Computational biology
Bioinformatics, computational genomics, workflow systems, version control, software environments, and reproducible analysis.
Teaching and mentoring
Make the invisible parts of research teachable.
I teach computational methods, data stewardship, and scholarly infrastructure by making systems concrete—then giving learners room to test, break, document, and improve them.
Teaching philosophy
Confidence comes from understanding the system.
Technical education works best when learners can see how the pieces connect: not only which command to run, but what the command changes, how to verify the result, and how to leave a useful record for the next person.
I design learning experiences around authentic research tasks and progressive independence. Demonstration leads to guided practice; guided practice leads to adaptation; adaptation leads to teaching someone else. Documentation, collaboration, accessibility, and responsible data practice are embedded in the work rather than added at the end.
This approach is especially important in bioinformatics and research infrastructure, where learners often inherit systems that appear more settled than they really are. I want students to become thoughtful practitioners who can evaluate tools, understand tradeoffs, and improve the environments in which research happens.
Teaching areas
Methods, infrastructure, and responsible practice.
Biocuration and data stewardship
Metadata, ontologies, persistent identifiers, data quality, provenance, repositories, preservation, and FAIR data practice.
Scholarly communication
Open access, data publishing, research integrity, licensing, reproducibility, peer review, and the changing scholarly record.
Learning design
A progression from seeing to doing to leading.
Whether the setting is a semester course, a workshop, or one-to-one mentoring, I use the same basic movement toward independence.
Observe
See the full workflow, its purpose, and the decisions hidden inside it.
Practice
Work through a realistic task with immediate feedback and a safe place to fail.
Adapt
Apply the method to new data, explain the tradeoffs, and document the changes.
Teach
Help another learner succeed and leave behind a more usable learning resource.
Selected experience
Courses, workshops, and mentoring.
-
Ongoing
Research mentoring and professional instruction
Mentoring students and professionals in research data, scholarly communication, reproducible computing, biocuration, and infrastructure development.
-
2023–2024
Carpentries-based bioinformatics instruction, USDA ARS
Delivered workshops in computational research skills and bioinformatics as a certified Carpentries instructor.
-
2020–2021
Bioinformatics Introductory Program Week, University of Notre Dame
Designed and led a four-day introductory program for incoming life-sciences graduate students, then returned as an instructor.
-
Fall 2020
BIOS 60132: Introduction to Computational Genomics
Designed and taught a graduate-level, three-credit course on foundational algorithms and practices in computational genomics.
-
Spring 2020
Data Carpentries Genomics
Taught a workshop series through Notre Dame's Center for Digital Scholarship, transitioning the course from in-person to online delivery.
Mentoring
The deliverable is a more independent researcher.
I mentor through regular conversation, explicit expectations, meaningful ownership, and gradual release of responsibility.
Students should understand how their task connects to a larger research or community need. They should also leave with artifacts that demonstrate their growth: code, documentation, data records, training materials, publications, presentations, or a system another person can use.
I am interested in mentoring undergraduate, master's, and doctoral researchers whose work intersects with research data, computational biology, scholarly communication, biocuration, or research infrastructure.
Ask about mentoring
Course development
Interested in a workshop, module, or curriculum collaboration?
I am especially interested in projects involving biocuration, reproducible workflows, research data, persistent identifiers, computational genomics, or open scholarship.
Start a conversation