In this working group we learn about methods and ideas for confronting models with data, and about data science generally.  Anyone is encouraged and welcome to attend.

Meeting schedule and topics:

In the Spring 2023 semester, we will meet in person again, Fridays 1pm in EN 2101.

Spring 2023

• 03 Feb 2023 We’ll have an informal planning meeting. Come along and let us know what you’d like to see in the Data Science Meetup. There’ll be muffins.

• 24 Feb 2023 “Finding Ice Age houses in Colorado and other problems in archaeological data analysis”, presented by Todd Surovell (Anthropology).

• 03 Mar 2023 “Using adaptive prior regularization to quantify species interactions in diverse communities”, presented by Christopher Weiss-Lehman (Botany).

• 24 Mar 2023 “Genomic approaches to studying North American snake diversity”, presented by Sean Harrington (INBRE).

• 07 Apr 2023 “Adventures in hierarchical Bayesian modeling”, presented by Andrew Siefert (Botany). For this one, we’ll be on zoom at https://uwyo.zoom.us/j/95012886827

• 14 Apr 2023 “Efficiently Mobilizing Data for Decision Making -- Implications for Data Science Curriculum Design”, presented by Timothy Robinson (Maths and Stats).

• 28 Apr 2023 “Data in Hydrology - More than just Streamflow”, presented by Fabian Nippgen and Salar Jarhan (Ecosystems Science and Management).

• 05 May 2023 "Leveraging Data of Large-Scale High-Fidelity Numerical Simulations in Wind Energy Applications", presented by Andrew Kirby (School of Computing).

Fall 2022

We’re going to try something different this semester and have a series of presentations from faculty and students across UW to see the breadth of data science (in the widest sense). The format is a short-ish talk/demo, followed by questions – we want this to be as interactive as possible! In the Fall 2022 semester, we met at 1 p.m. Fridays (Mountain Time) on Zoom.

If you’re interested in contributing a talk/demo/?, get in touch with Lars Kotthoff.

• 26 Aug 2022 “Physics-informed neural-networks (PINN) for solving linear elastic solid mechanics problems: a comparison with finite element method”, presented by Min Lin and Xiang Zhang (Mechnical Engineering).

• 02 Sep 2022 “Quantitative Structure-Property Relationships of COFs for Desalination”, presented by Ali Davies and Laura de Sousa Oliveira (Chemistry).

• 09 Sep 2022 “Rapid Introduction to Spatial R”, presented by Shannon Albeke (WyGISC). You can download the script and data here: Albeke_Example

• 16 Sep 2022 “Hands-On Introduction to Deep Learning”, presented by Mehdi Nourelahi (EECS).

• 23 Sep 2022 “Flow and transport in large-scale complex permeable media”, presented by Saman Aryana (Chemical and Biomedical Engineering). Note that this will be at 2pm instead of 1pm!

• 30 Sep 2022 “Dynamic Analysis of Ecological Restoration of damaged landscapes with Chronosequences, and building Models to Generate Alternatives for policy makers”, presented by Roger Coupal (Agricultural and Applied Economics).

• 07 Oct 2022 “Deep learning methods for geophysical inverse problems and applications to subsurface characterization”, presented by Dario Grana (Geology and Geophysics) and Mingliang Liu (Stanford).

• 14 Oct 2022 “Connecting Data Science and the Sustainable Built Environment”, presented by Liping Wang (Civil and Architectural Engineering).

• 21 Oct 2022 No talk.

• 28 Oct 2022 No talk, again.

• 04 Nov 2022 No ta… wait, no, actually there will be a talk. “Data-intensive freshwater ecology”, presented by Sarah Collins (Zoology and Physiology).

• 11 Nov 2022 “Divide and Conquer: Partitioning the Sky with the Hierarchical Equal Area isoLatitude Pixelisation”, presented by Adam Myers (Physics and Astronomy).

• 18 Nov 2022 02 Dec 2022 “Invasion informatics! Harnessing the big data revolution to overcome invasive species”, presented by Kelsey Brock (Plant Sciences).

Spring 2022

• 19 Jan 2022 – building an R package (for CRAN, GitHub, or Bioconductor).

  • how to make a minimal R package in the style expected by CRAN. We might want to work through https://r-pkgs.org/whole-game.html from https://r-pkgs.org (a book focused on this topic).

