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README 

          
# Lengerich Lab Reading List

Pull requests for additions / fixes welcome!



## Productivity



* [Getting Things Done](https://amzn.to/3UohLug) - this is the system I use



## Communication



* [How to email busy people](https://simplystatistics.org/2011/09/23/getting-email-responses-from-busy-people/): Be kind and direct. **Bold your requested action items**.

* [Grice's Maxims](https://www.sas.upenn.edu/~haroldfs/dravling/grice.html)

* [Guide for Interacting With Faculty](https://shomir.net/teaching_faq.html) - Shomir Wilson

* [Guide to Professorspeak](https://shomir.net/professorspeak.html) - Shomir Wilson



## Research Wisdom



* [Taste comes before skill](https://jamesclear.com/ira-glass-failure)

* [Ego and Math](https://www.youtube.com/watch?v=z7GVHB2wiyg)

* [Heckerthoughts](https://arxiv.org/pdf/2302.05449)

* [How to do research](https://pengsida.net/files/Bill_Freeman_How_to_do_research.pdf) - Bill Freeman

* [Principles of Effective Research](https://pengsida.net/files/Michael_Nielsen_How_to_do_research.pdf) - Michael A. Nielsen

* [Hanging on to the Edges: Staying in the Game](http://www.danielnettle.org.uk/wp-content/uploads/2017/09/Staying-in-the-game.pdf) - Daniel Nettle

* [You and Your Research](http://www.cs.cmu.edu/~15712/papers//hamming86.pdf) - Richard Hamming

* [The Structure of Scientific Revolutions](http://projektintegracija.pravo.hr/_download/repository/Kuhn_Structure_of_Scientific_Revolutions.pdf) - Thomas Kuhn

* [What are Worthwhile Problems?](http://scienceblogs.com/thescian/2008/03/11/what-are-worthwhile-problems-f/) - Richard Feynman

* [A Mathematician's Apology](https://www.math.ualberta.ca/mss/misc/A%20Mathematician%27s%20Apology.pdf) - G.H. Hardy

* [The importance of stupidity in scientific research](https://journals.biologists.com/jcs/article/121/11/1771/30038/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](http://rajlaboratory.blogspot.com/2017/08/figure-scripting-and-how-we-organize.html) - RajLab

    * [Example project organization](https://github.com/arjunrajlaboratory/example_project) - RajLab

* Reading:

    * [Staying on top of literature](https://docs.google.com/document/d/1QrDpIoOlQaUVjDnRCYqi_2IYYeWWz6OxC1w4pY5sIQs/edit) - RajLab

* Writing:

    * [Writing for Computer Science](https://amzn.to/44HN73P)

    * Writing a paper

        * [Guide for Scholarly Writing](https://shomir.net/scholarly_writing.html) - Shomir Wilson

        * [Guide for Citations and References](https://shomir.net/citations_and_references.html) - Shomir Wilson

        * [Guide for Research Conferences](https://shomir.net/scholarly_publishing.html) - Shomir Wilson

        * Introductions, annotated [example 1](https://docs.google.com/document/d/1nqVYRTql1sgUNxT9RfE0SX0RiyH3QlgUZgX_Jg6fsos/edit?usp=sharing), [example 2](https://docs.google.com/document/d/1lGpMFtW4x4GOx-TUmVD_iguM-vh3nvdjNhN-R2PyXt8/edit?usp=sharing), [example 3](https://docs.google.com/document/d/1TEFLJpjwRiSM2E0gmiUg98sZFEigg_8Pj4vblEh39xI/edit?usp=sharing) - RajLab

        * [Words to avoid when writing](https://docs.google.com/document/d/1r6nDcF43esu3xBjmk3ERAmaEHKEB75_HflSkk3zZhBk/edit) - RajLab

        * [Writing checklist (incomplete)](https://docs.google.com/document/d/1DmoBuFUK6bJG9C5AM5B7i12GI2ew8egg2b-50DdgRFI/edit) - RajLab

        * [Paper submission checklist](https://docs.google.com/document/d/1_5R2c6WVjV5qi5profAlMHQd2LHhhXgzmYn_htsJl4Q/edit?usp=sharing) - RajLab

    * Writing a response to reviewers for revising a paper

        * [General how to](https://docs.google.com/document/d/1fIpY8d90g0BrTCc0AAxr3PCZHhdVZUYPDxiPqSVkKOk/edit) - RajLab

