Recent technological and experimental advancements have allowed to collect large amounts of data in a wide variety of biological systems. As a consequence, there is a clear need for rigorous mathematical techniques to analyze and extract useful insights from this data, which are often noisy, incomplete, censored/truncated, and high-dimensional. For example, epidemic data, which are mostly reported in the form of an epidemic curve, are almost always incomplete, (left/right or interval) censored. Another challenge comes from noisy observations of biological aggregation across multiple scales.
This mini-symposium will focus on emerging probabilistic and topological tools applied to such complex large-scale biological systems. In particular, this session will connect researchers in topological data analysis, applied probability, classical and Bayesian statistics, uncertainty quantification, and machine learning. The talks will highlight some of the recent developments on the theoretical front as well as novel applications.
Maria-Veronica Ciocanel
Mathematical Biosciences Institute
The Ohio State University
Wasiur R. KhudaBukhsh
Mathematical Biosciences Institute
The Ohio State University
Francis C. Motta
Florida Atlantic University
USA.
Marilyn Vazquez
The Ohio State University
USA
Maria-Veronica Ciocanel
The Ohio State University
USA
Manuchehr Aminian
Colorado State University
USA
Wasiur R. KhudaBukhsh
The Ohio State University
USA
Arindam Fadikar
Argonne National Laboratory
USA
Pragya Sur
Harvard University
USA
Yuekai Sun
University of Michigan
USA