Hi! We seek to understand the structure and resilience of biological systems - often marine ecosystems - in theory and in data. We use a ~50/50 mix of ecology and applied math, so check out the sections below for an idea of what we do and the tabs above for the questions we like - and our linked publications.

Math perspective:
We explore how cycles, chaos, and phase shifts among attractors play out across networks - in particular, networks with heterogeneous nodes or dynamic interactions. For this, we use attractor reconstruction (aka Takens theorem) and Gaussian Processes (aka ‘deep’ machine learning) alongside classical approaches like ODEs, integrodifference equations, random matrices, and maximum likelihood. Foremost, we focus on finding novel dynamics and the simplest models that capture them.

Ecology perspective:
We look for the key ecological processes that predominantly shape ecosystems and their resilience. Depending on the system, these processes range from competition and climate change to animal behavior.

To do this, we build simple models that incorporate competing ecological hypotheses, fit these models to spatiotemporal data, and see which model (ie, hypothesis) better explains patterns in nature. When fitting mechanistic models, we also get estimates of ecological rates, interactions, and resilience “for free”. These are critical for management but notoriously hard to measure in the field.