Innovative technologies now allow us to probe the genome in more dimensions and at higher resolution than ever before, providing a wealth of information for studying the genomic basis of complex traits. However, meaningful biological insights are often masked by technical artifacts, systematic biases, or low signal-to-noise ratio (“needle in a haystack”). These challenges demand tailored statistical methodology in order to unlock the full potential of emerging assays.
The Korthauer research group focuses on developing novel frameworks and rigorous inferential procedures that exploit the increased scope and scale of high-throughput sequencing data, with the ultimate goal of uncovering new molecular signals in cancer, child health, and development. Active projects include (1) unraveling the spatial landscape of epigenomic signals, (2) modeling the influence of epigenomic signals on gene expression, and (3) understanding the genomic and epigenomic basis of complex traits.
Current Lab Members
- Giuliano Cruz (MS student, Bioinformatics)
- Erick Isaac Navarro Delgado (MS student, Bioinformatics)
- Keegan Korthauer (Principal Investigator)
- Marco Tello Palencia (PhD student, Bioinformatics)
- Ning Shen (PhD student, Statistics)
We are an interdisciplinary group, with a blend of quantitative skills in mathematics/statistics, programming (mostly in R but other languages welcome), computational biology, and/or bioinformatics. I welcome prospective students and postdocs with training and interests in one or more of those areas to contact me if interested in joining the group. Please send me your CV, along with a brief summary of your training and research interests. I am a faculty member of three graduate programs (Statistics, Bioinformatics, and Genome Science & Technology), each of which has an MSc and PhD track. Note that due to a high volume of email inquiries, I am not always able to respond to every request individually.