Modeling the COVID-19 Pandemic Using Statistical and Machine-Learning Methods

December 2, 2020

Dr. Maimuna (Maia) Majumder

Faculty, Computational Health Informatics Program (CHIP), Harvard Medical School and Boston Children’s Hospital

REGISTER NOW →   The lecture will take place as a hosted virtual Zoom webinar and is open to all Stevens students, faculty, staff, alumni, and invited guests. Registration is required.

Maimuna Majumder Photo

ABSTRACT: In this lecture, Dr. Majumder will discuss the work that her team has conducted between January 2019 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, Maia 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 Maia’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, Maia 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.

Maia'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 2019, Maia has been engaged in pandemic response efforts and is a leading expert in COVID-19 epidemiology.