[Team ABI Seminar] Bayesian Statistics in Microbial Live-Cell Imaging and Analysis
This talk will be held in a hybrid format, both in person at Meeting Room C of RIKEN AIP (Nihonbashi office) and online by Zoom. AIP Open Space: *only available to AIP researchers.
DATE, TIME & LOCATION
Wednesday, October 15th, 11:00 - 12:00, RIKEN AIP Nihombashi Office, Meeting Room C
TITLE
Bayesian Statistics in Microbial Live-Cell Imaging and Analysis
ABSTRACT
Microorganisms play a vital role in human society, contributing significantly to food production and medicine. Modern experimental platforms enable the collection of high-throughput data, but in order to derive reliable insights from the large amounts of data collected, uncertainty-aware computational tools are crucial. In this talk, I present our recent work on uncertainty estimation in cell tracking, a key tool for analysis of single-cell microscopy data, as well as on higher-order Hit-&-Run algorithms for non-uniform constrained densities, which are useful in Bayesian inference of metabolic fluxes from isotope labeling experiments. Finally, I will give an outlook on current work and ideas on model selection using marginal likelihood estimation, with applications to combined changepoint detection and model selection for microbial growth dynamics.
BIO
Richard D. Paul is a fourth-year PhD student at Forschungszentrum Jülich and LMU Munich, supervised by Katharina Nöh, Hanno Scharr, and Prof. David Rügamer. His background is in Computer Science, which he studied at the University of Vienna. His research focuses on the development and application of computational statistical methods for analyzing biological data.
