Hi, I'm Guru.

I'm a fourth-year Physics PhD student at UC San Diego, advised by Nigel Goldenfeld, working on the statistical mechanics of turbulence and machine learning.

The common thread in my work is how complex systems become unstable, transition, and organize — and I study it in two settings. In fluids, I showed that the laminar–turbulent transition in pipes with body forces is governed by tricritical directed percolation (Physical Review Letters, 2025), and I am testing its universality in stratified flows. In machine learning, I ask whether trained neural networks escape the random-matrix instability that May's complexity–stability theorem predicts for complex systems.

My tools: non-equilibrium phase transitions, finite-size scaling, stochastic modeling, and random-matrix theory.

Before UCSD, I did a Dual Degree in Engineering Physics at IIT Bombay, working on quantum condensed matter with Hridis Kumar Pal on topological insulator–superconductor junctions. I also spent time at TIFR Mumbai on electron–phonon equilibration via Keldysh field theory, and at Aalto University on fluctuations in non-centrosymmetric superconductors.

Affiliations

Current

2022 – present

UC San Diego Physics

Ph.D. candidate in theoretical physics. Thesis work: transition to turbulence under body forces, stratified flows, and the statistical mechanics of machine learning.

Education

2017 – 2022

IIT Bombay

Dual Degree (B.Tech + M.Tech) in Engineering Physics, specialization in Nanoscience. Master's thesis on topological insulator–superconductor junctions.