machine intelligence analytics tools, capable of predicting unobserved events in epidemiology and healthcare in the immediate or near future. Our work ranges from tracking disease outbreaks around the Globe, leveraging information from big data sets from Internet-based services (such as Google search activity, Twitter microblogs, Weather, Human Mobility, Electronic Health Records), to bed-side patient-centered monitoring approaches aimed at improving care in clinical settings. We also focus on the use of mathematical approaches to discover relationships between relevant outcomes in the health, physical and environmental sciences. For example we have studied the influence of climate change on the prevalence of antibiotic resistant infections, or the role of socio-economic factors and political leaning and mortality during the COVID-19 pandemic.
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Generally speaking, our approaches use machine learning techniques to identify patterns that have occurred historically that may be predictive of specific and future events of interest, for example:

• Can we characterize the time evolution of a disease outbreak in a population by analyzing: 
  • disease-related search activity on Internet search engines?
  • the local air temperature/humidity in their location?
  • human mobility patterns?
  • multiple disparate Internet-based data sources ?
• Can we identify real-time vital signs patterns in a patient's hospital visit that may suggest the need to intervene or change care plans, hours (or days) before this happens?
  
  

Our team consists of applied mathematicians, computer scientists, physicists, public health experts, and clinicians.  

Latest news

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9/19/2023
Team member Xiyu Yang gets their paper, titled 'De-identification and Obfuscation of Gender Attributes From Retinal Scans,' accepted in the Fairness of AI in Medical Imaging workshop at MICCAI 2023. Congratulations!

8/04/2023
Nicole Kogan was recently accepted to the ETH/EPFL Digital Epidemiology Summer School in Grindelwald, Switzerland on September 25-29, 2023. Organized by Marcel Salathé (Digital Epidemiology Lab at EPFL).

7/26/2023
Professor Santillana participated in the International Colloquium on Mathematical Modeling in Epidemiology, at the Fundação Getulio Vargas (FGV), Rio de Janeiro, Brazil on August 14th - 17th, 2023.

7/21/2023
Professor Santillana co-taught the module: Statistics and Modeling with Novel Data Streams at SISMID 2023, with Alessandro Vespignani, Jessica Davis, and Fred Lu.

6/01/2023
Leonardo Cazares chosen as a recipient of the Summer 2023 Scholarship "Beca de iniciacion a la investigacion" from UNAM (Mexico) to conduct research with our lab. Congratulations, Leonardo!