Difference between revisions of "David Thorsley, April 2008"

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==== Friday (25 Apr) ====
==== Friday (25 Apr) ====
{{agenda begin}}
{{agenda begin}}
{{agenda item|10:30|Julia}}
{{agenda item|10:30|Julia (329 Thomus)}}
{{agenda item|11:15|Erik Winfree}}
{{agenda item|11:15|Erik Winfree}}
{{agenda item|12:00|Lunch: Nok, Sayan, ___}}
{{agenda item|12:00|Lunch: Nok, Sayan, ___}}
{{agenda item|1:30|Sayan}}
{{agenda item|1:30|Sayan (334 Moore)}}
{{agenda item|2:15|Open}}
{{agenda item|2:15|Open}}
{{agenda item|3:00|Mani Chandy}}
{{agenda item|3:00|Mani Chandy (264 Jorgensen)}}
{{agenda item|3:30|Joseph Schaeffer}}
{{agenda item|3:30|Joseph Schaeffer}}
{{agenda end}}
{{agenda end}}

Revision as of 19:11, 22 April 2008

David Thorsley, a research associate with Eric Klavins at U. Washington, will be visiting Caltech on 24-25 April. This page is for keeping track of his schedule.


Thursday (24 Apr)

9:30   Nok Wongpiromsarn
10:15   Elisa
11:00   Seminar (CDS library)
12:15   Lunch: Nok, Dionysios, Joseph Schaeffer

Friday (25 Apr)

10:30   Julia (329 Thomus)
11:15   Erik Winfree
12:00   Lunch: Nok, Sayan, ___
1:30   Sayan (334 Moore)
2:15   Open
3:00   Mani Chandy (264 Jorgensen)
3:30   Joseph Schaeffer



David Thorsley
Research Associate
Department of Electrical Engineering
University of Washington

Thursday, April 24, 2008
11:00 AM to 12:00 PM
Steele 114 (CDS Library)

Understanding stochastic phenomena is a fundamental problem throughout science and engineering. When faced with new types of complex or complicated systems, we as engineers strive to develop intuitive methods that allow us to intelligently and safely make simple approximations, thus allowing us to more easily and efficiently answer questions about these systems. In the emerging field of systems biology, we are still striving to develop the necessary approaches to approximation that will make the analyses of such systems tractable. In this talk, I will present a general methodology for quantifying the differences between stochastic biochemical networks that can be used in to address the problems of model comparison, model reduction, and parameter optimization. The potential of this approach will be illustrated using a stochastic reaction network model of gene expression in bacteria.