  • Here is a quick tutorial, that is similar to the “whole game” tutorial linked above, but with a few twists.

  • hosting options and considerations

• 26 Jan 2022 – In advance of our meeting, please watch https://youtu.be/dag9l0GFci8 from the 2021 useR conference and come ready to discuss. This 40 minute talk (plus ~15 minutes of Q&A) covers a diversity of topics and provides some food for thought. See The United States Research Software Engineer Association and their job board.

• 02 Feb 2022 – Damir Pulatov – How to write unit tests for your R code why you should do it.

  • Brief shoutout - useR is an R conference and there is a call to offer tutorials (link goes to the useR page) at the conference. Proposals are due by Feb 2015. Might be a way to ‘level up’ for some of us. Suggested topics are broad and introductory (e.g., git with R, data visualization basics, etc.)

• 09 Feb 2022– … R, Iteration in R – loops, apply, map functions, Functional(s) and programming in R Eryn McFarlane - back to very basics. link here

• 16 Feb 2022 – No meeting this week. Consider watching this recorded talk instead: https://www.youtube.com/watch?v=B7TBfJrofQM

  • Not related, but I found this article on the use of AI insightful – https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/

• 23 Feb 2022 – visit with Nick Anderson, security engineer at Facebook, undergrad degree in math from UW, Wyoming native. We will learn about his education and career path and ask him questions like (please bring your own too):

  1. what advice would you give to yourself as a finishing undergrad at UW, what do you wish you had known at that point?

  2. what things would you encourage students and postdocs to do to prepare themselves for a career in security, or more broadly a career in a technology company?

  3. what are some of the main areas in which technology companies deal with security?

  4. what is your area of speciality within security and how has this changed over time?

  5. what are areas that you are excited learning more about in security?

  6. in what ways do machine learning experts and data scientists contribute to security teams?

• 02 Mar 2022 – Development environments (a.k.a. pimp my rIDE)

  • Discussion of last week’s meeting with Nick Anderson.

  • Code editors – Share, demo, try out different IDEs / editors: Visual Studio Code, BBedit, emacs ( read and write files remotely with built-in ssh support: /ssh:teton:tmp.txt; split screens and multiple windows on same buffer), vi, Rstudio, atom, Eclipse.

• 09 Mar 2022 – Some learning about Apache Arrow – in advance, please watch short talk by Wes McKinney (25 min, demo starts around 18 min) and look over this short introduction.

  • we will discuss and share thoughts on the video and short introduction

  • We can experiment with these simple examples. https://arrow.apache.org/docs/r/

  • We will also step through this vignette, which is part of the R package ‘arrow’. It calls for working with 37 GB of data from AWS S3. Consequently, I suggest we skip trying to run the code ourselves, but instead we step through the vignette and work to understand what it is doing without executing it ourselves.

  • Extras:

    • High-performance genetic datastore on AWS S3 using Parquet and Arrow

    • Optimizing performance of GATK workflows using Apache Arrow In-Memory data framework (see https://github.com/abs-tudelft/ArrowSAM)

• 16 Mar 2022 – spring break – no meeting

• 23 Mar 2022 – Interactive visualizations with Shiny – demo and hands-on – Heili Lowman – here is the shiny app that Heili Lowman built in the demo.

  • For inspiration when building your own Shiny applications, there are lots of examples online in the Shiny Gallery. There are also a number of examples of Shiny Dashboards, if you prefer that layout.

  • Additional tutorials are available here and here, and lots more details are available in the textbook Mastering Shiny by Hadley Wickham.

  • The Shiny Widget Gallery provides all the code necessary to add any kind of widget you like. The ‘shinyWidgets’ package also has more customizable widgets if you would like to offer the end user more freedom in their selections.

  • Today’s lesson was adapted from a lesson created by Allison Horst for the R Ladies Santa Barbara chapter (GitHub repo for the lesson here). A copy of today’s script ('app.R') can be found below.

  • Alex's example with >12,000 possible plots that are calculated on the fly. This was my first Shiny app, which I use for teaching.

• 30 Mar 2022 and remainder of spring semester – we are going to put the working group on hold for the rest of the semester and will see about regrouping again in fall 2022. Thanks everyone for participating and sharing your interest and enthusiasm for learning things together. Don’t forget the lesson about getting accustomed to failing. Get out there and push yourself and see what you can do. And have fun doing it!

