Katy L. Simonsen
Associate Professor (currently on leave)
Department of Statistics, Purdue University
Email: simonsen@stat.purdue.edu
Dr. Simonsen's research is at the interface of statistics, genetics, applied probability, mathematical modeling, and scientific computation. The major objective of Dr. Simonsen's multidisciplinary research is to understand and include the forces which contribute to genetic variability in natural populations, and the consequences of that variability on future natural populations and our ability to interpret data from present-day organisms. Natural populations of organisms such as humans are produced by naturally occurring matings without experimental intervention. Understanding the way DNA sequences vary among organisms is essential to our ability to detect and locate genes for specific traits and diseases. The impact of this can be felt in highly prevalent complex diseases such as diabetes and mental illness, and in disease management strategies such as pharmacogenomics. Phenomena such as natural selection, genetic recombination, population structure, mutation, and random genetic drift are examples of major forces that interact to create, maintain, and eliminate genetic variability. Dr. Simonsen's research is centered on developing novel mathematical and computational models of such phenomena, and uses these models to construct reliable procedures for estimating parameters and testing hypotheses about genes, traits, organisms, and their natural history.
Education and Employment
2006-present: Associate
Professor of Statistics, Purdue University,
1998-2006: Assistant
Professor of Statistics, Purdue University,
1996-1998: Post-Doctoral Research Associate with Bruce Weir in the Program in Statistical Genetics , Department of Statistics , North Carolina State University , Raleigh, NC.
1996: Ph.D.
Centre For Applied
Math, Cornell University
1993: M.Sc. Centre For Applied Math, Cornell University, Ithaca, NY.
1990: B.Math, Applied Mathematics, Faculty of Math, University of Waterloo, Waterloo, ON.
Teaching
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Statistics 512: Applied Regression Analysis (Fall 2005) Division 2, MWF
3:30pm, REC 123 Statistics 490B: Introduction to Bioinformatics (Fall 2005) MWF 12:30pm, Lilly
3102 Statistics 598M: Probability
Module for CLS (Fall 2005) Division 1, TuTh
12:00, REC 123 (Sept. 27 – Nov. 1) Statistics 598S: Statistics
Module for CLS (Fall 2005) Division 1, TuTh
12:00, REC 123 (Nov. 3 – Dec. 8) |
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Statistics 517: Statistical Inference (Spring 2005) MWF 2:30 pm, BRNG B222 Statistics 490B: Introduction to Bioinformatics (Spring 2005) MWF 12:30pm, Lilly
G-432 Statistics 503: Statistical Methods for Biology (Spring 2005) Course Coordinator Statistics 503: Statistical Methods for Biology (Fall 2004) Course Coordinator |
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Statistics 490B: Introduction to Bioinformatics (Spring 2004) MWF 12:30pm, Lilly
G-432 Statistics 512: Applied Regression Analysis (Fall 2003) Division 3, TuTh 1:30
pm, UNIV 101 Division 4, TuTh 12:00
pm, UNIV 101 |
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Statistics 512: Applied Regression Analysis (Spring 2003) Division 1, MWF
10:30am, UNIV 219 Statistics 490B: Introduction to Bioinformatics (Spring 2003) MWF 12:30pm, Lilly
3102 Statistics 512: Applied Regression Analysis (Fall 2002) Division 1, TuTh 9:00
am, UNIV 201 Division 4, TuTh 10:30
am, UNIV 203 |
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Statistics 517: Statistical Inference (Spring 2001) TuTh , 12:00 pm, LAEB B268 Statistics 503: Statistical Methods for Biology (Fall 2000) Division 1, MWF 10:30 am, LILY 2102 Division 2, MWF 11:30 am, LILY 2102 |
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Statistics 517: Statistical Inference (Spring 2000) MWF 2:30 pm, POTR 262 Statistics 503: Statistical Methods for Biology (Fall 1999) Division 2, MWF 11:30 am, LILY 2102 Division 3, MWF 12:30 pm, LILY 2102 |
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Statistics 503: Statistical Methods for Biology (Spring 1999) Division 3, MWF 12:30 pm, LILY 2102 Division 5, MWF 2:30 pm, LILY 2102 Statistics 503: Statistical Methods for Biology (Fall 1998) Division 7, MWF 3:30 pm, UNIV 003 |
Research
My research is in the area of Statistical Genetics. This field uses knowledge of both Statistics and Genetics in order to extract information from complex genetic data. Specific interests include
- coalescent models: stochastic processes used to study of the sampling properties of genetic data based on historical relationships
- linkage studies: determining the chromosomal location of genes involved in diseases and other complex traits
- hypothesis testing: finding powerful tests for population genetic hypotheses, and simulating data in order to study power in a wide variety of circumstances
Papers
Software
Interesting Links
Advice for Graduate Students (from Stephen C. Stearns)
Personal Information
My CV.
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