Special Statistics Lecture
STA 290 Seminar Series
Thursday, October 6th, 4:10pm, MSB 1147 (Colloquium Room)
Refreshments at 3:30pm before the lecture
Speaker: Axel Munk (Georg August Universität Göttingen, Germany)
Title: "Nanostatistics Statistics for Nanoscopy"
Abstract: Conventional light microscopes have been used for centuries for the study of small length scales down to approximately 250 nm. Images from such a microscope are typically blurred and noisy, and the measurement error in such images can often be well approximated by Gaussian or Poisson noise. In the past, this approximation has been the focus of a multitude of deconvolution techniques in imaging. However, conventional microscopes have an intrinsic physical limit of resolution. Although this limit remained unchallenged for a century, it was broken for the first time in the 1990s with the advent of modern superresolution fluorescence microscopy techniques. Since then, superresolution fluorescence microscopy has become an indispensable tool for studying the structure and dynamics of living organisms. Current experimental advances go to the physical limits of imaging, where discrete quantum effects are predominant. Consequently, this technique is inherently of a non-Gaussian statistical nature, and we argue that recent technological progress also challenges the long-standing Poisson assumption. Thus, analysis and exploitation of the discrete physical mechanisms of fluorescent molecules and light, as well as their distributions in time and space, have become necessary to achieve the highest resolution possible.
In this talk we discuss some physical principles underlying modern fluorescence microscopy techniques from a statistical modeling and analysis perspective. In the first part we discuss statistical methods for image deconvolution and more complicated issues of image reconstruction and enhancement, including adaptive variational multiscale methods for confocal and stimulated emission depletion (STED) microscopy, and motion correction. We illustrate that such methods benefit from advances in large-scale computing, for example, from recent tools from convex optimization. In the second part of the talk we address challenges of quantitaive biology which require more detailed models that delve into sub-Poissonian statistics. To this end we suggest a prototypical model for fluorophore dynamics and use it to quantify the number of proteins in a spot.
A second lecture will follow on Friday October 7, Titled "Multiscale Blind Source Separation" - abstract, time and location to be posted soon.