We are inviting some speakers to join us this semester, with a few different potential topics including computer security, value systems associated with data sharing, data science in biotechnology companies, careers in data science, etc. If you have suggestions or requests, please add them here or in the wishlist below.

Wish list for topics

Please add and edit this list, including expanding the plan and volunteering to lead on one of these topics.

• Short (5-10 minute) presentations by group members on data science related to their research with Q&A afterwards

• See page that lists previous and potential people to invite to talk about careers in non-academic data science (requires login).

• Reproducible research with workflow managers like Nextflow, and visit from bioinformatician at Wyoming Department of Health

• Compositional data analysis,

• Machine learning

  • Scaling up machine learning. Would like to do a hands-on project on some really large data (leaving this vague, so we can find something useful to the broader group).

  • A more introductory overview of machine learning for beginners. Maybe someone can give some background for part of a group meeting, and several of us can share how we are implementing machine learning (or would like to) in our own research? Joshua Harrison

  • Bayesian machine learning/neural networks

• Computational statistics

  • Bayesian multilevel modeling (using the brms R package or other methods)

  • Bayesian methods that don’t involve MCMC: variational inference, INLA, ABC

  • Nonlinear modelling (frequentist and Bayesian approaches)

  • Compositional data analysis, potentially as applied to microbiome data – transformations, and considerations (useful for those working with 16S sequence data), or other bioinformatics-related topics if there is enough demand. Scott Klasek Melissa DeSiervo I could provide perspective / share some stuff about compositional data that is not 16S related. I’ve been working with this “ecotraj” package in R to perform some analyses on speed and directionality of community shifts https://cran.r-project.org/web/packages/ecotraj/vignettes/IntroductionETA.html

  • Spatial analysis

• Text mining intro - a basic primer of questions and tools and maybe a follow-up if anyone is interested in digging deeper. Maybe with a focus on how the methods have been applied to biological questions, rather than social science questions.

• Linux

  • Intermediate bash tips & tricks

  • replicable data cleaning and manipulation

• How to make your own SQL database

• Webscraping

• Visualization

  • Interactive plots

  • How to make an html-based dashboard, with an example for hands-on learning.

  • Effective story-telling and visualization for scientific research with applications in R

• Math for machine learning/statistics: linear algebra, probability, calculus

Fall 2021

• 01 Sep 2021 – Organizational meeting

  • develop list of topics for the semester

  • pair and share – break out into smaller groups and share what you have been up with respect to data science, what you’ve been wanting to learn, and learn about other people in the group.

• 08 Sep 2021 – Lars Kotthoff will do a brief intro to mlr3pipelines

  • https://mlr3pipelines.mlr-org.com

  • https://mlr3book.mlr-org.com/pipelines.html

  • https://mlr3gallery.mlr-org.com/#category:mlr3pipelines

• 15 Sep 2021 – Dylan Perkins (ARCC/End User Support): Intro to shared computing at UW – Teton compute and storage resources (video is below, slides are in https://docs.google.com/presentation/d/145AVEOLHi22CPn0IwpLkVCNWJ7ZzGJq1fZDWt4IBE8I/edit?usp=sharing).

  • As a follow-up, please contact arcc-info@uwyo.edu with questions.

  • Please drop them a line if you are interested in participating in some testing of the new browser-based, graphical interface they are developing to the teton compute resources. They would appreciate several people testing the system.

    dylan_perkins_ARCC_15sept21.mp4

• 22 Sep 2021 – hands on teton

  • Tasks you would like help with or demonstrated, from entry-level to advanced

    • we did one big group screen share to show:

      • how to configure ssh so that we can provide Teton password and validation (2FA) once per session

      • how to launch a SLURM interactive session to do some text wrangling with UNIX command line tools

      • demonstrate two ways of using text editors to write bash scripts that can be executed on teton; this include making a SLURM compliant script that we submitted withs batch

      • our script demo’d the use of /dev/shm, /lscratch, and /gscratch for simulation output and how to move data home and clean up after yourself at the end

    • We did not get to demo how to install R packages yourself. Instead, I started a Knowledge Base entry on this, which you are welcome to add to, edit, and improve.