        * [Annotated example](https://docs.google.com/document/d/17f4pyQ1kowgTOIM7mazbXV8uzp2Ev8jaDOYp7MPnYF8/edit)  - RajLab

        * [Another example](https://drive.google.com/file/d/13d9x4V_RowStgSjDMm8MpiKx_xtlj5pV/view?usp=sharing)  - RajLab

        * [Quick guidelines](https://jef.works/blog/2020/06/17/responding-to-scientific-peer-review/)

    * Figure making

        * [Illustrator guide](https://docs.google.com/document/d/1psC5olObkGHDfw3c7am9jpD2OdCN4lnCU_QF26MAQmQ/edit#heading=h.or1to9c1y8il)  - RajLab

        * [Blog post about figure making](http://rajlaboratory.blogspot.com/2019/08/i-adobe-illustrator-for-scientific.html)  - RajLab

        * [Labeling small multiples](http://rajlaboratory.blogspot.com/2016/01/a-proposal-for-how-to-label-small.html)  - RajLab

        * [Incomplete blog post about figure design principles](https://docs.google.com/document/d/1RozjPwJO57FndomEKUEkG9XwDNeXWj1X24TKq5CMNa0/edit) - RajLab

        * [How to write a figure legend](https://blog.bioturing.com/2018/05/10/how-to-craft-a-figure-legend-for-scientific-papers/)

* Presenting:

    * [Kellis Lab] [How to present - Writing, Figures, Talks (MIT Deep Learning Genomics Lecture 22)](https://www.youtube.com/watch?v=KzyvIBjBkuc)

    * [RajLab] [RajLab: “Refusing the call” and presenting a scientific story](https://rajlaboratory.blogspot.com/2023/09/refusing-call-and-presenting-scientific.html)

    * [RajLab] [RajLab: Some thoughts on how to structure a talk](https://rajlaboratory.blogspot.com/2016/09/some-thoughts-on-how-to-structure-talk.html)

    * [How To Give a Talk](https://www.howtogiveatalk.com/)

    * [RajLab] [RajLab: Simple tips to improve your presentations](https://rajlaboratory.blogspot.com/2014/01/simple-tips-to-improve-your.html)

    * [Designing Effective Scientific Presentations • iBiology](https://www.ibiology.org/professional-development/scientific-presentations/)

* Reviewing:

    * [RajLab] [How to review a paper](http://rajlaboratory.blogspot.com/2014/04/how-to-review-paper.html)

    * [Another perspective on how to review a paper](https://github.com/jtleek/reviews)

    * [RajLab] [How to re-review a paper](http://rajlaboratory.blogspot.com/2014/04/how-to-re-review-paper.html)

    * [RajLab] [Annotated example review of science paper](https://docs.google.com/document/d/1unO4J36sfmfynFNjBbjkjIiwrdAh0rg9BAB7TKF0R_M/edit)

    * [RajLab] [Annotated example review of tech paper](https://docs.google.com/document/d/1k-DCuiR0cDM4h04AQXFjViNF2A0V0MLtA9zu9e-JmcU/edit)



## Career



* Letters of Recommendation

    * [RajLab] [RajLab: Dear me, I am awesome. Sincerely, me… aka How to write a letter of rec for yourself](https://rajlaboratory.blogspot.com/2019/02/dear-me-i-am-awesome-sincerely-me-aka.html)

* PhD

    * [Choosing your dissertation committee](https://www.dropbox.com/s/rj0yedv0v55n5nl/ChoosingyourDissertationCommittee.doc?dl=0)

    * [PhD: An uncommon guide to research, writing & PhD life](https://www.dropbox.com/s/omgfbmklnudy1m2/PhD_an_uncommon_guide_to_research_writing_and_PhD_life_free_sample%20copy.pdf?dl=0)

    * [Phobidden FooD](https://www.dropbox.com/s/o0y0oha9f8ylcjf/PhorbiDden_PhooD_1stEdition.pdf?dl=0)

    * [RajLab] [How to assemble and use a thesis committee](https://docs.google.com/document/d/14w3XX1n8Ees2Wy_pIZFj3FN645Yr4mQX_Hyav7xneRw)

