Teaching

For the last few years, my teaching has mainly focused on AI-related courses and introduction to astronomy courses.

AI Series

PHYS 301 — Intro Scientific Computation

Foundations of scientific computing with Python: NumPy/Scipy/Matplotlib, numerical methods, DFT, SVM, PCA, Monte Carlo, data handling, visualization, cross-validation, and an intro to ML workflows.

• Support Vector Machine

PHYS 302 — Sci. Comp. & Machine Learning

Scientific Computation and ML: neural network, CNN, differential equations, symbolic computation, and model optimization.

• Lightening Fast Pandas
• Neural Network Basics

PHYS 303 — Bayesian/Deep Learning in Sci.

Bayesian inference (priors, posteriors, MCMC/HMC), uncertainty quantification, reinforcement learning, the Transformer architecture.

• Bayes Theorem
• Attention in Transformers
• Build Your Own GPT

Astronomy

Cosmology (PHYS 120) — Astronomy: Earth/Cosmos

A broad introduction from the Earth to the most distant galaxies and cosmology, including stars, galaxies, supernovae, black holes, gravitational lensing, dark matter and dark energy.

• Hubble Ultradeep Field
• The Crab Nebula and Supernova 1054
• The Known Universe
• Detection of Gravitatoinal Waves
• Understanding the Image of a Black Hole

Planetary Science (PHYS 121) — Planetary Astronomy

Solar System and exoplanet systems: formation, dynamics, atmospheres, and current discoveries. Below are some of the resources I use in the class.

• Saturn's Rings
• The Formation of the Moon
• Exoplanets Discoveredy the Kepler Satellite

In earlier years, I also taught the following courses

PHYS 371 — Math Methods for Sci and Eng

Mathematical Physics at the level of Boas.

PHYS 343 — Astropyhysics

Under this course number/title, I taught an introduction to cosmology course.