• 29 Sep 2021 – Short demonstration of LaTeX as implemented in Overleaf, followed by hands-on session for participants to sign-up for a free account, make documents with one or more templates, and ask questions of more experienced users. Alex Buerkle will do initial demo and will ask for helpers to assist others in hands-on session.

  • Overleaf features:

    • the direct submission button (arxiv, biorxiv, society journals, etc.)

    • built-in version control

    • built-in multi-author, shared editing

  • Latex features

    • references to figures, tables, supplementary sections

    • citation management and bibliography

    • sectioning

  • Follow-up:

    • see Overleaf documentation, including:

      • creating your first latex document

      • creating a document from a template

      • https://www.overleaf.com/learn/how-to/Using_bibliographies_on_Overleaf

• 06 Oct 2021 – an introduction to Bayesian modeling. Eryn McFarlane Topher Weiss-Lehman will discuss the basis of Bayesian thinking and talk about why one might want to use Bayesian methods.

• 13 Oct 2021 – an introduction to computational Bayesian modeling (Andrew Siefert , Joshua Harrison. Why do computers help when doing Bayesian statistics? What does sampling and convergence mean? A high level overview of the different tools one can use to do Bayesian statistics. Finish with illustration of a model specified with R and Stan so that folks can get an idea of the modeling process.

  • Animation of samplers for Bayesian modeling: https://chi-feng.github.io/mcmc-demo/app.html

  • HERE is a git repo that has the code for the little mini-talk that Josh gave. We can keep posting Bayesian stuff here if we want. Feel free to do pull requests. If you have not used git, you can go to that link and view the different files and download them as you like.

• 20 Oct 2021 – Breakout groups for hands-on and Q&A regarding Bayesian models for parameter estimation and inference. Request a group below. We’ll add one or two more at the beginning of the meeting.

  • Bayesian hierarchical modeling using brms (an R interface that can use traditional model specification as one would find in lme4 to create and run models in STAN)

  • More about specifying models in STAN

  • More about model specification itself, before one writes code to implement the model.

• 27 Oct 2021 – Regular expressions tutorial and practice – Alex will lead and we will do hands-on work in break out groups. Regular expression gymnastics - fall 2021

  • We will use R and https://regex101.com as sandboxes for learning

  • Regular expressions are a tool for parsing, modifying, and wrangling text and are present in many programming language, command-line tools, and text editors. They are very useful for extracting information from text fields and for reformatting text for input to different software.

• 03 Nov 2021 – Reproducible research with R, Git, LaTeX, etc. Jessi Rick

  • We’ll discuss creating dynamic documents (i.e., where data, code, analysis, and output are all in one place, with automated updating if any of them are modified) as a way of making research more reproducible. While there are numerous options that all lead toward this end, I’ll be introducing how to do this when R is used for analysis and LaTeX/Overleaf is being used for document creation.

    • Link to the Overleaf tutorial I’ll be working from: https://www.overleaf.com/read/fxmzqhndjdxv

  • Useful links:

    • General intro to reproducible research ideas/tools: https://doi.org/10.1002/bes2.1801

    • Using knitr from within Overleaf: https://www.overleaf.com/learn/latex/Knitr

    • More general intros to using knitr with LaTeX: https://kbroman.org/knitr_knutshell/pages/latex.html and http://edrub.in/ARE212/latexKnitr.html

    • Tutorial for syncing between RStudio and Overleaf using Git/GitHub: https://github.com/jessicarick/resources/blob/master/tutorials/R_Overleaf_Integration.pdf

    • Tutorial for writing manuscripts directly in RMarkdown (no LaTeX necessary): https://jhemberger.github.io/posts/posts/r-markdown-manuscripts/

    • Joshua Harrison 's tutorial on how to use Make to integrate analyses + manuscript into one reproducible workflow: How to use Make with R and Latex

• 10 Nov 2021 – A bit more hierarchical modeling in STAN (why, yes, it is machine learning ).

  • in advance of our meeting, please watch this 11 minute video that illustrates the benefit of using hierarchical models for priors and the information sharing that results. https://youtu.be/dNZQrcAjgXQ.

