Teaching
I teach courses ranging from introductory physics for engineering students to upper-year and graduate courses in particle physics. I have a particular interest in computational physics and in integrating modern data-analysis and machine-learning methods into the physics curriculum.
PHYS 2801 — Computational Methods for Physics
This second-year course introduces computational methods used throughout modern physics. Topics include scientific computing in Python, numerical methods, data analysis and visualization, statistical methods, and an introduction to machine learning. The course emphasizes hands-on problem solving using Jupyter notebooks and applications drawn from physics.
PHYS 4409 — Thermodynamics and Statistical Physics
This fourth-year course develops an integrated understanding of thermal physics at both the macroscopic and microscopic levels. Topics include the laws of thermodynamics, entropy and free energy, followed by statistical mechanics and classical and quantum statistics.
PHYS 4807/5002 — Statistical Analysis Techniques for Physics
This upper-year and graduate course introduces statistical and computational methods used in experimental physics. Topics include probability, hypothesis testing, maximum-likelihood and least-squares estimation, confidence intervals, uncertainty quantification, Monte Carlo simulation and numerical methods, with hands-on exercises in Python or C++.
PHYS 1004 — Introductory Electromagnetism
I regularly teach PHYS 1004, the second-semester introductory physics course for engineering students. The course covers electricity and magnetism, circuits, electromagnetic induction, AC circuits, electromagnetic waves and related applications.
PHYS 6602 — Advanced Particle Physics Phenomenology
I occasionally teach this PhD-level course covering advanced topics in particle physics, with a particular focus on physics at the Large Hadron Collider (LHC).