A short reading guide compiled from my email responses to undergrads and incoming grad students about what my advisor works on and how to get started in the field:

• Gnedin & Madau (2022):
  A short review that surveys the key physics and modeling approaches used to study cosmic reionization.
• Saas-Fee Notes (Gnedin 2014):
  Lecture notes for anyone interested in starting research in reionization. A guided tour through reionization, galaxy formation, and the physics of cosmic gas - from the IGM to star-forming regions.
• Computational Hydrodynamics for Astrophysics:
  A hands-on introduction to hydrodynamics solvers by Michael Zingale.

For my research, I primarily code in Python and C++. I’ve also found Mathematica useful in several projects involving more analytical calculations. Additionally, I think a solid grasp of statistics is the baseline for producing rigorous research. I took undergraduate major requirements in Computer Science and Statistics, and personally found the core theoretical courses more interesting than most applied ones.

Things I learned before grad school and still reference often in my research:

• Understanding Machine Learning: From Theory to Algorithms
• Daniel Baumann’s Lecture Notes on Cosmology

Absolutely tangential, but fun stuff I was interested in that helped shape how I think:

• Competitive Programmer’s Handbook
• The Art of Insight in Science and Engineering: Mastering Complexity