  • The analyses in the video are in Hierarchical in https://github.com/MaggieLieu/STAN_tutorials.git. You can clone the repo with git clone https://github.com/MaggieLieu/STAN_tutorials.git The directory contains an Rmarkdown file and a result file that is rendered to html. In our meeting we will break into groups discuss the video, experiment with the code, and share experiences.

  • Here are some more tutorials that break down the theory of hierarchical models a bit more, if there’s other folks (like me!) who got a little lost in the weeds yesterday. https://www.youtube.com/watch?v=VssgU4Ey7ss , (and a less math-y version) https://www.youtube.com/watch?v=SMWleVKO9ZM .

• 17 Nov 2021 – No group meeting. Please put requests for topics for the remaining semester meetings in the time slots below.

• 24 Nov 2021 – No meeting, give thanks and take Thanksgiving Break.

• 01 Dec 2021 – Drop-in, social meeting, to discuss ad hoc and other topics (below)

  • Running shiny apps, web-hosting shiny apps, and other interactive visualizations

• 08 Dec 2021 – Hosting webpages at github.io (or bitbucket.io) from scratch. Alex Buerkle will lead and will appreciate input from others.

  • Here’s an example of how a company makes it easy to embed html in your own documents: https://help.twitter.com/en/using-twitter/embed-twitter-feed . The can obviously be done in a Content Management System (CMS), but they are doing it by making a button available to you to insert some html. Similarly, here’s the same thing to embed YouTube videos in your page: https://support.google.com/youtube/answer/171780?hl=en

  • another reason to consider html rather than markdown (or a CMS), is that you can readily embed D3 and related tools directly inside of pages for dynamic visualizations. To make this point, I made my index.html more interesting and have not deleted it from GitHub, yet.

  • And, here, straight from 1998, is a very easy introduction to html (from before css existed). Beyond that, here is the official specification of html and lots of tutorials and modern stuff.

  • Thanks everyone for participating this semester. Have a good break and see you in January.

Spring 2021

• 10 February 2021 – organizational meeting

• 17 February 2021 – Introduction to mlr3 – Damir Pulatov (UW Computer Science graduate student)

  • In advance of our meeting, please read this very short introduction to the use of R6 in R (one form of object-oriented programming in R) from the mlr3 book (~5 minutes).

• 24 February 2021 – profiling and comparison of code running on data.table versus data.frame.

• 03 Mar 2021 – Alex Buerkle will present a short primer on Bayesian estimation and inference. We will do a bit of hands-on with posterior probability distributions. Beyond conceptual and math foundations, we will talk about computational methods for parameter estimation (MCMC, HMC, variational inference).

Below is R code to accompany beta distribution of allele frequencies in a population. This uses the closed form solution for P(p|x,n) = P(x|p,n)*P(p), where the product of a binomial and a beta is a new beta distribution.

(160+1)/(160+1 + 200-160+1)  # expectation with P(p)=beta(1,1)
(160+0.1)/(160+0.1 + 200-160+0.1) # expectation with P(p)=beta(0.1,0.1)

p<-seq(0, 1, 0.001)
plot(p, dbeta(p, shape1=160+1, shape2=40+1), type="l")

par(mfrow=c(2,1))
plot(p, dbeta(p, shape1=160+1, shape2=40+1), type="l", xlim=c(0.5, 1), col="red")
abline(v=qbeta(c(0.025, 0.975), shape1=161, shape2=41), col="red")
abline(v=qbeta(c(0.025, 0.975), shape1=16 + 1, shape2=4+1), col="blue")
lines(p, dbeta(p, shape1=16+1, shape2=4+1), col="blue")

par(mfrow=c(3,1))
plot(p, dbeta(p, 1, 1), type="l") # beta(1,1)
plot(p, dbeta(p, 0.1, 0.1), type="l")  # beta(0.1, 0.1)
plot(p, dbeta(p, 160+1, 200-160+1), type="l")

• 10 Mar 2021 – No meeting. Consider reading Chapters 1 & 2 of Statistical Rethinking (2nd edition) if the introduction to Bayesian inference last week was new to you, or if you want a refresher. Chapter 2 is particularly good. If you want code in various languages from the second edition, you can find it here (R, python, Julia, etc.).

