Teaching
UC Berkeley CHEM 274B teaching, course materials, and molecular machine learning topics.
UC Berkeley CHEM 274B: Molecular Science & Software Engineering
Adjunct Faculty — graduate-level course on software engineering, machine learning, and cheminformatics for molecular science.
The course sits at the intersection of two gaps: molecular scientists who need to write better software, and ML students who need to understand the chemistry behind their data. We work in RDKit, PyTorch, and scientific Python, with case studies drawn from drug discovery and materials science.
- Course repository: github.com/nrflynn2/swe-molecular-sciences (selected public materials, not the full course)
- Topics: Python for scientists, RDKit and cheminformatics, molecular ML, graph neural networks, generative models for drug design
| Term | Role | Focus |
|---|---|---|
| Spring 2026 | MSSE Capstone Mentor | Two project teams building applied molecular ML and scientific software systems |
| Fall 2025 | Instructor | CHEM 274B course delivery, labs, projects, and final presentations |
| Spring 2025 | MSSE Capstone Mentor | One project team building an applied molecular science software project |
| Fall 2024 | Instructor | CHEM 274B course launch and graduate project studio |
Earlier Teaching Experience
I started as a TA and course developer in undergrad, and that’s where most of my instincts about teaching come from — give people working code, real data, and a question worth answering.
| Year | Role | Course or Program | Institution |
|---|---|---|---|
| 2018 | Teaching Assistant | Algorithms for Computational Biology | WUSTL |
| 2016 | Teaching Assistant | Bioinstrumentation Lab | UIUC |
| 2015 | Resident Project Advisor (RA) | Illinois Engineering First-Year Experience, Summer Scholars Program | UIUC |
| 2014, 2015 | Course Developer | Introduction to Computer Science, Honors Section | UIUC |
Course Development
I’ve contributed to coursework in computational biology, bioinstrumentation, introductory CS, and software engineering. I tend to build assignments around concrete scientific questions — the kind where students walk away with code they’ll actually use again.
For inquiries about course materials, capstone mentoring, or teaching collaborations, reach out via email.