Irene Y. Chen
Lab Group
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Bio
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Resources
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Reading List
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Blog
Resources
Advice for aspiring and current ML researchers
Enormous list of resources for all things research-related
– by Shaily Bhatt (
@shaily99
)
Applications for computer science PhD
– by Jean Yang (
@jeanqasaur
)
Applying to Ph.D. Programs in Computer Science
– by Mor Harchol-Balter
Emailing professors
– by Dan Roy (
@roydanroy
)
Interviewing for PhD programs
– by Nils Gehlenborg (
@ngehlenborg
)
What should grad students be learning?
– by Michael Mitzenmacher
Starting out in AI research
– by Tom Silver (
@tomssilver
)
Expectations for advisors and students
– by John Regehr (
@johnregehr
), Suresh Venkatasubramanian (
@geomblog
), and Matt Might (
@mattmight
)
PhD Syllabus
– by Mor Naaman (
@informor
)
Handling math bullies
– by Fan Chung Graham
Combatting Anti-Blackness in the AI Community
– by Devin Guillory (
@databoydg
)
Paper writing tips
– by Jacob Steinhardt
Shortening papers
– by Devi Parikh (
@deviparikh
)
Responding to peer feedback
– by Matt Might (
@mattmight
)
Writing conference rebuttals
– by Devi Parikh (
@deviparikh
), Dhruv Batra (
@DhruvBatraDB
), Stefan Lee (
@stefmlee
)
Tweeting about papers
– by Lisa Nivison-Smith (
@LNivisonSmith
)
Reviewing conference papers
– by Colin Raffel (
@colinraffel
)
How to write a good conference review
– CVPR 2020 Tutorial
Academic job search in 10 questions
– by Elissa Redmiles (
@eredmil1
) and Nicolas Papernot (
@NicolasPapernot
)
Another academic job search guide
– by Westley Weimer
How to reject a candidate –
by Sara Davis (
@PsySciSar
)
Where to present research on machine learning, healthcare, and/or fairness
Neural Information Processing Systems (NeurIPS)
International Conference for Machine Learning (ICML)
ACM Conference on Health, Inference, and Learning (CHIL)
Fair ML for Health Workshop at NeurIPS
Machine Learning for Health (ML4H) Workshop at NeurIPS
Representative Machine Learning
Women in Machine Learning
Black in AI
Queer in AI
LatinX in AI
(Dis)Ability in AI
Muslims in ML
Introductory guides
Causal Inference textbook
– by Miguel Hernan and Jamie Robins
History of Fairness in ML
– by Ben Hutchinson and Margaret Mitchell
ML for Healthcare class at MIT
– by course staff including myself