NC State
Muse Lab at NC State

This graduate training program has been funded by NIH since 2008 (NIGMS grant number T32 GM081057) and currently supports five PhD students per year. The goal of the program is to provide a cross-disciplinary training experience to students that bridges the fields of biostatistics and bioinformatics. All students take a set of core courses and take part in a collaborative data analysis experience with a genomic sciences laboratory on the NC State campus.

Program Overview

The overall goal of the training program is to combine training in biostatistics and statistical genetics/bioinformatics, recognizing that genetic data have become an important and standard component of biostatistical research in the academic, industrial, and government sectors. Our training program includes a curriculum with standard statistics PhD coursework at its core, but is supplemented with courses to provide an understanding of genetic data and the statistical issues surrounding the analysis of those data. In addition to the genetic data analysis coursework, the second piece of our program that makes it unique from other biostatistics courses of study is something we are calling a “genomics immersion”.  For a minimum of one semester trainees will collaborate with a genomics laboratory on campus, learning about the biological topics studied in the lab, the relationship of that work to public health, and identifying and assisting with data analysis problems that arise from the lab’s research. This experience provides first-hand knowledge of research performed in an interdisciplinary setting. 

Trainees are expected to pursue dissertation research in the broad areas of biostatistics, statistical genetics, or bioinformatics. There are no restrictions on the choice of advisor, aside from the faculty member being a member of the Department of Statistics. Trainees are expected to take a few extra courses in the biostatistics, bioinformatics and genomics area, but no formal background in biology is required. For students with some exposure to this area, we will tailor the choice of courses to match their background and research interests. The first year will be primarily focused on the “standard” statistics first year courses and passing the Basic qualifying exam. A very nice aspect of the training grant is that while trainees are supported on it, they have no work responsibilities such as teaching problem sessions or grading papers.

The training provides a 12-month stipend of $25,000, and it also covers tuition, required fees (currently $1,250/semester and not usually covered by TA positions), and student health insurance. An annual travel allowance is in place to allow students to attend a scientific conference of their choice, and funds are available for items to support their training, such as a laptop computer. The expected period of support is three years, assuming satisfactory academic performance. After that period students return to the financial support arrangement offered by the Department of Statistics (the training grant would replace, not supplement, the funding offered to you by the Department during the period you are supported by the grant).

Trainees and Outcomes

Our past trainees have moved on to excellent positions in academia, government, and industry. We have had 12 students complete their PhDs, with an average time to degree of 5.4 years; 4 of the 12 PhD graduates have taken faculty positions. 5 past trainees left our program after completing a Masters degree, and we have 10 trainees who remain in training.

  • Arielle Balthazard (in training)
  • Chad Brown (PhD 2012): Bioinformaticist, Quintiles
  • Karen Carroll (MR 2013): Mathematics Teacher, Ravenscroft School
  • Tina Davenport (PhD 2013): Biostatistician, Duke University
  • Kyle Duke (in training)
  • Marschall Furman (in training)
  • Hilary Graham (MR 2014): Biostatistician, Eli Lilly
  • Kevin Gunn (in training)
  • Sarah Hale (PhD 2016): Biostatistician, Duke University
  • Nick Kapur (in training)
  • Meredith King (PhD 2018): Data Scientist, Northrop Grumman
  • Megan Neely (PhD 2011): Assistant Professor, Duke University
  • Alexandra Larsen (PhD 2018): Postdoc, Duke University
  • Kristin Linn (PhD 2014): Assistant Professor, University of Pennsylvania
  • Kara Martinez (in training)
  • Emma Morrison (MR 2013): World Relief
  • Jami Mulgrave (PhD 2018): Postdoc, Columbia University
  • Grace Rhodes (in training)
  • Sarah Riegel (in training)
  • Sidd Roy (MR 2018): Mathematical Statistician, FDA
  • Cheyenne Swanson (MR 2018): Nutrition and Economic Data Analyst, USDA
  • Bryan Ting (in training; changed to PhD program in Bioinformatics)
  • Joe Usset (PhD 2014): Algorithm Analyst, Apple
  • Rachel West (PhD 2017): Statistical Geneticist, UNC-CH
  • Ander Wilson (PhD 2014): Assistant Professor, Colorado State University
  • Stacey Winham (PhD 2011): Associate Professor, Mayo Clinic
  • Kade Young (in training)