Teaching
I teach for depth, clarity, and agency: making hard ideas understandable, asking students to reason from first principles, and giving them hands-on practice so they can build, test, and communicate like engineers.
Live problem solving, labs, and short coding reps instead of passive lectures.
Multiple explanations (intuition, math, code) paired with real instrumentation and reproducible workflows.
High expectations, transparent grading, and frequent feedback to build confidence.
- • Circuits, programming, machine learning
- • Studio-style labs + live coding
- • Emphasis on clarity and transferable skills
Courses
Selected courses I developed or teach regularly. Tight summaries for a search committee audience.
Approach to AI in the classroom
Generative AI is both a risk and a lever for deeper learning. I teach students how these models work, require first-principles competence, and set clear rules for responsible use so AI becomes a tool for harder problems—not a shortcut.
- • Oral and blue-book style assessments to verify understanding without external aids.
- • Explicit AI “code of conduct”: cite prompts, validate outputs, explain the method.
- • Live demos of attention, tokenization, and fine-tuning to demystify model behavior.
- • Emphasis on bias, privacy, and safety in biomedical contexts.
- • Use AI for feedback and acceleration, never as a substitute for reasoning.
- • Encourage reproducible workflows (notebooks, version control) even when AI assists.
For prospective students & postdocs
We welcome applicants with backgrounds in biomedical engineering, neuroscience, applied math/physics, or machine learning. Projects span closed-loop ultrasound experiments, state-space modeling of neural dynamics, and thermodynamic analyses of brain computation. We value clear writing, reproducible code, and collaborative science.