Join us in welcoming Kevin Flores, an assistant professor from the Mathematics Department here at NC State University, as he discusses learning differential equation models from noisy biological data.
https://ncsu.zoom.us/j/91749207591?pwd=UnhkSldaQ290RG0rYitSVVZZK0lDUT09
Meeting ID: 917 4920 7591
Passcode: 303411
Equation learning aims to discover differential equations that describe the underlying dynamics of time series data. Biological systems have unique challenges in applying equation learning, namely, the observed data are sparsely measured and have high levels of proportional error noise. Moreover, equation learning methods can suffer from the model specification problem in which the governing differential equation model must be specified a priori either explicitly or as a library of candidate terms. This poses a challenge for biological systems, which are often described by nonlinear terms with unknown exponents. These issues help explain why equation learning has seen very few applications in real-world biological systems. In this talk, I will discuss methods that were developed to overcome these challenges. The methodology is demonstrated on simulated data of biological transport from the Fisher-KPP equation, experimental data of collective cell migration in scratch assays, and agent-based model simulations of a birth-death-migration process on a 2-d lattice.
Kevin Flores joined NC State in August 2015 as a Chancellor’s Faculty Excellence Program cluster hire in Precision Medicine. He is an associate professor in the Department of Mathematics and is a member of the Center for Research in Scientific Computation. Flores’ research focuses on mathematical modeling, optimal experimental design and uncertainty quantification with applications to precision medicine, systems and synthetic biology, and environmental toxicology.
He earned a Ph.D. in applied mathematics from the School of Life Sciences at Arizona State University. Previously, he was a postdoctoral fellow in NC State’s Department of Mathematics. He was a postdoctoral associate in the School of Life Sciences at Arizona State University and a bioinformatician in the Division of Biomedical Statistics and Informatics at the Mayo Clinic. He currently serves as co-chair of the Methods for Biological Modeling subgroup for the Society for Mathematical Biology and as an Associate Editor for the journal Mathematical Biosciences and Engineering.