    * [Documenting your PhD — Keeping Track of Meetings, Experiments and Decisions • David Stutz](https://davidstutz.de/documenting-your-phd/)

    * Fellowships

        * [RajLab] [So you want to apply for a PhD fellowship?](https://docs.google.com/document/d/1WTMW3LZl1ifpFE1ddH1lvfijmmMsFwZggwsuQcotV_A/edit)

        * [RajLab] [Example F30 specific aims](https://docs.google.com/document/d/1DPCGlyU6yoSPnnZH1EAtLmdPs8CRuF2CWoyw3JEoj84/edit#heading=h.kr6a3jnprsx7)

* Jobs

    * Broad CS:

        * [Computer Science Graduate Job and Interview Guide](https://web.eecs.umich.edu/~weimerw/grad-job-guide/guide/index.html)

        * [Shomir Wilson] [Example CV for PhD Students](https://shomir.net/wilhom_rosins.html)

    * CS Academia:

        * [Thoughts from my faculty application experience](https://docs.google.com/document/d/1ucYHlFbIw87sTWKH3KfP78giPVQycc8R7O5--yxXw0U/edit)

        * [Academic Job Search](https://docs.google.com/document/u/1/d/e/2PACX-1vSeOnC_QdaJVc3OuuMfDHVlk3QotUxvghytRFaDsrdA0uovD5axQjp8kJCM4Evu1cCf9Hg_u_Stabu1/pub)

        * [Interview Questions for Computer Science Faculty Jobs](https://csfaculty.github.io/)

        * [https://sites.google.com/view/elizabethbondi/blog?pli=1](https://sites.google.com/view/elizabethbondi/blog?pli=1)

        * [Faculty job talks: tips from the faculty – MIT EECS](https://www.eecs.mit.edu/career-opportunities-at-eecs/faculty-job-talks-tips-from-the-faculty/)

        * [Guide for the Tenure-Track Job Market in Computer/Information Sciences](https://shomir.net/tt_job_guide.html)

    * Academia General:

        * [Faculty Application : EECS Communication Lab](https://mitcommlab.mit.edu/eecs/commkit/faculty-application/)

        * [https://rosecersonsky.com/assets/slides/2022-06-14-academic-jobs.pdf](https://rosecersonsky.com/assets/slides/2022-06-14-academic-jobs.pdf)

        * [Virtual Workshops - YouTube](https://www.youtube.com/playlist?list=PLAc3DH2raxwoljRhz0x8w8cXHSffewgd8)

        * [Tips for negotiating salary and startup for newly-hired tenure-track faculty | Dynamic Ecology](https://dynamicecology.wordpress.com/2017/03/01/tips-for-negotiating-salary-and-startup-for-newly-hired-tenure-track-faculty/)

        * [RajLab] [Thoughts on applying for faculty jobs](https://docs.google.com/document/d/1Yew6wb1PMDPanPyJIRspqFvYDcIe7FwwvmKheRy8XHI/edit#heading=h.mmvbe39ryleo)



## Fun



* [On the Turing Completeness of PowerPoint](https://www.youtube.com/watch?v=uNjxe8ShM-8)



## Classic Papers



* Statistics

    * Lasso - Tibshirani, [Regression shrinkage and selection via the lasso](http://statweb.stanford.edu/~tibs/lasso/lasso.pdf)

    * Group Lasso - Yuan & Lin, [Model selection and estimation in models with grouped variables](http://pages.stat.wisc.edu/~myuan/papers/glasso.final.pdf)

    * Covariance Regularization - Witten & Tibshirani, [Covariance regularized regression and classification for high-dimensional problems](http://faculty.washington.edu/dwitten/Papers/WittenTibshiraniJune2010CorrectedForWebsite.pdf)

    * L1/L2 Regularization - Obozinski et al., [High-dimensional support union recovery in multivariate regression](http://papers.nips.cc/paper/3432-high-dimensional-support-union-recovery-in-multivariate-regression.pdf)

* Machine Learning

    * [Latent Dirichlet Allocation](https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf)

    * [On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes Andrew Y. Ng Computer Science](https://ai.stanford.edu/~ang/papers/nips01-discriminativegenerative.pdf)

    * [A Unifying Review of Linear Gaussian Models](https://cs.nyu.edu/~roweis/papers/NC110201.pdf)

