Bio

Iā€™m a computational scientist with a background in chemical engineering. I am currently a Sr. R&D Engineer in the Computational Chemistry team at Nextmol, Barcelona, where I am employing molecular simulations and data-driven modeling methods to engineer complex polymeric systems for industrial applications.

General interests
  • Data-driven methods & applications.
  • Statistical mechanics, Enhanced sampling methods, Probability, Nonlinear dynamics.
  • Modeling (molecular), numerical methods, computational design.
  • Deep learning (AI for science), Coding, Algorithms, Network theory.
Most recent work

In my last role as a postdoc in Ferguson lab at University of Chicago, I focused on developing & applying data-driven methods comprising molecular simulations and machine learning to solve problems in biophysics & materials science.

  • Development and implementation of enhanced sampling methods in SSAGES and PySAGES.
  • Artificial neural network based CV development in PLUMED.
  • Applying active learning to design switchable materials composed of dielectric nanoparticles.
  • Machine learning (ML) based solution for tackling a grand challenge in water.
  • Multiscale modeling of complex biological processes using machine learning.
Previously

  • I have a background in chemical engineering and trained as a computational molecular scientist during graduate research at Sarupria research group, Department of Chemical Engineering, Clemson University.

    • Dissertation (PhD, 2019): Towards computer aided engineering of proteins and protein-surface complexes.
    • Thesis (MS, 2015): Understanding molecular interactions between proteins and carbon nanomaterials.

  • I also worked briefly as a software developer at mscripts, Exeter @Banglore.šŸ‘ØšŸ½ā€šŸ’»