Tanima Chatterjee

Tanima Chatterjee serves as Clinical Assistant Professor of Computing & Data Sciences and Director of Undergraduate Studies in Boston University's Faculty of Computing & Data Sciences, a position she assumed in 2022. She holds a PhD in Theoretical Computer Science from the University of Illinois at Chicago, completed in 2021. Prior to joining Boston University, Chatterjee conducted postdoctoral research at Yale University, collaborating with molecular biologists on graph theoretic concepts and participating in the second phase of the PsychENCODE Consortium project within Yale's molecular biology department. Her research embodies a transdisciplinary approach to computing and data science, centering on the analysis of the human brain connectome using network topology to study neurodegenerative diseases. This focus originated in her doctoral thesis and was extended during her postdoctoral training. With several years of experience working with data, Chatterjee is committed to making coherent sense of abundant datasets across various domains.

In her role at Boston University, Chatterjee's primary responsibilities involve teaching foundational competencies and practical skills essential for students to address advanced data science subjects and practical applications. She instructs courses such as mathematical foundations of data science for the online Master's program and deep learning. Renowned for her ability to break down advanced mathematical concepts into intuitive, digestible explanations, her passion for teaching was shaped by growing up in a family of educators. She holds the conviction that the human mind is a blank slate capable of extraordinary achievements, finding deep fulfillment in contributing to its evolutionary growth. Chatterjee's key publications include "Single-cell genomics and regulatory networks for 388 human brains" in Science (2024), "Single-cell multi-cohort dissection of the schizophrenia transcriptome" in Science (2024), "Detecting network anomalies using Forman–Ricci curvature and a case study for human brain networks" in Scientific Reports (2021), "On the computational complexities of three problems related to a privacy measure for large networks under active attack" in Theoretical Computer Science (2019), and "On theoretical and empirical algorithmic analysis of the efficiency gap measure in partisan gerrymandering" in Journal of Combinatorial Optimization (2020).

Professional Email: tchatt2@bu.edu