Lengerich Lab Reading List

Pull requests for additions / fixes welcome!

Productivity

• Getting Things Done - this is the system I use

Communication

• 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

Research Wisdom

• 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

The Craft of Research

• How to do computationally reproducible research
  • Blog post on how we organize computational projects in the lab - RajLab
  • Example project organization - RajLab
• 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:
  • [RajLab] How to review a paper
  • Another perspective on how to review a paper
  • [RajLab] How to re-review a paper
  • [RajLab] Annotated example review of science paper
  • [RajLab] Annotated example review of tech paper

Career

• Letters of Recommendation
  • [RajLab] RajLab: Dear me, I am awesome. Sincerely, me… aka How to write a letter of rec for yourself
• 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:
    • Computer Science Graduate Job and Interview Guide
    • [Shomir Wilson] Example CV for PhD Students
  • 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:
    • Faculty Application : EECS Communication Lab
    • https://rosecersonsky.com/assets/slides/2022-06-14-academic-jobs.pdf
    • Virtual Workshops - YouTube
    • Tips for negotiating salary and startup for newly-hired tenure-track faculty | Dynamic Ecology
    • [RajLab] Thoughts on applying for faculty jobs

Fun

• On the Turing Completeness of PowerPoint

Classic Papers

• 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
  • Latent Dirichlet Allocation
  • On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes Andrew Y. Ng Computer Science
  • A Unifying Review of Linear Gaussian Models
  • Information Theory and Statistical Mechanics - ET Jaynes
• 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.