SMB2020 subgroup Poster Prize: Vahini Nareddy

Congratulations to Vahini Nareddy, one of the 4 winners of the SMB2020 PDEE poster prizes!

View the prizewinning poster here

I am Vahini Nareddy, a physics graduate student from UMass Amherst working in the area of classical statistical mechanics with application to ecology. Currently, I am working with Prof. Jon Machta towards my dissertation titled “Dynamical models of statistical physics for spatially-coupled ecological oscillators”. We work in collaboration with Prof. Alan Hastings and Dr. Shadisadat Esmaeili from UC Davis and Prof. Karen Abbott from Case Western.

Our research focuses on understanding and predicting emergent dynamical phenomena in spatially-coupled ecological systems. In graduate school, I was drawn to this research problem primarily due to a chance to work on applications of “Ising universality class”. Different systems such as magnets, neurons and masting trees share common behaviors near the critical transition. This phenomenon of sharing properties exactly among different systems is known as “universality” and is well studied in statistical physics. The Ising model in two dimensions is one of the simplest statistical models initially developed to explain the arrangement of electronic spins (either up or down) in magnets. Spatially-coupled, two-cycle ecological oscillators and many other systems share common features with Ising model and hence exist in Ising universality class. This ensures that a simple Ising model can replicate long-time properties of these ecological systems.

Our research currently focuses rather on dynamics of ecological oscillators. More traditionally this problem has been studied using nonlinear dynamical models and coupled lattice maps whereas our approach for this problem involves using techniques from statistical physics. Specifically, we use a dynamical Ising model with memory to represent the ecological oscillators with two-cycle behavior. This approach is advantageous in revealing properties essential for synchrony and can be more generally applied to other systems which lack details about underlying mechanisms. We use maximum likelihood inference methods and forecast prediction tools for analyzing the dynamics.

We have recently submitted this work to a journal and interested readers can check it out on the arXiv. This work encourages us to approach other complex biological and ecological systems which share common features with other universality classes.

I want to thank SMB for this award and also for giving me a chance to participate in the poster session and interact with peers from different backgrounds working on interdisciplinary research topics. My experience has been great and unique as this was my first virtual poster session and also my first professional society meeting outside traditional physics conferences.