Probabilistic models are common in biology. Many of the successful models have been readily tractable, leaving calculations on models with a combinatorial-sized state space as an open problem. Dr. Kirkpatrick's talk examines two kinds of models with combinatorial state spaces: continuous-time and discrete-time Markov chains. These models are applied to two problems: RNA folding pathways and family genetics. While the applications are disparate topics in biology, they are related via their models, the statistical quantities of interest, and in some cases the computational techniques used to calculate those quantities.
Dr. Kirkpatrick earned an undergraduate degree in Computer Science at Montana State University before moving to California, where she earned a Ph.D. in Computer Science from the University of California, Berkeley. Her doctoral dissertation was titled, "Algorithms for Human Genetics." Currently she is doing post-doctoral work in the Department of Computer Science at the University of British Columbia in Vancouver, Canada.
Mechanical Science and Engineering
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