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|Chris Wiggins, Columbia University|
|Monday, September 23, 2013, 2 p.m., building Marchstraße 23, room 6.004|
Advances in biological technologies over the past two decades have dramatically increased the abundance of data available to biologists, and thereby changed the relationship between biology and statistics. While this is most famously celebrated in the subfield of genomics (both sequencing and functional genomics), there is increasing need in the subfield of molecular biology, particularly for methods based on generative models motivated by biologists' domain expertise. A natural set of tools is that provided by inference with latent variables. In this talk I'll introduce one application of a variational approach to inference; I then present current work on a closely-related hierarchical modeling approach, based on collaborations with the Gonzalez lab at Columbia, for understanding time-series data insingle-molecule biophysics.
Chris Wiggins is an Associate Professor at the Department of Applied Physics and Applied Mathematics at Columbia University. He is also affiliated with Columbia's Center for Computational Biology and Bioinformatics. He is an applied mathematician with a Ph.D. in theoretical physics (Princeton University, 1998) working on computational biology. Focus areas include applications of machine learning, statistical inference, and information theory for the inference, analysis, and organization of biological networks.In 2011 he was selected among 25 "People to watch in Silicon Alley" (Crain's).
ContactProfessor Dr. Martin Opper