Monday, 15 January 2024 @ 14:00–15:00 CET
On-site:
University of Vienna
Seminarraum 18
Kolingasse 14–16
1090 Vienna
Online:
Zoom-Link: https://univienna.zoom.us/j/68154688415?pwd=anBCaXlnZ0ZRZ0R2YkV0U2hMSXdIQT09
Meeting-ID: 681 5468 8415
Passcode: 517043
Statistical analysis and optimality of biological systems
Abstract:
Optimization theories and statistical inference provide complementary approaches for the study of biological systems. An optimization theory, also called a "normative theory", postulates that organisms have adapted to efficiently solve essential tasks and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on the efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function. Traditionally, these two approaches were developed independently and applied separately. Here, we unify them in a coherent Bayesian framework that embeds a theory into a family of maximum-entropy ''optimization priors.'' This family defines a smooth interpolation between a data-rich inference regime and a data-limited prediction regime. Using simple examples from neuroscience and gene regulation, we demonstrate that our framework allows one to address fundamental challenges relating to inference in high-dimensional, biological problems.
Bio:
Gašper Tkačik studied physics at the University of Ljubljana and at Princeton University, where he received his PhD in 2007. From 2008 to 2010, he held a postdoctoral position at the University of Pennsylvania, Philadelphia. In 2011, he returned to Europe as Professor at the Institute of Science and Technology Austria, where he established the research group “Information Processing in Biological Systems“. Tkačik has received several awards for his research, including grants from the Human Frontier Science Program (HFSP), from the Austrian Science Fund FWF and the Lieben Prize of the Austrian Academy of Sciences (ÖAW) in 2019.
