Pull requests for additions / fixes welcome!
- Getting Things Done - this is the system I use
- How to email busy people: Be kind and direct. Bold your requested action items.
- Grice's Maxims
- Guide for Interacting With Faculty - Shomir Wilson
- Guide to Professorspeak - Shomir Wilson
- Taste comes before skill
- Ego and Math
- Heckerthoughts
- How to do research - Bill Freeman
- Principles of Effective Research - Michael A. Nielsen
- Hanging on to the Edges: Staying in the Game - Daniel Nettle
- You and Your Research - Richard Hamming
- The Structure of Scientific Revolutions - Thomas Kuhn
- What are Worthwhile Problems? - Richard Feynman
- A Mathematician's Apology - G.H. Hardy
- The importance of stupidity in scientific research - Martin A Schwartz
- How to do computationally reproducible research
- Reading:
- Staying on top of literature - RajLab
- Writing:
- Writing for Computer Science
- Writing a paper
- Guide for Scholarly Writing - Shomir Wilson
- Guide for Citations and References - Shomir Wilson
- Guide for Research Conferences - Shomir Wilson
- Introductions, annotated example 1, example 2, example 3 - RajLab
- Words to avoid when writing - RajLab
- Writing checklist (incomplete) - RajLab
- Paper submission checklist - RajLab
- Writing a response to reviewers for revising a paper
- General how to - RajLab
- Annotated example - RajLab
- Another example - RajLab
- Quick guidelines
- Figure making
- Illustrator guide - RajLab
- Blog post about figure making - RajLab
- Labeling small multiples - RajLab
- Incomplete blog post about figure design principles - RajLab
- How to write a figure legend
- Presenting:
- [Kellis Lab] How to present - Writing, Figures, Talks (MIT Deep Learning Genomics Lecture 22)
- [RajLab] RajLab: “Refusing the call” and presenting a scientific story
- [RajLab] RajLab: Some thoughts on how to structure a talk
- How To Give a Talk
- [RajLab] RajLab: Simple tips to improve your presentations
- Designing Effective Scientific Presentations • iBiology
- Reviewing:
- Letters of Recommendation
- PhD
- Choosing your dissertation committee
- PhD: An uncommon guide to research, writing & PhD life
- Phobidden FooD
- [RajLab] How to assemble and use a thesis committee
- Documenting your PhD — Keeping Track of Meetings, Experiments and Decisions • David Stutz
- Fellowships
- [RajLab] So you want to apply for a PhD fellowship?
- [RajLab] Example F30 specific aims
- Jobs
- Broad CS:
- CS Academia:
- Thoughts from my faculty application experience
- Academic Job Search
- Interview Questions for Computer Science Faculty Jobs
- https://sites.google.com/view/elizabethbondi/blog?pli=1
- Faculty job talks: tips from the faculty – MIT EECS
- Guide for the Tenure-Track Job Market in Computer/Information Sciences
- Academia General:
- Statistics
- Lasso - Tibshirani, Regression shrinkage and selection via the lasso
- Group Lasso - Yuan & Lin, Model selection and estimation in models with grouped variables
- Covariance Regularization - Witten & Tibshirani, Covariance regularized regression and classification for high-dimensional problems
- L1/L2 Regularization - Obozinski et al., High-dimensional support union recovery in multivariate regression
- Machine Learning
- Computer Science
- Best Paper Awards in Computer Science
- Structure and Interpretation of Computer Program (video lectures)
- Hints for Computer System Design - Butler Lampson
- End-to-End Arguments in Systems Design - J.H. Saltzer, D.P. Reed, D.D. Clark
- Deep Learning Basics
- Ilya Sutskever's "90% of Deep Learning in 30 papers"
- Deep Learning book chapter on convolutional nets
- Generalization and Network Design Strategies - LeCun
- ImageNet Classification with Deep Convolutional Neural Networks - Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton, NIPS 2012.
- On Random Weights and Unsupervised Feature Learning
- Quoc Le's lectures on Deep Learning
- Introduction to NN architectures
- Computational Biology
- GWAS / eQTL mapping
- Graph-guided fused Lasso - Kim & Xing
- Tree-guided group Lasso - Kim & Xing
- Joint eQTL Mapping & Network Inference - Sohn & Kim
- Gaussian Graphical Models / Gene Networks
- Graphical Lasso -- Friedman et al.
- Neighborhood Selection -- Meinshausen & Buhlmann
- KELLER: estimating time-varying interactions between genes -- Song et al.
- TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages -- Parikh et al.
- Structured learning of gaussian graphical models -- Mohan et al.,
- Bayesian inference of multiple gaussian graphical models -- Peterson et al.,
- Learning graphical models with hubs -- Tan et al.,
- An Introduction to Graphical Models (Mike Jordan, brief course notes)
- A View of the EM Algorithm that Justifies Incremental, Sparse and Other Variants - Neal & Hinton,
- A Unifying Review of Linear Gaussian Models - Roweis & Ghahramani
- An Introduction to Variational Methods for Graphical Models - Jordan et al.
- GWAS / eQTL mapping