[🌎 Global Equality for PhDs in NLP / AI] One non-negligible reason for success is access to information, such as (1) knowing what a PhD in NLP is like, (2) knowing what top grad schools look for when reviewing PhD applications, (3) broadening your horizon of what is good work, (4) knowing how careers in NLP in both academia and industry are like, and many others. Check this awesome github resource repository (https://github.com/zhijing-jin/nlp-phd-global-equality) with answers on many such questions. Thanks to Zhijing Jin for putting this together.

[🍩 Telegram channel for job posts] I maintain a telegram channel [link] where I post tweets made by professors on MS+PhD open positions. I usually follow ML & AI people. Often recruitment ads come from these two areas mostly. You may use this job posting with proper care for instance writing a personalized email while contacting profs.

[🧰 Tools for CS research] Undergrads who are getting started with research often get stuck because they are unfamiliar with various tools. Here is a pointer to a very useful MIT course: https://missing.csail.mit.edu. This blog post might also be relevant.

[📚 Reading groups] I should mention communities that provide opportunities for ML research beyond university/industry research labs. You can join reading groups to follow discussion on state-of-the-art AI research and get feedback on your research. Reading groups could possibly be a starting point for your first collaboration. MLC, MILA, Stanford NLP Seminar are a few of them. Such a list can be found here.

Phd & Research Guidance

  1. Syllabus for Eric’s PhD students, Eric Gilbert [doc]
  2. An Opinionated Guide to ML Research, John Schulman [html]
  3. Ph.D. Applications: FAQ, Noah Smith [doc]
  4. A Survival Guide to a PhD, Andrej Karpathy [html]
  5. Machine Learning PhD Applications — Everything You Need to Know, Tim Dettmers [html]
  6. A Guide to Cold Emailing, Eugene Vinitsky [html]
  7. How I Keep My Projects Organized, Sebastian Raschka [html]
  8. Research Taste Exercises, Colah [html]
  9. Heuristics for Scientific Writing (a Machine Learning Perspective), Zachary Lipton [html]
  10. The Definitive ‘what do I ask/look for’ in a PhD Advisor Guide [pdf]
  11. Questions to Ask a Prospective Ph.D. Advisor on Visit Day, With Thorough and Forthright Explanations, CMU [html]
  12. PhD Syllabus, Mor Naaman [html]

Career

  1. āĻĢā§āĻ°ā§‡āĻļāĻžāĻ° āĻšāĻŋāĻļā§‡āĻŦā§‡ āĻ¸āĻĢāĻŸāĻ“ā§Ÿā§āĻ¯āĻžāĻ° āĻ‡āĻžā§āĻœāĻŋāĻ¨āĻŋā§ŸāĻžāĻ°āĻŋāĻ‚ āĻšāĻžāĻ•āĻ°āĻŋ āĻ–ā§‹āĻāĻœāĻžāĻ–ā§āĻāĻœāĻŋāĻ° āĻ…āĻ­āĻŋāĻœā§āĻžāĻ¤āĻž, āĻ•ā§āĻ¯āĻžāĻŽā§āĻĒāĻžāĻ¸ āĻœā§āĻ¨āĻŋā§ŸāĻ°āĻĻā§‡āĻ° āĻœāĻ¨ā§āĻ¯ āĻŦā§ŸāĻžāĻ¨ [html]
  2. āĻ¸āĻŋāĻ¨āĻŸā§āĻ¯āĻžāĻ•ā§āĻ¸ āĻŸā§ āĻ•āĻŽā§āĻĒāĻŋāĻŸāĻŋāĻŸāĻŋāĻ­ āĻĒā§āĻ°ā§‹āĻ—ā§āĻ°āĻžāĻŽāĻŋāĻ‚ āĻœāĻžāĻ°ā§āĻ¨āĻŋ [html]
  3. Dodging pitfalls when transitioning from academia to industry, Archy [html]
  4. 7 reasons not to join a startup and 1 reason to, Chip Huyen [html]

Machine Learning + Undergrad Research

  1. Reproducing SOTA works as a pathway for research and prep for a bachelor thesis [html]
  2. Notes on machine learning, part 1 [pdf]

Research Reading Groups

  1. NLP Reading Fall 21 @Dhaka
  2. Deep Learning: Classics and Trends
  3. Stanford NLP Seminar
  4. Mila Tea Talks

Blogs to Follow

  1. Lil’Log
  2. Sebastian Ruder
  3. Jay Alammar