Resources

🌎 Global Equality for PhDs in NLP / AI

One reason for success is access to information. For example, knowing what a PhD in NLP is like, what top grad schools look for when reviewing PhD applications, and how careers in NLP in both academia and industry are like. This github repository has answers on many such questions. Thanks to Zhijing Jin for putting this together.

🍩 Telegram channel for job posts

I maintain a telegram channel for phd+research job postings. The ads come from professors’ tweets. To use this job posting service, contact the recruiter with a personalized email.

🧰 Tools for CS research

If you’re an undergrad getting started with CS, you might feel stuck because you’re unfamiliar with the tools you need. Luckily, there’s an MIT course that can help. You might also find this blog post useful.

📚 Reading groups

There are communities that provide opportunities for ML research beyond university/industry research labs, e.g. reading groups. These can be helpful for learning about machine learning research and possibly a starting point for your first collaboration.

PHD ADMISSION

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

SOP + TECHNICAL WRITING

How to Write a Bad Statement for a Computer Science Ph.D. Admissions Application, Andy Pavlo html
How to write “statements of purpose” - Boaz Barak pdf
Heuristics for Scientific Writing (a Machine Learning Perspective), Zachary Lipton html
Planning paper writing, Devi Parikh html
How to ML Paper - A brief Guide doc

CS + GETTING INTO RESEARCH

Notes on machine learning, part 1 pdf
An Opinionated Guide to ML Research, John Schulman html
How I Keep My Projects Organized, Sebastian Raschka html
Research Taste Exercises, Colah html
You and Your Research, Richard Hamming pdf
An opinionated guide for CS undergrads (local universities) html

INDUSTRY + CAREER

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Dodging pitfalls in a software engineering career html
7 reasons why you should not work in a startup html
Navigate Through the Current AI Job Market: A Retrospect, Billy Ian html
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