Atish Agarwala

I am currently a physics PhD student at Stanford University advised by Daniel S. Fisher. Previously I studied math and physics as an undergraduate at Swarthmore College.

My research area is, broadly speaking, theoretical biology. During my PhD, I’ve primarily studied evolution - trying to understand how different dynamical processes combine to give evolutionary dynamics, and to characterize things like the speeed and predictability of evolution. I’ve worked on analyzing data from experimental evolution, and developed robust methods of inferring fitness from abundance data (code here). I used my code to understand the nature of fitness gains in glucose limited yeast (in collaboration with experimentalists from the Petrov and Sherlock labs at Stanford).

Most of my work consists of building mathematical models of different evolutionary scenarios, and trying to understand them both quantitatively and qualitatively. I studied evolution in the presence of epistasis (interactions between the effects of mutations) by developing a class of random fitness landscapes to model the complexities of fitness in real biological systems. I derived a computational and analytical framework to understand the stochastic dynamics quantitatively in the low-mutation rate regime, and showed how evolution now is heavily conditioned on past evolution. I’m currently working at the intersection of ecology and evolution, trying to understand how host-pathogen interactions and spatial structure might stabilize the within-species diversity found in even well-mixed microbial ecosystems.

I’m also interested in theoretical neuroscience and theoretical machine learning. I want to understand the scaling laws of neural systems - how various geometric and dyamical quantities change with network size, neural statistics, learning rates, and other network/dynamical parameters. In 2018 I did a research internship at Google Brain, studying early learning dynamics with mean field theory to try and understand the performance of different initialization statistics.

For more on my background, check out this interview conducted as part of my CEHG fellowship.

Links:

Google scholar

CV