Modeling the COVID-19 Pandemic Using Statistical and Machine-Learning Methods
December 2, 2020, 4:00 P.M. ET
ABSTRACT: In this lecture, Dr. Majumder will discuss the work that her team has conducted between January 2020 and present in response to the COVID-19 pandemic.
BIOGRAPHY: Dr. Maimuna (Maia) Majumder is a member of the ladder-rank faculty at the Computational Health Informatics Program (CHIP) based out of Harvard Medical School and Boston Children’s Hospital and a recent graduate of the Engineering Systems program at MIT’s Institute for Data, Systems, and Society (IDSS). In between her graduate studies and her current position at CHIP, Dr. Majumder spent a year at the Health Policy Data Science lab at Harvard Medical School’s Health Care Policy department as a postdoctoral fellow. During her masters and doctoral studies at MIT, she was funded through a graduate fellowship at HealthMap.
Prior to Dr. Majumder’s arrival at MIT, she earned a Bachelor of Science in Engineering Science (with a concentration in Civil and Environmental Engineering) and a Master of Public Health in Epidemiology and Biostatistics at Tufts University. While at Tufts, Dr. Majumder was a field researcher with the International Centre for Diarrheal Disease Research, Bangladesh (ICDDR,B), where she worked with clinic patients (and their data) to learn how to better tell their stories.
Dr. Majumder's current research interests involve probabilistic modeling, artificial intelligence, and “systems epidemiology” in the context of public health, with a focus on causal inference for infectious disease surveillance using digital disease data (e.g. search trends; news and social media). She also enjoys exploring novel techniques for data procurement, writing about data for the general public, and creating meaningful data visualizations. As of January 2020, Dr. Majumder has been engaged in pandemic response efforts and is a leading expert in COVID-19 epidemiology.