    * [Information Theory and Statistical Mechanics - ET Jaynes](https://batistalab.com/classes/CHEM584/Jaynes.pdf)

* Computer Science

    * [Best Paper Awards in Computer Science](https://jeffhuang.com/best_paper_awards/)

    * Structure and Interpretation of Computer Program ([video lectures](http://groups.csail.mit.edu/mac/classes/6.001/abelson-sussman-lectures/))

    * [Hints for Computer System Design](http://www.cs.cmu.edu/~15712/papers//lampson83.pdf) - Butler Lampson

    * [End-to-End Arguments in Systems Design](http://www.cs.cmu.edu/~15712/papers//saltzer84.pdf) - J.H. Saltzer, D.P. Reed, D.D. Clark

* Deep Learning Basics

    * [Ilya Sutskever's "90% of Deep Learning in 30 papers"](https://arc.net/folder/D0472A20-9C20-4D3F-B145-D2865C0A9FEE)

    * [Deep Learning book chapter on convolutional nets](http://www.iro.umontreal.ca/~bengioy/DLbook/convnets.html)

    * [Generalization and Network Design Strategies](http://yann.lecun.com/exdb/publis/pdf/lecun-89.pdf) - LeCun

    * [ImageNet Classification with Deep Convolutional Neural Networks](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf) - Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton, NIPS 2012.

    * [On Random Weights and Unsupervised Feature Learning](http://www.stanford.edu/~asaxe/papers/Saxe%20et%20al.%20-%202010%20-%20On%20Random%20Weights%20and%20Unsupervised%20Feature%20Learning.pdf)

    * [Quoc Le's lectures on Deep Learning](http://www.trivedigaurav.com/blog/quoc-les-lectures-on-deep-learning/?owa_referral=pitt&owa_source=~gtrivedi/blog/quoc-les-lectures-on-deep-learning/)

    * [Introduction to NN architectures](http://culurciello.github.io/tech/2016/06/04/nets.html?utm_content=bufferbbc97&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer)

* Computational Biology

    * GWAS / eQTL mapping

        * [Graph-guided fused Lasso]((http://www.plosgenetics.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pgen.1000587&representation=PDF)) - Kim & Xing

        * [Tree-guided group Lasso](http://www.cs.cmu.edu/~sssykim/papers/tlasso_final.pdf) - Kim & Xing

        * [Joint eQTL Mapping & Network Inference](http://www.cs.cmu.edu/~sssykim/papers/377_paper.pdf) - Sohn & Kim

    * Gaussian Graphical Models / Gene Networks

        * [Graphical Lasso](http://statweb.stanford.edu/~tibs/ftp/graph.pdf) -- Friedman et al.

        * [Neighborhood Selection](https://projecteuclid.org/download/pdfview_1/euclid.aos/1152540754) -- Meinshausen & Buhlmann

        * [KELLER: estimating time-varying interactions between genes](http://bioinformatics.oxfordjournals.org/content/25/12/i128.full.pdf) -- Song et al.

        * [TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages](https://www.cs.cmu.edu/~apparikh/papers/Parikh_Wu_Curtis_Xing_ISMB11.pdf) -- Parikh et al. 

        * [Structured learning of gaussian graphical models](http://papers.nips.cc/paper/4499-structured-learning-of-gaussian-graphical-models.pdf) -- Mohan et al., 

        * [Bayesian inference of multiple gaussian graphical models](http://www.stat.rice.edu/~marina/papers/JASA14.pdf) -- Peterson et al., 

        * [Learning graphical models with hubs](http://jmlr.org/papers/volume15/tan14b/tan14b.pdf) -- Tan et al., 

        * [An Introduction to Graphical Models](http://www.cis.upenn.edu/~mkearns/papers/barbados/jordan-tut.pdf) (Mike Jordan, brief course notes)

        * [A View of the EM Algorithm that Justifies Incremental, Sparse and Other Variants](http://www.cs.toronto.edu/~radford/ftp/emk.pdf) - Neal & Hinton, 

        * [A Unifying Review of Linear Gaussian Models](http://authors.library.caltech.edu/13697/1/ROWnc99.pdf) - Roweis & Ghahramani

        * [An Introduction to Variational Methods for Graphical Models](http://www.cs.berkeley.edu/~jordan/papers/variational-intro.pdf) - Jordan et al.