• 17 Mar 2021 – Alex Buerkle will lead a discussion and some hands-on to help learn how to marginalize discrete parameters in hierarchical Bayesian models, in JAGS and STAN.  The example application pertains to modeling the distribution of a type of organism across sampling sites. The idea would apply to any type of detection problem, where a thing might exist at a point or not (a Bernoulli variable), but there are a bunch of ecologists who are interested in ‘occupancy models’. For example, this type of model could apply to monitoring for an invasive species, like Zebra mussels, or New Zealand mud snails, at monitoring sites. The data can be discrete, but discrete parameters that are inferred from the data cannot be used directly in a STAN model (because discrete distributions are not differentiable).

  • In advance, please read over Marginalized occupancy models: a how-to for JAGS and Stan, and try out the code to the extent that it interests you. Please come with questions. We will use this webpage and code as our basis for discussion. Note, JAGS models look like R code, but are not and are instead a model specification.

  • Additional resources:

    • If you are totally new to Bayesian analysis, consider reading Chapters 1 & 2 of Statistical Rethinking, as I suggested for last week

    • For a more explicit and mathematical notation-heavy presentation of many of the same ideas, see A step-by-step guide to marginalizing over discrete parameters for ecologists using Stan

    • See Rao-Blackwellization and discrete parameters in Stan

    • Video tutorial by Ben Lambert

    • Here is some of Mikey’s Stan code to run a pathogen prevalence model (same model structure as occupancy) that accounts for both false positive and false negative error rates. It’s written a little differently than how we discussed things yesterday, because I took advantage of the log_mix function, which I found more efficient when using the two detection parameters.

• 24 Mar 2021 – Continuation of Bayesian modeling – Discussion of a paper: Ensuring identifiability in hierarchical mixed effects Bayesian models. Ogle and Barber 2020. Ecological Applications. 30(7):e02159. (pdf).

• 31 Mar 2021 – Nominally spring break at UW. Initially I overlooked that 31 Mar 2021 is part of spring break, so I have bumped our scheduled discussion to April 7 and we will not plan to meet this week.

• 07 Apr 2021 – Discussion of part of Chapter 5 (Model-Agnostic Methods) of the book Interpretable Machine Learning (most recently updated on 22 March 2021!). Please read the lead-in to the chapter. Additionally, please read sections 5.7-5.10 (or 5.7 and 5.9 only if pressed for time and skim the others).

  • related to this topic, see A translucent box: interpretable machine learning in ecology – Lucas – Ecological Monographs (this paper is not as long as you’d expect based on the journal)

• 14 Apr 2021 – presentation by John Calder on Bayesian mixture modeling. Until recently John was a postdoc at UW, now he works as a data scientist for MX (a financial services company).

• 21 Apr 2021 – Lars Kotthoff will present a brief intro on automated machine learning – bring your hyperparameters.

  • Below is R code that we’ll use as basis for discussion – first doing some hyperparameter tuning “manually”, then with mlr and mlrMBO.

  • For comparing different learners, see https://mlrmbo.mlr-org.com and https://mlr3book.mlr-org.com/benchmarking.html. For avoiding overfitting of hyper parameters, see nested resampling in the MLR3 book.

  • Free online automated machine learning course (including Lars) that just came online.

  • Automated Machine Learning book.

# tune two parameters of the rpart classification tree learner
require(rpart)

train.indices = sample(1:150, 150 * 2/3)
test.indices = setdiff(1:150, train.indices)

evalParams = function(...) {
    model = rpart(Species~., iris[train.indices, ], ...)
    preds = predict(model, newdata = iris[test.indices, -5], type = "class")
    return(list(pars = ..., acc = sum(preds == iris[test.indices, "Species"]) / length(preds)))
}

pars = expand.grid(minbucket = 1:20, minsplit = 1:20)
res = lapply(1:nrow(pars), function(i) do.call(evalParams, as.list(pars[i,])))
best = which.max(sapply(res, function(x) x$acc))
res[[best]]

# ...and now with mlr/MBO
# adapted from https://mlrmbo.mlr-org.com/articles/supplementary/machine_learning_with_mlrmbo.html
require(mlr)
require(mlrMBO)

# tune same parameters
par.set = makeParamSet(
  makeIntegerParam("minbucket", 1, 20),
  makeIntegerParam("minsplit", 1, 20)
)

ctrl = makeMBOControl()
tune.ctrl = makeTuneControlMBO(mbo.control = ctrl, budget = 10)
res = tuneParams(makeLearner("classif.rpart"), iris.task, cv3, par.set = par.set, control = tune.ctrl)

plot(cummin(getOptPathY(res$opt.path)), type = "l", ylab = "mmce", xlab = "iteration")

• 28 Apr 2021 – Work and help session

  • people will document, edit, call out unclear sections, or request documentation

    • how to use teton

    • how to do specific scientific computation tasks (on teton or elsewhere).

