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