Research

The world around us is full of complex dynamical systems, from the weather and climate, to financial markets, to the brain, and each of these systems is characterized by subtle fluctuations that encode information about their underlying mechanisms. How can we extract and understand these patterns from data, and use them to gain insight into the underlying mechanisms that generate them? Addressing this question requires connecting deep theoretical concepts about dynamical structure to the real-world applications for which they could be transformative. Our research is thus highly interdisciplinary, both in the methods that we develop and apply (from physics to statistical learning), and in the processes we study (from fluctuations of single living cells to whole-brain neural activity dynamics).

While our research projects are diverse in their specific aims and applications, we all have a common interest in dynamics, whether we study it on a theoretical level using numerical simulation, to develop new methods to quantify subtle dynamical patterns in real-world data measured from a complex systems, or to apply existing methods in creative ways to new types of problems.

Recent work includes developing new methods for tracking the distance to a critical point from time-series data, modeling complex correlation structures in time series using methods from quantum physics, quantifying time-irreversibility from time-series data, and tracking non-stationary variation in a dynamical recording as a way to better represent the continuous dynamical fluctuations often present in living systems.