Hi! I’m Matthew

I build statistical methods for cancer genomics, focused on tumor evolution and transcriptomic deconvolution. Recent work includes pan-cancer subclonal reconstruction across 9,000+ samples and DeMixNB, a sparse-data deconvolution method applied to bulk and spatial tumor data.

I hold a PhD in Quantitative and Computational Biosciences from Baylor College of Medicine in Houston, Texas. My thesis work was conducted under the guidance of Dr. Wenyi Wang at The University of Texas MD Anderson Cancer Center. I received my B.S. in Genetics and Biotechnology from Brigham Young University in Utah.

My research centers on creating and using statistical tools to better understand the biology of human health and disease. I focus on several key areas:

Tumor Evolution - Intra-tumor heterogeneity plays a crucial role in how tumors evolve and respond to treatment. My research focuses on understanding the clonal and subclonal architecture of tumors, which is key to advancing precision cancer treatment.

Cancer Genomics - I work on identifying genetic mutations that drive cancer. By developing tools to detect these mutations, I help map out the genetic changes across different types of cancer, aiding in the pursuit of personalized medicine.

Computational Deconvolution - Deconvolution is a powerful tool for identifying cell type- and tumor-specific patterns from bulk RNA-seq data. This is especially important for understanding the diverse cell populations within tumors. I developed DeMixNB to extend deconvolution to sparse-count data such as miRNA-seq and spatial transcriptomics.

Biomarker discovery - I collaborate with clinicians to identify meaningful molecular signatures that can predict cancer progression and response to therapy, with the goal of improving personalized treatment strategies.

Outside the lab, I enjoy cycling, disc golf, reading, 3D printing, cooking, and gardening.

This is my personal website where I maintain an up to date list of publications, and where we can get in contact