I am a tenured Associate Professor in the Department of Computer Science at Virginia Tech.
My primary research interests are computational biology and applied machine learning with a particular focus on AI-powered molecular modeling.
I am also interested in applications of data science in bioinformatics.
My recent projects are focused on developing data-driven machine learning and optimization algorithms to elucidate complex relationships
between macromolecular sequences, structures, functions, interactions, and folding pathways.
I am a recipient of the NSF CAREER Award (2020), NIH MIRA (R35) Award from NIGMS (2020),
AU Ginn Faculty Fellowship (2020), AU Outstanding Engineering Faculty Award (2019), and MU Outstanding CS Ph.D. Student Award (2014).
Before joining Virginia Tech, I was an Assistant Professor at Auburn University (2017 - 2021) and at Wichita State University (2016 - 2017).
I obtained my Ph.D. in Computer Science from the University of Missouri - Columbia in 2016 under the supervision of Prof. Jianlin Cheng.
Office: 3120B Torgersen Hall
620 Drillfield Drive
Blacksburg, VA 24061
Phone: (540) 231-2865
Fax: (540) 231-6075
Email: dbhattacharya@vt.edu
NEWS
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11 Nov. 2024:
Announcements
We are always looking for self-motivated Ph.D. students. If you are interested, please apply through the CS at VT Ph.D. admissions process while mentioning my name in your application package and send me an email at dbhattacharya@vt.edu.
Important Note: If you are interested in working with us, the best thing to do is to read several of our recent papers to get a sense of our research and figure out mutual interest. Please understand that we receive a lot of emails/applications, and we may not be able to respond to inquiries that do not exhibit a clear match.
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lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures
Sumit Tarafder*, Debswapna Bhattacharya
Journal of Chemical Information and Modeling, 10.1021/acs.jcim.4c01621, 2024
Abstract • PDF • GitHub
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EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks
Rahmatullah Roche, Bernard Moussad, Md Hossain Shuvo, Sumit Tarafder, Debswapna Bhattacharya
Nucleic Acids Research, 52 (5), e27-e27, 2024
Abstract • PDF • GitHub
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The landscape of RNA 3D structure modeling with transformer networks
Sumit Tarafder*, Rahmatullah Roche*, Debswapna Bhattacharya
Biology Methods and Protocols, 9 (1), bpae047, 2024
Abstract • PDF • GitHub
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The transformative power of transformers in protein structure prediction
Bernard Moussad, Rahmatullah Roche, Debswapna Bhattacharya
Proceedings of the National Academy of Sciences, 120 (32) e2303499120, 2023
Abstract • PDF • GitHub
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E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction
Rahmatullah Roche, Bernard Moussad, Md Hossain Shuvo, Debswapna Bhattacharya
PLOS Computational Biology, 19(8): e1011435, 2023
Abstract • PDF • GitHub
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DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins
Sutanu Bhattacharya, Rahmatullah Roche, Bernard Moussad, Debswapna Bhattacharya
PROTEINS: Structure, Function, and Bioinformatics, 90(2): 579-588, 2022
Abstract • PDF • GitHub
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QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks
Md Hossain Shuvo, Sutanu Bhattacharya, Debswapna Bhattacharya
Intelligent Systems for Molecular Biology (ISMB), 2020
Bioinformatics, 36(S1): i285-i291, 2020
Abstract • PDF • GitHub
© Debswapna Bhattacharya,