Badhan Das
Exploring the intersection of biology, data, and computation
Exploring the intersection of biology, data, and computation
I am a Postdoctoral Associate in the Center for Genomics and Systems Biology, Department of Biology at New York University. My research focuses on developing and applying computational and machine learning approaches for genomic data analysis, with an emphasis on regulatory genomics, gene regulatory network inference, and sequence-based modeling.
I am a PhD graduate in Computer Science from Virginia Tech, specializing in Computational Biology and Bioinformatics. My research focuses on analyzing large-scale genomic data to uncover insights into evolutionary and epidemiological processes, and on developing computational models and machine learning tools to study complex biological systems, particularly in virology and antimicrobial resistance. My academic journey combines both teaching and research, with over 3.5 years of experience in academia. I previously served as a full-time faculty member at Southeast University for more than three years and worked as an adjunct faculty member at Virginia Tech during the summer of 2024.
My doctoral research investigated the evolutionary dynamics of RNA viruses, modeling the interplay between mutation, selection, and transmission using graph-based frameworks. I developed ViraFit, a mechanistic simulation model that integrates fitness landscapes and viral quasispecies to study virus diffusion with mutations on contact networks. I also introduced the Variant Evolution Graph (VEG), a novel graph-based framework for representing SARS-CoV-2 evolution and disease transmission networks from large-scale genomic data. Most recently, I proposed the Ancestor-Joining algorithm, which constructs a Mutation Learning Graph (MLG) to organize strains based on mutational transitions.