Useful links

• My PhD thesis
• My publications on ADS
• Pan-survey SED forum
• UniverseTBD
• Useful places to keep track of astronomy conferences
  • CADC
  • Lorentz center
  • KITP
  • Aspen
• Starting points for literature surveys:
  • ADS - note that ADS has a bunch of features that folks don't use regularly, such as the similar(), reviews() and trending() secondary operators
  • Pathfinder - semantic search for astonomy papers
  • benty-fields - a replacement for voxcharta, vote and discuss papers during journal clubs
  • google scholar - still my go-to for papers beyond astronomy
  • astrobites - blog-style summaries of papers
  • arXivsorter , semanticscholar, connectedpapers
  • Papers With Code, Astrophysics Source Code Library, Journal of Open Source Software
• Astronomy starter pack (from .Astronomy 11 in 2019)
• Generative tools:
  • Online generative text models: Claude / Gemini / ChatGPT etc.
  • Quick RAG for working with documents: NotebookLM (though most public LLM services provide some version of this these days)
  • Text-to-image models: Stable diffusion / Flux / midjourney / imagen / dall-e
• Visualization stuff: Coolors, MaterialUI color picker, markdown , openseadragon, openspace, bokeh , streamlit
• Code stuff
  • Github tutorials here, here and here, more stuff for students
  • OpenAstronomy Python Packaging Guide
  • Packages I find useful: emcee (MCMC), dynesty (nested sampling), george, ltu-ili (SBI), python-fsps (stellar population modeling), pySR (symbolic regression)
  • easy readme and badges for github projects: readme.so
• Time management / collaboration:
  • mattermost - local alternative to slack/discord
  • read.ai - summary/meeting notes for zoom
• NSF funding opportunities
• AAS job register
• This or this or this

Less broadly useful but still rather fun:

• JWST HLSPs (high level science products) on MAST
• Requesting an allocation on NASA computational facilities
• A comparison of different samplers by mattpitkin
• the best introduction to Gaussian processes on distill.pub (apart from the standard reference textbook by Rasmussen and Williams: GPML )
• explore the relation between distributions
• amazing collection of astrophotography: https://welcome.astrobin.com/
• Labocine - movies from phd theses?
• Hearing the shape of a drum
• NYC Coronavirus wastewater surveillance
• Math videos by 3blue1brown and numberphile

Writing:

• J. F. Nash (1949) - Equilibrium points in N-person Games
• C. Shannon (1953) - Computers and Automata (p. 703 here)
• R. Feynman (1955) - The Value of Science
• M. E. Clynes & N. S. Kline (1960) - Cyborgs and Space
• R. K. Merton (1968) - The Matthew Effect in Science
• P. R. Halmos (1970) - How to write mathematics
• M. Mori (1970) - The Uncanny Valley
• R. Feynman (1974) - Cargo Cult Science - originally a caltech commencement address in 1974
• J. Cohen (1994) - The earth is round (p<0.05) - critique on significance tests to validate hypotheses instead of using them to gauge the evidence assuming the null hypothesis is true
• N. Bostrom (2005) - The Fable of the Dragon-Tyrant - i don't know if i agree, but it is an interesting argument nevertheless, and illustrates the fact that a fable tends to carry much more persuasive power when compared to an essay, critique, opinion or parable with the same message.
• T. Tao (2007) - What is good mathematics?
• M. A. Schwartz (2008) - The importance of stupidity in scientific research
• M. Stanley (2016) - Why should physicists study history? (tl;dr - it is not just a subject, but a way of thinking, quite complementary to scientific thought)
• M. Baldwin (2017) - In referees we trust? - an origin story of peer reviews
• M. Raissi et al. (2017) - Physics Informed Neural Networks
• A. Gelman & A. Vehtari (2020) - What are the most important statistical ideas of the past 50 years?
• Elimelech et al. (2023) - Algorithm-assisted discovery of an intrinsic order among mathematical constants
• Royal Society report on science in the age of AI
• NYTimes - how studying the humanities helps humanity

Blogs and books: - Greg Egan (also a big fan of his books) - Terence Tao - Saugata Basu - The proto-book on geometric deep learning: Grids, Groups, Graphs, Geodesics, and Gauges (and the arxiv link)

I have a stormy on-and-off relationship with the Collatz conjecture ever since one of my professors introduced me to the problem back in a random matrix theory class, though my workload had helped me put off working on it for a while. I recently learned that Terry Tao made some remarkable progress on this, and found both this quanta article and the original blog post quite fun to read (and sparked some ideas that unfortunately didn't go anywhere...)