Updated: 16 December, 2023

Contact information

Expertise

Large-scale scientific project management.

Expert R software development, including modern and scalable C / C++ and database interfaces. REST and other API client development. Experienced AWS, Google, Azure cloud management.

Unique fluency in high-throughput molecular biology, informatics, and statistical approaches to comprehension.

Effective communicator with extensive experience training and motivating beginner, intermediate, and advanced users and developers of bioinformatic tools.

Academic and professional experience

  • Roswell Park Comprehensive Cancer Center, 2015-Present. Current position: Member (Professor)
  • Fred Hutchinson Cancer Research Center, 2005-2015.
  • Washington State University, 1996-2005. Assistant and Associate (tenured) Professor, School of Biological Sciences.
  • University of Chicago, 1988-1993. PhD in Evolutionary Biology.

Major accomplishments

I led the Bioconductor project (2009-2021) for the statistical analysis and comprehension of high-throughput genomic analysis. Bioconductor is an open-source, open-development project with contributions from 1000’s of academic and other researchers across the globe. It is widely used (more than 1M unique IP downloads annually) and highly impactful (more than 75000 ‘PubMedCentral’ citations in the scientific literature). I obtained major (US) federal and other funding, coordinated global project direction and vision, and formalized project leadership structure. I oversaw the project’s internet presence and computational resources, and contributed key ‘infrastructure’ packages for parallel computing and domain specific analysis.

Earlier in my career, I made significant technical contributions to understanding the evolutionary role of genome-wide ‘deleterious mutation’, including its role in patterning neutral molecular variation, generating inbreeding depression, and the selection and evolution of self-fertilization, migration, and life history.

During my gradate training, I developed understanding of selection on plant reproductive morphologies using quantitative genetic and ‘evolutionary stable strategy’ models coupled with statistical tools for the inference of paternal reproductive success.