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<!DOCTYPE html>
<html lang="en-US">
<head>
<meta http-equiv="content-type" content="text/html; charset=utf-8">
<title>Massive Computational Experiments, Painlessly (STATS 285)</title>
<meta name="description" content="">
<meta name="viewport" content="width=device-width, initial-scale=1">
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<body>
<section class="page-header">
<div>
<h1 class="project-name">Massive Computational Experiments, Painlessly (STATS 285)</h1>
<h2 class="project-tagline">Stanford University, Fall 2023</h2>
<h2 class="project-tagline text-italic">Website Relocated: <a href="https://sites.google.com/stanford.edu/stats285">New STATS 285 Website</a> </h2>
<!--
<h2 class="project-tagline text-italic">Ambitious Data Science requires massive computational experimentation; the entry ticket for a solid PhD in some fields is now to conduct experiments involving 1 Million CPU hours. This course covers state-of-the-art practices for conducting massive computational experiments in the cloud in a pain-free and reproducible manner. In addition to giving students a hands-on experience with cluster computing, the course features several guest lectures by renowned data scientists.</h2>
</div>
<div class="tile-row">
<div class="tile tile-bordered text-white">
<h2>Instructors:</h2>
</div>
<div class="tile">
<div class="instructor">
<a href="https://statweb.stanford.edu/~donoho/">
<div class="instructorphoto"><img src="assets/img/donoho17-2.jpg"></div>
<div class="text-white">
David Donoho
</div></a>
</div>
<div class="instructor">
<a href="https://web.stanford.edu/~kipnisal/">
<div class="instructorphoto"><img src="assets/img/Alon_Kipnis_2.jpg"></div>
<div class="text-white">
Alon Kipnis
</div></a>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/mahsa-lotfi/">
<div class="instructorphoto"><img src="assets/img/Mahsa_Lotfi.jpg"></div>
<div class="text-white">
Mahsa Lotfi
</div></a>
</div>
</div>
</div>
<div class="tile tile-bordered col-1 text-center">
<h2>TA</h2>
<div class="instructor">
<a href="http://web.stanford.edu">
<div class="instructorphoto"><img src="assets/img/face-1.jpg"></div>
<div class="text-white">
TA
</div></a>
</div>
</div>
-->
</section>
<section class="main-content">
<!--
<div class="items">
<div class="item">
<h1>Logistics</h1>For questions, concerns or bug reports, please contact
<a href="https://web.stanford.edu/~kipnisal/">Alon Kipnis</a> or
<a href="https://www.linkedin.com/in/mahsa-lotfi/">Mahsa Lotfi</a> or
<a href="https://profiles.stanford.edu/david-donoho">David Donoho</a>.
This course meets Mondays 2:30-3:50 PM on Zoom. If you are a guest speaker for
this course, please read <a href="#plan-your-visit">travel section</a> to plan your visit.
</div>
<div class="item">
<div class="jekyll-twitter-plugin">
<a class="twitter-timeline" data-height="300" href="https://twitter.com/stats285?ref_src=twsrc%5Etfw">Tweets by stats285</a>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
</div>
</div>
</div>
-->
<!--
<h2 id="follow-stat285-on-researchgate-videos"><a href="https://www.researchgate.net/project/Massive-Computational-Experiments-Painlessly">Follow Stat285 on ResearchGate (videos)</a></h2>
<h2 id="stats285-hackathon"><a href="assets/hackathon/hack">Stats285 Hackathon</a></h2>
<a href="https://www.youtube.com/watch?v=twGjvNNKTcM&feature=youtu.be"><img src="./assets/img/hack-thumbnail.png" width="400"></a>
<p><a href="https://www.youtube.com/watch?v=7AkjNSDaR5M&feature=youtu.be"><img src="./assets/img/hack-thumbnail2.png" width="400"></a></p>
-->
<!--
<h2 id="data-science-news">Data Science News</h2>
<ul>
<li>
<a href="https://www.nap.edu/read/24886/chapter/1">Envisioning the Data Science Discipline (NAS)</a>
</li>
<li>
<a href="https://www.kaggle.com/paultimothymooney/2020-kaggle-data-science-machine-learning-survey">The State of Data Science (Kaggle)</a>
</li>
</ul>
<h2 id="-guest-lectures"><a href="#guest_lectures"></a> Guest Lectures</h2>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/donoho17-2.jpg"></div>
<div class="card">
<a class="talkdate" href="./assets/lectures_21/Lecture-01-20210329.pdf">Mon, 03/29/2021</a><br>
<h4>The Revolution is Here!</h4>
<span class="speaker">David Donoho</span><br>
<span class="speakerposition">Stanford</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/XiaoyanHan.jpg"></div>
<div class="card">
<a class="talkdate" href="./assets/lectures_21/XYZ Studies-X.Y. Han.pdf">Mon, 04/05/2021</a><br>
<h4>XYZ Studies</h4>
<span class="speaker">Xiaoyan Han</span><br>
<span class="speakerposition">Cornell</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/VardanPapyan.png"></div>
<div class="card">
<a class="talkdate" href="./assets/lectures_21/Lecture-03-STATS285-2021-04-12.pdf">Mon, 04/12/2021</a><br>
<h4>Massive Computational Experiments, Painlessly</h4>
<span class="speaker">Vardan Papyan</span><br>
<span class="speakerposition">University of Toronto</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/Mahsa_Lotfi.jpg"></div>
<div class="card">
<a class="talkdate" href="./assets/lectures_21/Lecture-04-20210419.pdf">Mon, 04/19/2021</a><br>
<h4>IT Infrastructure for Research</h4>
<span class="speaker">Mahsa Lotfi</span><br>
<span class="speakerposition">Stanford University</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/Alon_Kipnis_2.jpg"></div>
<div class="card">
<a class="talkdate" href="./assets/lectures_21/Lecture-05-Kedro-20210426.pdf">Mon, 04/26/2021</a><br>
<h4>Painless Data Pipelining with Kedro</h4>