  • people will get or give assistance or a demonstration of how to do specific things

    • we will also use break out rooms to assist with specific tasks (please invite your colleagues to help and get help).

• 05 May 2021 – Demo day

  • we will do some short demos of different technologies

    • cloud computing with AWS – we can spin up some different disk images using AWS EC2. There are 400 different instances to choose from. Ideally several of us will get free sessions started on different instances and report back. The interest here is that many companies do their compute in the cloud rather than on shared academic systems. We are fortunate that UW supports our very good shared system within ARCC.

      • if this looks useful for you to experiment with, please sign up for an AWS account. This is free, but they do use a credit card to verify your identity. There is a fair amount of information to share to get set up, and it takes several minutes after all the info is entered, so perhaps do this in advance.

      • Connect to your Linux instance using SSH

      • example of running R on a linux instance or alternatively https://www.louisaslett.com/RStudio_AMI/

      • AWS Cloud Credit for Research Program – grants program, students are eligible

    • Regarding the question of easier access to cloud computing tools, you might be interested in the Discovery Environment at Cyverse, in addition to the more standard cloud computing tools that are available for free to academic researchers.

    • if there is interest:

      • Docker – demo a docker image of MISO, the LIMS software for the GTL.

      • wrap_slurm.pl – a program to launch many SLURM jobs on the cluster that need different inputs (a complement to SLURM arrays).

      • … other requests or suggestions …

• Thanks everyone for participating this spring semester. Let’s call this semester complete and plan to return in the fall semester, very possibly in person in Aven Nelson 210. Feel free to use the data science Confluence space over the summer break to share and organize things. All the best for your research over the summer and for recharging any depleted energy reserves.

Ideas for spring 2021

• Many additional ideas can be found on the Previous meetings archive and The "queue" pages.

• Short research topic presentations with a key methodological or technical illustration from group members. Please indicate your interest in giving a presentation and leading a discussion below.

• Reading and study groups

  • Who would be interested in discussing the 2nd edition of Statistical Rethinking?: Alex Buerkle Joanna Blaszczak Andrew Siefert Erin BentleyShannon E Albeke Bridger Huhn …

  • Other topics?

• Host non-academic data scientists to visit our group and discuss careers, technical things, and other topics of mutual interest

• Possible statistical modeling and machine learning topics

  • Genetic programming and symbolic regression in R: A friendly introduction to RGP, which discusses genetic programming using the RGP package (which is archived on CRAN and is no longer being developed, but is probably useful for learning)

• Hands-on and conceptual learning about sparse models (including following paper by Runge et al.)

• Read and discuss: Detecting and quantifying causal associations in large nonlinear time series datasets. Runge et al., Sci. Adv. 2019; 5 : eaau4996 27 November 2019

• Continue reading a few sections from Interpretable Machine Learning

• read some sections of Machine Learning: a Probabilistic Perspective by Kevin Patrick Murphy?  It is available for free as an ebook from UW library. Alternatively, he is completing two additional books, including Probabilistic Machine Learning: An Introduction.

• Non-centered parameterizations

  • Simple, univariate models

  • Hierarchical models (including understanding Cholesky factorization)

  • Non-multivariate normal alternatives to non-centered hierarchical models

• Wondering about talking with ARCC about methods for reducing IO bottlenecks. I run into this most often with large Raster datasets with multithreading

• AWS - primer and practice usage. A lot of jobs want people to have experience with AWS. Maybe we could set up a UWYO account that lets people practice and see what is about.

• Docker - At some point Jason did a show and tell on Docker (search for Docker on this website to find his pages), but it might be worth revisiting how to use containers, from a practical standpoint. What I mean by that is, how to load up and use containers that others will have built. Most of us, when starting a new job, probably would be using existing docker containers instead of making our own. I assume using these is pretty straightforward, but have never actually done it. Would be useful to do so.