Personal reflections, practical guides for aspiring researchers, and explainer posts. Below, you will also find some of my study notes covering the mathematical and physical foundations of these fields.
[Most posts are placeholders for now. This page will be updated soon with all materials.]
On the questions that first pulled me toward studying the universe, and why I keep returning to them. A note for anyone wondering whether to chase the big questions.
What worked, what completely failed, and the things I wish someone had told me before I sent my first email to a professor. Plenty of honest mistakes.
Most days in research don't feel like a discovery. A short essay on staying motivated when results take months — or years — to appear.
How I think about chasing the topics that genuinely fascinate me versus the very real pressure to publish, ship code, and graduate.
A timeline, reading list, and breakdown of what each part of a PhD application is actually testing — SOP, recommendation letters, GRE/PGRE, and fit.
Where to look for undergraduate research opportunities (REUs, DAAD-RISE, IUSSTF, summer schools), how to time applications, and what makes a strong one.
How to write the first email to a researcher whose work excites you — what to say, what to never say, and how to follow up without being a nuisance.
On finding co-authors, navigating long-distance collaborations, joining open-source astronomy projects, and being someone people want to work with again.
How to turn a tangle of code, plots, and half-formed ideas into a paper your collaborators will sign off on. Tools, structure, and submission tips.
Heuristics for spotting topics that are tractable, interesting, and aligned with where you want to be in five years — without getting stuck in someone else's agenda.
A learning path through the Python ecosystem every working astronomer relies on: numpy, scipy, astropy, healpy, scikit-learn, and JAX. With small projects to practice each.
How to set up LaTeX painlessly, write equations that read well, manage references with BibTeX, and submit your first preprint to arXiv without panic.
A three-pass method for cutting through dense astrophysics papers, taking notes that stick, and knowing when to read deeply versus skim and move on.
How to set up a personal site (yes, like this one), structure GitHub repos so people actually want to read them, and prepare clean talks that get remembered.
The bare minimum ML you need to do credible work at the intersection of astrophysics and machine learning: linear algebra, optimization, neural nets, and best practices.
A curated tour of the software stack working astronomers use day to day: TOPCAT, ds9, CASA, JupyterLab, Snakemake, and how to learn each one fast.