Teaching & Mentorship¶
I enjoy an active faculty role in the Department of Biology at Texas A&M University, where I teach across molecular and computational biology and mentor scientists in translating broad biological questions into testable, reproducible work.
Faculty mentor · Graduate and undergraduate education
Research mentorship¶
My courses and laboratory mentoring share the same intellectual standard: computational fluency must be paired with biological reasoning, explicit assumptions, clear documentation, and honest interpretation of limitations.
My students often say that I ask “simple questions that are impossible to answer.” Those deceptively simple questions expose hidden assumptions and require students to connect mechanism, evidence, and analytical choices before an answer becomes scientifically defensible.
The laboratory's people-and-projects page presents current graduate research and former undergraduate contributions in their scientific context.
Meet our researchers Public lab outputs
Fall 2026 teaching¶
Fall 2026 · BIOL 647 · 4-credit graduate course · Online
Digital Biology: reproducible computation by doing
Developed and taught since 2012, Digital Biology is a hands-on, command-line-driven course that teaches students to turn biological questions into organized computational analyses. Students work with Unix/Linux, Bash, Git and GitHub, text-processing tools, genomic data formats, NGS, sequence alignment, read mapping, transcriptome assembly and quantification, R/RStudio, and execution across local, high-performance-computing, and cloud resources.
Repository-based assessment makes reproducibility a daily practice: students document methods, scripts, and analysis decisions with version control before completing project work and a final report. AI-assisted coding, workflow development, and data exploration are paired with required verification, disclosure, biological judgment, and attention to reproducibility, bias, and methodological rigor.
Fall 2026 · BIOL 213 · 3-credit undergraduate course · In person
Molecular Cell Biology: mechanisms and evidence
This course examines how cells are built, powered, and regulated, including membranes and transport, cellular energy, genome organization, gene expression, intracellular trafficking, cytoskeletal systems, signaling, cell division, and cancer.
Students connect molecular mechanisms with quantitative reasoning, core experimental methods, and the data and evidence underlying current biological understanding. AI may support learning, but not replace independent reasoning or mastery of biological mechanisms.
Selected curriculum developed¶
Computational Genomics and Genomics¶
These undergraduate and graduate courses prepared students to acquire, organize, analyze, and interpret genome-scale data using Galaxy, CyVerse, national cyberinfrastructure, and Texas A&M computing resources while examining how experimental design and genome annotation affect computational results.
Archived Computational Genomics materials (2023). This is an earlier course archive, not the Fall 2026 syllabus.
Advanced Eukaryotic Genetics and Epigenetics¶
This graduate course examined genetic and epigenetic mechanisms across eukaryotic model systems, with particular attention to RNA silencing, non-Mendelian inheritance, non-coding RNA, and the experimental logic used to study gene regulation.
Information in Biology¶
Information in Biology explored how biological systems encode, transmit, compare, and act on information—a question that continues to connect my experimental genetics, computational genomics, and current work with large protein models.