Debsindhu Bhowmik

Computational Scientist

Home Page: 

For details visit:

  • Profile: Dr. Debsindhu Bhowmik is an expert in AI technologies especially focusing on the domain of quantitative biophysics for integrative molecular approach. He is presently a computational Scientist in Advanced Computing for Health (ACH) Sciences Section of Computational Sciences and Engineering Division (CSED) and Health Data Sciences Institute (HDSI) at the Oak Ridge National laboratory (ORNL). He holds Ph.D. from Université Pierre et Marie Curie (UPMC), France with highest remarks for his doctoral work and a B.Sc. and a M.Sc. in Physics from Jadavpur University, India. He received the prestigious CFR (Contrat de formation par la recherche) Fellowship to pursue his doctoral work from CEA (Alternative Energies and Atomic Energy Commission), France. Upon finishing his Ph.D. he joined as Postdoctoral Research Fellow at the Donostia International Physics Center, Spain and later at the Wayne State University, USA.    

  • 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