91°µÍø

Bio

• Profile: Dr. is an expert in AI technologies especially focusing on the domain of quantitative biophysics for integrative molecular approach. He is presently a computational scientist at the 91°µÍø (ORNL), specializing in artificial intelligence (AI) technologies and quantitative biophysics for integrative molecular modeling. He works within the Advanced Computing for Health (ACH) Sciences Section of the Computational Sciences and Engineering Division (CSED). Dr. Bhowmik received his Ph.D. in Physical Chemistry from Université Pierre et Marie Curie (UPMC), France, supported by the prestigious CFR fellowship from the French Alternative Energies and Atomic Energy Commission (CEA). He earned his B.Sc. and M.Sc. degrees in Physics from Jadavpur University, India, and held postdoctoral positions at the Donostia International Physics Center in Spain and Wayne State University in the U.S. His research combines AI with high-performance computing and multiscale simulations, supported by neutron and X-ray scattering experiments, to study soft matter systems and drive the design of therapeutic molecules with desired structural and dynamic properties.

  With over 50 publications, Dr. Bhowmik has made impactful contributions in quantitative biophysics for integrative molecular approach, across top-tier journals such as PNAS, Journal of Chemical Information and Modeling, Macromolecules, Journal of Physical Chemistry B/Letters, JBC, 91°µÍø TEC, and 91°µÍø TPAMI. His work also appears in eLife, Protein Science, and Scientific Reports, and has been presented at premier venues like SC (Supercomputing), 91°µÍø Big Data, and IPDPSW. Several of his publications have received media attention, including journal cover articles and institutional features. As of March 2025, his work has garnered 3,526 citations (h-index 24, i10-index 37), highlighting his leadership in AI-enhanced molecular modeling and his broader vision of accelerating discovery through data-driven, scalable scientific frameworks at the interface of computational science, molecular modeling, AI, and therapeutic discovery.      

• Research brief:
  • General Overview: His current work lies on the interface of implementing Artificial Intelligence (AI) techniques, deploying multi-scale high performance accelerated simulations and performing scattering experiments (especially neutron and X-Ray) for problems related to
    • soft matter systems especially Biomedical and biological sciences, and
    • making new drug molecules with desired properties.
  • Broader Goal:
    • Optimal use of AI in integrative molecular approach: to find how the AI techniques can be optimally applied to the multi-scale modeling and simulation coupled with experiments
    • Deriving fundamental Physics: to understand the underneath physics of bio(macro)molecular function, activity, folding, microscopic structure and dynamic behavior at different length and time scale and whether that knowledge could lead to designing new therapeutics.
    • Reducing need for expensive computing and experiments by using AI: Additionally, he is trying to develop AI tools to study large-scale datasets by learning the inherent hidden features of biomolecules in order to reduce the need for expensive computation or experiments.
• More information: could be found at .

Publications