Teaching & Mentoring
From equations to evidence
Across mechanics, laboratory physics, molecular biophysics, and cancer biology, students learn to move between physical models, computation, data, and scientific argument.
Python, simulation, machine learning, and generative AI enter the classroom where they sharpen a question or expose an assumption. The standard remains the same: students must understand the physics, test the output, and communicate what the evidence supports.
- 01Model
Translate a physical or biological question into assumptions, variables, and equations.
- 02Compute
Use Python, numerical methods, simulation, or AI to explore what the model predicts.
- 03Test
Compare results with data, limiting cases, uncertainty, and established scientific knowledge.
- 04Communicate
Explain the method, evidence, limitations, and next question clearly.
Current and regularly taught courses
The courses below reflect active, upcoming, and regularly taught work. Assignments change by term; the undergraduate and graduate Physics bulletins remain the authoritative sources for course descriptions.
AI and the Physics of Life: AlphaFold and Why Molecular Shape Matters
Students use protein structure prediction as a route into the relationship between molecular shape and biological function. AlphaFold becomes both a scientific tool and a case study in what an AI prediction can—and cannot—explain.
- AlphaFold
- molecular structure
- AI evaluation
Mechanics
Students turn equations of motion into computational models, use Python to study systems that resist simple solutions, and visualize how assumptions shape predictions. A generative-AI exercise asks students to reproduce a difficult modeling task, diagnose the output, and explain the physics needed to judge it.
- Python
- numerical modeling
- visualization
- AI critique
Intermediate Laboratory II
The course connects experiments in mechanics with computer simulation. Its computational work emphasizes Python, data analysis, and machine learning, asking students to move carefully between measured data, models, uncertainty, and scientific presentation.
- experiments
- Python
- data analysis
- machine learning
Generative AI in Biophysics
Students read, present, and discuss current work in AI, deep learning, molecular biophysics, and drug discovery. Source-grounded research tools support literature exploration, while presentations and reflection keep scientific judgment and responsible research practice at the center.
- primary literature
- deep learning
- drug discovery
- research reflection
Interdisciplinary and special-topic teaching
Cell Biology of Cancer
Connects molecular mechanisms with the evidence used to reason about cancer biology, including structural and computational perspectives.
Research Topics in Structural & Computational Biophysics
Builds interdisciplinary discussion around current research in molecular structure, simulation, experiment, and quantitative interpretation.
Topics in Cancer Biology
Uses student presentations, discussion, and review writing to connect cancer mechanisms with emerging methods, including generative AI in drug discovery.
Mentoring from coursework into research
Increasing independence
Students begin by learning a physical system and a computational workflow, then take ownership of the question, validation, interpretation, and communication.
Research-ready habits
Reproducible analysis, careful figures, critical reading, and constructive discussion are treated as scientific practices—not finishing touches.
AI as a tool—and a subject of study
Generative AI is useful when it supports learning rather than bypassing it. Students compare generated answers with equations, data, and primary sources; identify failure modes; and remain responsible for the scientific claims they make.
Read the Wake Forest Center for the Advancement of Teaching profile, “Fred Salsbury: AI as a Tool, Not a Shortcut”.
Current and prospective students
Looking for the right next step?
Use the advising guide for official program links, planning questions, and ways to prepare for a conversation.