Research and Mentoring

Research Overview

My research lies at the intersection of statistical genetics, bioinformatics, and computational genomics. I develop novel statistical methods and computationally efficient bioinformatics tools—and leverage modern machine- and deep-learning approaches—to analyze high-dimensional next-generation sequencing and multi-omics data to identify genes and regulatory mechanisms underlying complex disease. In collaborative settings, I translate these approaches to address genetic and public-health questions across the biomedical sciences.

I plan to engage students in this work by developing new courses (e.g., Statistical Genetics), integrating dataset-driven in-class projects, and supervising independent studies, honors theses, and – where appropriate – MS/PhD dissertations. Students will gain experience in R/Python, reproducible workflows (Quarto/Git), and scalable computing, and will be encouraged to disseminate their findings through campus research symposia, undergraduate research showcases, and regional/national conferences.

Recent Projects

Submitted for publication:

  • TWAS-CTL: A robust and efficient framework in multi-tissue transcriptome-wide association studies using cross-tissue learner. Preprint available at bioRxiv.

  • GBoost-CTL: A novel method in multi-tissue transcriptome-wide association studies in cross-tissue learner incorporating GWAS information. Preprint available at bioRxiv.

Finalizing manuscript for publication:

  • Application of Deep Learning and Boosted Tree Methods in multi-tissue TWAS framework
  • Unsupervised Clustering of Pleiotropic Signals in GWAS Using Sparse Phenotype Networks

Pedagogical Scholarship Interests

My pedagogical scholarship focuses on evidence-based teaching practices that enhance student engagement, conceptual understanding, and quantitative reasoning in mathematics, statistics, and data science. I am particularly interested in implementing flipped classroom models, active learning strategies, and student-centered instructional approaches that support the development of statistical reasoning and data literacy.

Another area of interest is the design of scalable and equitable assessment frameworks, including alternative grading systems that can be implemented effectively in both small and large classrooms. I study how these approaches influence student motivation, learning outcomes, and inclusive classroom environments.

I am also interested in exploring the role of generative AI and emerging computational tools in teaching and learning. In particular, I examine how AI-assisted workflows can enhance computational literacy, support programming instruction in programming tools, and help students develop reproducible analytical practices in modern STEM curricula.

Student Mentoring

Macalester College: Capstone Project Mentor

  • Max Clifford and Ryan Mickelborough.
    Investigating Energy Grid Balancing Authorities and Renewable Energy Usage Using a Time Series Analysis Approach. Fall 25 - Spring 26

  • Noah Shannon and Austin Mills.
    What Drives Winning in College Basketball? — A Mixed Effect Model Approach. Fall 25 - Spring 26