About Me

I am a computational physicist working on the development of electronic-structure and many-body methods for quantum materials. My research focuses on wave-function and Green’s-function approaches, tailored to capture thermal, disorder and relativistic effects. I place a strong emphasis on open-source, high-performance software, and currently work as the core-developer for GREEN software ecosystem, implementing methods that bridge first-principles theory with predictive materials modeling.

For complete CV, see here.

Experience

Research Area Specialist - University of Michigan
Aug 2025 - present

Core developer for the Green-Phys software package, leading implementations of new Green’s function methods, training new users, and optimizing performance for modern HPC architectures. Key products include the following flagship packages in the GREEN ecosystem:

  • green-mbpt - first-principles GW and GF2 for solid-state systems
  • green-mbtools - initialization and post-processing tools for many-body calculations in green-mbpt
Postdoctoral Research Fellow
Jan 2022 - Aug 2025
Developed first-principles Green’s function approaches for electronic structure of solid-state systems, with emphasis on relativistic effects, disorder, and topological phases.
Scientist Engineer - SAC-ISRO
Sep 2015 - Jul 2016
Managed 24/7 thermal-vacuum lab operations, supervised staff, maintained equipment and budgets, and contributed to the design of a large-scale vacuum chamber

Education

2016 - 2021
MS & PhD in Physics and Astronomy
Rice University, Houston, TX
GPA: 3.95 out of 4.0

Dissertation: Wave function theories for finite-temperature electronic structure

Key Publications:

2011 - 2015
Bachelor of Technology in Physical Sciences
Indian Institute of Space Science and Technology, Thiruvananthapuram
GPA: 9.2 out of 10.0

Academic Highlights

  • Institute Gold Medal for outstanding academic performance
  • Director’s Gold Medalist for best all-round performance

Major Projects

  • Non-linear semiclassical magnetization dynamics in nanoscale ferromagnets.
  • Neural Network and Bayesian classification of ionospheric data sets.