Ken Boucher Group

Kenneth Boucher

Kenneth Boucher

Kenneth M. Boucher, PhD
kenneth.boucher@hci.utah.edu

Cancer Center Bio


 

My career path has taken several seemingly random turns, which is perhaps fitting for a statistician. After high school I enrolled at Washington University in St. Louis intending to study chemical engineering. After an engineering professor informed a class I was in that it didn’t matter why the reaction equations on the board worked as long as they worked, I discovered that I was more interested in theory than practice.  I switched to math. Not knowing exactly what I wanted to do with a math degree after college I completing the requirements for all three tracks - probability and statistics, applied math and pure math.  After graduating from Washington University I pursued graduate training in pure math, studying dynamical systems, combinatorial group theory, and geometric topology at Notre Dame and then the University of Michigan. I eventually completed a PhD dissertation at the University of Michigan in geometric topology.  (My dissertation has applications to string theory).  After graduating I took a three year position in the math department at the University of Utah.

While at the University of Utah, my interest in statistics resurfaced. I obtained a one year position in the Department of Family and Preventive Medicine teaching biostatistics in the MPH program and analyzing air pollution data. I was recruited to Huntsman Cancer Institute in 1996 to work with Tomi Mori and Andrei Yakovlev in the areas of cancer biostatistics and applied mathematical modeling. I have been at HCI ever since.

At HCI the majority of my time is spent on very rewarding collaborative statistical consulting with basic scientists, epidemiologists and clinicians. My personal research interests are varied. They include, among other things, Markov Chain Monte Carlo algorithms for genetic epidemiology, carcinogenesis modeling, identifiability of stochastic models, asymptotic properties of statistical methods, and methods for analysis of high-throughput genomics data.