I am a final-year PhD scholar in computational polymer physics at IISER Pune, working with Prof. Srabanti Chaudhury. My research sits at the intersection of soft matter physics, biophysics, and statistical mechanics, and starts from one question: how do long-chain molecules behave in environments as dense and messy as the inside of a living cell?

Inside cells, DNA, RNA, and proteins never exist in isolation — they fold, move, and assemble in a background crowded with other macromolecules. My work uses coarse-grained molecular dynamics simulations to understand how intrinsic polymer properties interact with that environment. Three threads run through it:

Topology. Ring polymers have no free ends, and that single constraint changes almost everything about how they compact, adsorb onto surfaces, and move under crowding. My recent work shows that ring and linear polymers respond in qualitatively different ways to crowded interfaces — behavior relevant to bacterial chromosomes, plasmids, and chromatin.

Electrostatics and self-assembly. Charged and amphiphilic polymers respond to salt in ways that are anything but monotonic: multivalent ions can drive aggregation, collapse, re-expansion, and re-entrant condensation, and polyzwitterions famously expand where ordinary polyelectrolytes shrink (the anti-polyelectrolyte effect). I study the mechanisms behind these effects with minimal models that make the physics legible.

Transport. Polymer translocation through nanopores under crowding — how entropic barriers, depletion forces, and the geometry and flexibility of surrounding obstacles set transport pathways and timescales, with implications from biological transport to nanopore sequencing technologies.

Methodologically, everything is grounded in bead–spring models, statistical physics, and large-scale MD, with a growing interest in machine learning as a tool for extracting reduced models from simulation data.

AI and Industry Work

Alongside my PhD, I work with Mercor as a physical-sciences domain expert, designing and reviewing research-grade computational chemistry problems used to train and evaluate frontier AI models. The work is a kind of adversarial peer review: constructing problems whose solutions cannot be recalled or pattern-matched, then auditing them for scientific correctness, solvability, and information leakage. It has sharpened how I think about what models actually understand about physics — and about what makes a scientific problem genuinely hard rather than merely obscure.

Interactive Simulations

This site hosts a growing collection of interactive simulations — dependency-free, browser-based physics you can push around: active-matter particle life, graph search on energy landscapes, with more on the way. They are the playful end of the same interests that drive my research: minimal models, emergent behavior, and algorithms you can watch think.

Skills

Programming and Data Analysis

  • Python, MATLAB, Julia (scientific computing, data analysis, visualization)
  • C / C++ / FORTRAN (simulation and numerical methods)
  • JavaScript (browser-based interactive physics simulations)
  • Bash scripting for automation and workflows

Molecular Simulation Software

  • LAMMPS (coarse-grained molecular dynamics)
  • VMD (trajectory visualization and analysis)
  • GROMACS (familiarity with atomistic and coarse-grained workflows)

Machine Learning / AI

  • PyTorch, TensorFlow/Keras, NumPy, SciPy, Pandas
  • ML-assisted analysis and pattern extraction from simulation data
  • Design and evaluation of scientific reasoning tasks for large language models

Documentation and Writing

  • LaTeX (manuscripts, theses, presentations)
  • Static-site tooling (Hugo, Markdown, HTML) for academic web publishing

High-Performance Computing

  • HPC clusters: job scheduling, parallel workflows, and data management for long MD runs

Version Control

  • Git and GitHub for versioning, collaboration, and reproducible research

Outside Academia

Away from the keyboard I draw — pencil sketching and shading, which trains the same muscles as science: observation and patience. I read widely across fiction, non-fiction, and scientific writing, and I keep a regular gym routine, which does more for research productivity than most productivity advice.