Megan Coffee, MD, PhD

Megan Coffee is an infectious disease physician and researcher, working on mathematical modeling and machine learning approaches to predict, identify and respond to infectious disease outbreaks in resource limited locations globally. She teaches on infectious disease outbreaks globally in resource limited settings with Prof Rachel Moresky through the Department of Population and Family Health. She received her undergraduate and medical degrees at Harvard University. She obtained her doctoral degree in mathematical modeling and epidemiology of infectious diseases from Oxford University, working on computer models of HIV spread in southern Africa, looking at migration and mobility. She completed her residency in internal medicine at Massachusetts General Hospital and her fellowship at UCSF. She is board certified in infectious diseases and internal medicine and is a licensed doctor in multiple states, including NY where she lives. She has worked globally on infectious diseases. She was the director of a tuberculosis ward at the main general hospital in Port-Au-Prince, Haiti for 4 years and continues to help TB and HIV patients with telemedicine home-based care, through the non-profit Ti Kay, which she runs with a Haitian and expat team. She was the coordinator in charge of multiple Ebola Units in Sierra Leone with the International Rescue Committee (IRC). She then was the IRC West Africa Health Advisor and later as a Communicable Disease Advisor working with outbreaks and infectious diseases in many countries. She has worked as a part of WHO, CDC, and UN working groups and meetings regarding infectious disease epidemics, including on antimicrobial resistance, Ebola, cholera, and vaccines in emergencies. She sees patients clinically at Bellevue Hospital in New York City and is a clinical assistant professor in infectious diseases at NYU Grossman School of Medicine. She is involved in research on computer-based tools and artificial intelligence for best responding to epidemics, including working on social media and vaccine hesitancy, AI based radiologic tools for tuberculosis classification and prediction and clinical severity tools for COVID and other infections.