Teaching

Philosophy

I enjoy teaching courses in both astronomy and physics.  In my experience, the best way to learn something is to actually do it.  Thus, I am a very strong proponent of active learning at all levels, including in-class activities, discussions, and highly interactive lectures.  Furthermore, I employ state-of-the-art pedagogical technology in my courses, including virtual demos and online tools for in-class activities. 

So far, I have taught three courses at Iowa State as described below.

 Stars, Galaxies, and Cosmology (Astronomy 1500)

Course Homepage Image -- Image Credits: NASA, STScI, JWST, EHT

I teach the intro to non-Solar System astronomy for non-majors.  I always absolutely love teaching this course and interacting with the enthusiastic students who take it.  Below is a more detailed description and the learning objectives. 

Course Description

For the nonscientist. A survey of astronomy with a focus on the universe beyond our solar system. Basic observational astronomy and the history of astronomy. Stellar astronomy: motions, distances, sizes, spectra; types of stars; variability; binary systems. Stellar evolution: the birth, life, and death of stars, including supernovae, neutron stars, and black holes. The structure and evolution of the Milky Way Galaxy. Other galaxies, clusters of galaxies, quasars. Theories of the origin of the universe.

Learning Objectives


Introduction to Solar System Astronomy (Astronomy 3420)

Course Homepage Image -- Image Credits: Credit: NASA, SwRI, JHU APL, Alex Parker,  ESA, A Simon (Goddard Space Flight Center) and MH Wong (University of California, Berkeley), ALMA Partnership (2015)

Very recently, I started teaching the Introduction to Solar System Astronomy course at the 3000 level (usually, physics majors with an astro minor and aerospace engineers take this course).

Course Description

An introduction to the physics of the Solar System and the planetary systems discovered around other stars. General characteristics of planetary systems: dynamics, thermodynamics, internal and surface structure of planets and minor bodies, physics of their atmosphere. Discovery techniques and characterization of extrasolar planets, and planetary systems formation models. 'Grand tour' of the Solar System, using data and imagery from probes and telescopes that have visited these worlds. The origin and evolution of life on Earth, and the ongoing search for life in the Solar System and elsewhere in the universe.

Learning Objectives

In this course students learn the basics of planetary science, including solving quantitative problems to calculate the parameters of planets and their atmospheres from actual observational and experimental data. They learn how the composition of the atmosphere and the physics of planetary environment affects the climate of planets, including Earth. They apply these concepts to a month-long project where they calculate the structure and evolution of an extrasolar planetary system of their choice. In doing the various tasks as outlined above, particularly the month-long project, my goal is for the students to get a sense of what scientific research is like in a practical sense; i.e., moving beyond problem sets and doing research!

Computational Physics (Physics 5510)

Course Homepage Image -- Left: the Rayleigh-Taylor Instability;   Right:  IBM Blue Gene/P supercomputer at the Argonne Leadership Computing Facility.

Occasionally, I teach a graduate-level course in computational physics.  I really enjoy teaching this course as it really gets into the details of an area I love. I have also structured this course around an active learning approach, with particular focus on "computational lab exercise" and a research-focused term project.  Below is the syllabus description of the course and the learning objectives. 

Course Description

The primary goal of this course is to train students in using computational methods to solve problems as an alternative and/or complementary approach to the analytical techniques learned in other classes. Students will be trained in a level of practicality that can be applied to their own research. Students will also gain an understanding of the care that is required in employing numerical techniques and will learn a number of applications, including integration schemes for differential equations, Monte Carlo approaches, data fitting, and basic multi-processor and supercomputing applications. The course will be primarily taught using Python 3, but we may briefly explore other languages.

Learning Objectives