Seminar Details

Modern experiments monitor biological systems with high resolution that may reach the
molecular level. Excessive noise caused by the measuring hardware and the experimental
procedures or unaccounted processes demand the formulation of specialized methods for
the analysis and interpretation of the acquired datasets. Nevertheless, physical limitations
and the inherent uncertainties in the underlying systems, such as unknown parameters,
states, or dynamics pose unique conceptual and computational challenges that lead to
intractable model selection problems. In this talk, I will present an overview on the difficulties
that are commonly encountered and highlight recent advances including novel Bayesian
non-parametric approaches which provide elegant alternatives to model selection.

Ioannis Sgouralis is a Postdoctoral Scholar in the Center for Biological Physics at Arizona
State University. Previously, he completed a postdoctoral fellowship in the National Institute
for Mathematical and Biological Synthesis (NIMBioS) at the University of Tennessee, Knoxville.
Ioannis completed his Ph.D. at Duke University and his undergraduate studies at the National
Technical University of Athens, Greece. His research is in the foundational aspects of Data
Science and his interests range from theory to implementation in several areas including
Physics, Chemistry, Biology, and Medicine