Abstract: Collective behavior is ubiquitous in living systems (e.g. bacteria, fish schools, bird flocks and humans.) While there are several current models that successfully describe qualitative features of collective structures in animal behavior, the dynamical behavior of these systems in response to perturbation is not well understood. I show results of two recent experiments to better understand collective behaviour from a physicist point of view. Using midges, I study a disorganized aggregation (a swarm), and compare its natural fluctuations with the group-level response to an external stimulus. I quantify the swarm’s frequency-dependent linear response and its spectrum of intrinsic fluctuations, and show that the ratio of these two quantities has a simple scaling with frequency. In my second set of experiments, I explore the thermodynamics of fish schools. Leveraging the fish’s innate negative phototactic behavior, I employ both static light fields and dynamic light fields which apply a stress on the school increasing the density. Our results highlight how a materials and thermodynamic approach can give insights to current models.

Abstract: Throughout it's history, astronomy has been about using light from the heavens to make sense of our universe. With our eyes, then with telescopes, and eventually with cameras and detectors that have extended the range of the electromagnetic spectrum that we can observe, we have make great progress in understanding the universe and our place in it. Along with some important contributions from particle astrophysics, everything we know about the universe beyond our solar system comes from some form of electromagnetic radiation. With the detection of gravitational waves from merging black holes by the LIGO collaboration in 2015, scientists now have a brand new tool for astronomy. A tool that has already demonstrated its potential for answering, and asking, astrophysical questions. In this talk, I will give a brief overview of the physics and astrophysics of gravitational waves, the observations made to date, and prospects for the future of this exciting and emerging field.

Abstract: As people age, we accumulate damage. This damage is stochastic, and leads to variation in individual health and lifespan. We can characterize damage with the Frailty Index (FI), which is the proportion of accumulated health deficits. We model a population of aging individuals with a stochastic network model, where interacting nodes represent their health attributes. Our model recovers Gompertz' law of mortality, the observed increase in FI with age, and an empirical limit to the observed FI. With stochastic simulation, mutual information, mean-field theory, and network physics, our model allows us to explore how deficit accumulation occurs and why the FI works to predict health and mortality.