Paarth Gulati
Tarbutton Postdoctoral Fellow, Department of Physics, Emory University
I’m a physicist interested in collective behaviour: how many interacting parts, from molecules to cells, come to move and organise as a whole. Near equilibrium, physics has a reliable recipe for describing such systems with a few coarse-grained variables. Living systems, however, are driven far from equilibrium, and most of those guiding principles no longer apply. My goal is to build effective, coarse-grained models for them anyway.
I approach this from two directions. During my PhD at UC Santa Barbara with M. Cristina Marchetti, I worked bottom-up, building continuum theories of active fluids, whose components burn energy to drive their own flows. With simulations, and experiments from the Dogic lab, we showed how activity reshapes phase separation and interfaces. Now, with Ilya Nemenman, I work top-down: I develop physics-minded machine-learning methods that learn the effective variables of a broad class of dynamical systems directly from high-dimensional data, such as videos of experiments, and estimate how many of them actually matter for predicting what the system does next.
Interests
- Biological physics
- Data-driven discovery
- Machine learning
- Soft and active matter
Education
- PhD in Physics, 2025University of California, Santa BarbaraDissertation: Phase Separation and Interfaces in Active Liquid Crystals
- BS (Research) in Physics, 2019Indian Institute of Science, Bengaluru
News
- Attended the Genesis Mission hands-on hackathon at Argonne National Laboratory.
- Had a great week at the Junior Scientist Workshop on Theoretical Biophysics at Janelia Research Campus, getting to know the theoretical biophysics community. I gave a talk and a tutorial, Predictive information and low-dimensional structure in dynamical systems.
- New preprint: Controlling turbulent flows in compressible active nematics, with Dimitrios Krommydas, Aparna Baskaran and Cristina Marchetti. Real active suspensions patterned with light develop large density variations, so we model them as effectively compressible fluids. Compressibility then becomes a control knob for turbulence: with patterned activity, it steers turbulent flow from high- to low-activity regions, and at a sharp activity boundary it holds the flow in a single chain of vortices.