Bio/Links

Bio/Links

I am a Postdoctoral Fellow in the Climate and Ecosystem Sciences Division at Lawrence Berkeley National Laboratory

Talks and presentations

A Bayesian Approach for Data-Driven Dynamic Equation Discovery

August 10, 2022

Talk, Joint Statistical Meetings, Washington DC, Washington DC

Abstract

Many real-world geophysical, ecological, and biological processes are governed by complex nonlinear interactions, and differential equations are commonly used to explain the dynamics of these complex systems. While the differential equations generally capture the dynamics of the system well, they impose a rigid modeling structure that assumes the dynamics of the system are known. In many complex systems we may know some dynamical relationships a priori, but not the form of the governing equations. Discovering the form of the governing equations can lead to a better understanding of these complex systems and the interactions within. Here, we present a Bayesian data-driven approach to nonlinear dynamic equation discovery that can accommodate measurement noise and missing data. We show the effectiveness of our method on simulated data and apply the method to real-world processes.

Data-Driven Approach to Nonlinear Dynamic Equation Discovery

August 09, 2021

Talk, Joint Statistical Meetings, Virtual Conference, Virtual

Abstract

Many real-world atmospheric, ecological, and economic processes are governed by complex, non-linear, interactions, and differential equations are commonly used to approximate the dynamics of these complex systems. While the approximating differential equations generally capture the dynamics of the system well, they impose a rigid modeling structure that assumes the dynamics of the system. Recently, there has been work in the applied math and computer science community to use a data-driven approach to learn the dynamics that govern complex systems. Here, we present a Bayesian data-driven approach to non-linear dynamic equation discovery that is robust to measurement noise and stochastic forcing. We show the effectiveness of our method on simulated systems and then apply the method to a real-world environmental process.

On the Spatial and Temporal Shift in the Archetypal Seasonal Temperature Cycle as Driven by Annual and Semi-Annual Harmonics

August 04, 2020

Talk, Joint Statistical Meetings, Pennsylvania Convention Center (Virtual), Philadelphia, PA

Abstract

Statistical methods are required to evaluate and quantify the uncertainty in environmental processes, such as temperature, in a changing climate. Typically, annual harmonics are used to characterize the variation in the seasonal temperature cycle, overlooking the semi-annual harmonic, which can account for a significant portion of the variance of the seasonal cycle. Together, the spatial variation in the annual and semi-annual harmonics can play an important role in driving processes that are tied to seasonality. We propose a multivariate spatio-temporal model to quantify the spatial and temporal change in minimum and maximum temperature seasonal cycles as a function of the annual and semi-annual harmonics. Our approach captures spatial dependence, temporal dynamics, and multivariate dependence of these harmonics through spatially and temporally-varying coefficients. We apply the model to minimum and maximum temperature over North America for the years 1979 to 2018. Model inference within the Bayesian paradigm enables the identification of regions experiencing significant changes in seasonal temperature cycles due to the relative effects of changes in the two harmonics.