Mehdi is a Northwestern University graduate with a background in Electrical Engineering, Physics and Artificial Intelligence. He is interested in research applying to AI/Machine learning in Epidemiology and Social Sciences. His research within the MOBS lab involves time series forecasting, simulation based inference, surrogate and generative models and Bayesian optimizations.

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Andy (he/him) is a research assistant contributing to the development of an existing agent-based model that simulates the COVID-19 outbreak in Massachusetts and analyzes high-resolution mobility data (SafeGraph and Cuebiq) under the supervision of Dr. Matteo Chinazzi. He had a bachelor's degree in Statistical Science from the University of California, Santa Barbara, and a Master's degree in Bioinformatics & Data Analytics from the Northeastern University. Before joining as a Research Assistant, he worked as a research co-op student at NetSI with Dr.Jessica Davis and Dr. Brennan Klein.

Ankit is a Computer Science M.S. graduate from Northeastern University, specializing in Natural Language Processing, Algorithms, HCI, and Programming Languages. His interests span Software System Design, Data Analysis, Computational Social Science, Digital Humanities, and Art. At the Network Science institute he is working under the leadership of Dr. Matteo Chinazzi on using large scale location data for epidemiological modeling and analysis.

Matteo Chinazzi is a Research Associate Professor at Northeastern University (Department of Physics), Roux Institute member, and Core Faculty at the Network Science Institute. He conducts research at the intersection between network science, data science, epidemiology, economics, and artificial intelligence. His research interests include: a) the development of computational and analytical models to study and forecast the spatial spread of infectious diseases; b) the development of agent-based models to create realistic representations of population dynamics; c) the study of human mobility and contact patterns using high-resolution large-scale de-identified location data; d) the development of computational frameworks that combine mechanistic epidemic models with machine learning/deep learning models; and e) the study of the evolution and structure of science and innovation.