Biostatistics – Biomedical Informatics – Big Data (B3D)

Co-organized by the Department of Biostatistics at the Harvard T.H. Chan School of Public Health and the Department of Biomedical Informatics at Harvard Medical School, the Biostatistics - Biomedical Informatics - Big Data (B3D) Seminar is a series of research talks on statistical, computational, and machine learning methods for analyzing large complex data sets, with a focus on applications in biomedical science and public health, including:

  • Genetics and genomics
  • Epidemiological and environmental health science
  • Comparative effective research
  • Electronic medical records
  • Digital health
  • Neuroscience
  • Social networks

The goal of the seminar is to provide a forum for brainstorming and exchanging ideas, and promoting interdisciplinary collaboration among researchers from a variety of disciplines such as biostatistics/statistics, biomedical informatics, computer science, computational biology, biomedicine, public health, social sciences, and other related areas. The seminar will feature local, national, and international speakers who are leaders in their field.

Selected Mondays
*All lectures will be virtual until further notice*

Recordings of talks will be made available on our YouTube channel

HMS Countway Library (scan ID or sign in at desk if no ID, take elevator to fifth floor)

For complete details, visit

B3D Mailing List – sign up to receive emails on the B3D Seminar Series and other news and events on big data and data science.

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Upcoming Virtual Lectures


Please see our DBMI Calendar for our Spring 2021 dates 

Recordings of talks will be made available on our YouTube channel

Next Seminar:
March 8, 2021 
1:00-2:00 PM

Sohini Ramachandran, PhD
Associate Professor of Ecology and Evolutionary Biology, Associate Professor of Computer Science
Brown University

Leveraging Linkage Disequilibrium to Gain Insights into Human Complex Trait Architecture
Across 15 years of genome-wide association (GWA) studies, 90.8% of the samples were from individuals of European ancestry, but recent research has shown that GWA variants and effect sizes identified in one ancestry do not readily replicate in other ancestries. Multiethnic GWA studies to date have assumed that the true effects of variants are the same for all individuals, discounting the possibility that genetic trait architecture may differ across ancestries at multiple genomic scales—from variants to genes to pathways. Thus, it is unknown whether results from over 4500 GWA studies to date are relevant to the majority of the human population. I will introduce recent methodological advances in my group for characterizing complex trait architecture and identifying genomic targets of positive selection, with the ultimate goal of developing frameworks for characterizing evolutionary processes underlying complex trait architecture within and across human ancestries. In particular I suggest that medical genetics and evolutionary genetics should not be divorced as they have been, but rather population geneticists should develop theory and methods for identifying evolutionary scenarios that produce ancestry-specific trait architectures.