Bio

Hi, I am currently a postdoc in Ferguson lab at the Pritzker School of Molecular Engineering, University of Chicago.

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.

Ongoing work

My recent work as a postdoc in Ferguson lab 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.👨🏽‍💻