[🌎 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 repository with answers on many such questions. Thanks to Zhijing Jin for putting this together.

[🍩 Telegram channel for job posts] I maintain a telegram channel where I occasionally attatch tweets made by professors on MS+PhD open positions. I mostly follow ML & DL researchers and recruitment ads often come from these two areas. You may use this job posting with proper care i.e. 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: This blog post might also be relevant.

[📚 Reading groups] There are communities that provide opportunities for ML research beyond university/industry research labs. Those reading groups can be a helpful for learning about machine learning research, additionaly they could possibly be a starting point for your first collaboration. I have a listed few groups below.

Phd Admission

  1. Syllabus for Eric’s PhD students, Eric Gilbert [doc]
  2. Ph.D. Applications: FAQ, Noah Smith [doc]
  3. A Survival Guide to a PhD, Andrej Karpathy [html]
  4. Machine Learning PhD Applications — Everything You Need to Know, Tim Dettmers [html]
  5. A Guide to Cold Emailing, Eugene Vinitsky [html]
  6. The Definitive ‘what do I ask/look for’ in a PhD Advisor Guide [pdf]
  7. Questions to Ask a Prospective Ph.D. Advisor on Visit Day, With Thorough and Forthright Explanations, CMU [html]
  8. PhD Syllabus, Mor Naaman [html]
  9. EVERY PHD IS DIFFERENT, Maxwell Forbes [html]
  10. Advice for prospective PhD students on deciding which program to join [html]

SOP & Technical Writing

  1. How to Write a Bad Statement for a Computer Science Ph.D. Admissions Application, Andy Pavlo [html]
  2. How to write “statements of purpose” - Boaz Barak [pdf]
  3. Heuristics for Scientific Writing (a Machine Learning Perspective), Zachary Lipton [html]
  4. Planning paper writing, Devi Parikh [html]

Getting into Research + Philosophy

  1. Reproducing SOTA works as a pathway for research and prep for a bachelor thesis [html]
  2. Notes on machine learning, part 1 [pdf]
  3. An Opinionated Guide to ML Research, John Schulman [html]
  4. How I Keep My Projects Organized, Sebastian Raschka [html]
  5. Research Taste Exercises, Colah [html]
  6. You and Your Research, Richard Hamming [[pdf]][]


  1. ফ্রেশার হিশেবে সফটওয়্যার ইঞ্জিনিয়ারিং চাকরি খোঁজাখুঁজির অভিজ্ঞতা, ক্যাম্পাস জুনিয়রদের জন্য বয়ান [html]
  2. সিনট্যাক্স টু কম্পিটিটিভ প্রোগ্রামিং জার্নি [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]
  5. Navigate Through the Current AI Job Market: A Retrospect, Billy Ian [html]
  6. AI research: the unreasonably narrow path and how not to be miserable, Rosanne Liu [video]

ML Reading Groups

  • NLP Reading Fall 21 @Dhaka: I am running this group since summer 2020. We typically prepare a course for each season on a list of paper. Out invited speakers present them every week.
  • Deep Learning: Classics and Trends: Amazing Rosanne Liu is running this cool group. It is the best place on internet for for developing research taste! They host weekly reading, sub reading groups, research jamming and fun socials!
  • Stanford NLP Seminar: Weekly reading Stanford University. You can follow cutting edge research here.
  • Mila Tea Talks: MILA host weekly tea talks, diverse topics on deep learning.

Blogs to Follow

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