<p>According to its documentation, “Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code“ that “borrows concepts from software engineering best-practice and applies them to machine-learning code; applied concepts include modularity, separation of concerns, and versioning”. In this lecture, we introduce the main features of Kedro and demonstrate how they can alleviate many of the pain points of data science experiments.</p>
<span class="speaker">Alon Kipnis</span><br>
<span class="speakerposition">Stanford University</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/XiaoyanHan.jpg"></div>
<div class="card">
<a class="talkdate" href="./assets/lectures_21/Lecture-06-tableau_painless_2021-2.pdf">Mon, 05/03/2021</a><br>
<h4>Exploratory Data Analysis, Painlessly</h4>
<p>One key aspect for the analysis and exploration of datasets is for the scientist to be able to quickly and painlessly clean, summarize, and visualize the data. Traditional solutions include code-based frameworks like Tidyverse in R or pandas/matplotlib in Python—or even spreadsheet based approaches such as Microsoft Excel. However, these options are often cumbersome with significant time overheads to perform even the simplest tasks; thereby, putting a significant barrier between a dataset and the scientists who may wish to understand it. Recently years have seen the rise in “Business Intelligences” tools such as Tableau or Microsoft BI that allow users to clean and visualize data using simple drag-and-drop GUI interfaces that significantly cuts down—if not eliminate—the overhead of working with code or spreadsheets. In this lecture, we will explore this new trend in data cleaning and visualization through some simple demonstrations in Tableau.</p>
<span class="speaker">Xiaoyan Han</span><br>
<span class="speakerposition">Cornell</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/vcs.jpg"></div>
<div class="card">
<a class="talkdate" href="./assets/lectures_21/Lecture-07-Stodden-20210510.pdf">Mon, 05/10/2021</a><br>
<h4>When are Data Science Results Reproducible?</h4>
<p>A defining feature of science is independent reproducibility of results. Since the 1600’s this requirement was satisfied through written language (e.g. English) descriptions of the research steps in the final publication, intended to permit another researcher in the field to carry out the same experiment. In this talk I will discuss what reproducibility might mean in the modern context of massive data science experiments, and the state of the debate today. Reproducibility implies transparency and reliability whose interpretation can present new challenging questions at computational scale, using massive datasets, and regarding bias, incentives, and public access to and trust in science. Reproducibility in computational science was first identified as a research area in the early 1990’s by Stanford Professor Emeritus Jon Claerbout who presented implementations and guiding principles. Since then, as the use of computation in scientific discovery became ubiquitous, myriad approaches have emerged. I will trace this history to give a clear understanding of ongoing reproducibility discussions and solutions, and present recent contributions including the Whole Tale project (2020), AIM for reproducibility in ML tournaments (2018), and Reproducibility Standards Development including the National Academies report "Reproducibility and Replication in Science" (2019) for which I was a committee member. I will motivate what I believe are the most pressing problems to be solved to ensure computational and data-enabled scientific research is reproducible and elucidate a vision for reproducible discovery in data science.</p>
<span class="speaker">Victoria Stodden</span><br>
<span class="speakerposition">University of Southern California</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/donoho17-2.jpg"></div>
<div class="card">
<a class="talkdate" href="./assets/lectures_21/Lecture-01-20210329.pdf">Mon, 05/17/2021</a><br>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/mark_piercy_profile_pic.png"></div>
<div class="card">
<a class="talkdate" href="./assets/lectures_19/Stats_285_Fall2019_Sherlock.pptx">Mon, 09/30/2019</a><br>
<span class="speaker">Mark Piercy</span><br>
<span class="speakerposition">Stanford (SRCC)</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/riccardo_murri_roundpic.jpg"></div>
<div class="card">
<a class="talkdate" href="./assets/lectures_19/lecture04.pdf">Mon, 10/14/2019</a><br>
<span class="speaker">Riccardo Murri</span><br>
<span class="speakerposition">University of Zurich</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/PercyLiang.jpg"></div>
<div class="card">
<a class="talkdate" href="./PercyLiang_lecture">Mon, 10/21/2019</a><br>
<span class="speaker">Percy Liang</span><br>
<span class="speakerposition">Stanford</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/OrhanFirat.jpg"></div>
<div class="card">
<a class="talkdate" href="./OrhanFirat_lecture">Mon, 10/28/2019</a><br>
<span class="speaker">Orhan Firat</span><br>
<span class="speakerposition">Google</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/VardanPapyan.png"></div>
<div class="card">
<a class="talkdate" href="./VardanPapyan_lecture">Mon, 11/4/2019</a><br>
<span class="speaker">Vardan Papyan</span><br>
<span class="speakerposition">Stanford</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/LelandWilkinson.jpg"></div>
<div class="card">
<a class="talkdate" href="./LelandWilkinson_lecture">Mon, 11/11/2019</a><br>
<span class="speaker">Leland Wilkinson</span><br>
<span class="speakerposition">H2O, UIC</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/HanLiu.jpg"></div>
<div class="card">
<a class="talkdate" href="./HanLiu_lecture">Mon, 11/18/2019</a><br>
<span class="speaker">Han Liu</span><br>
<span class="speakerposition">Northwestern</span>
</div>
</div>
<hr>
<div class="speaker-wrap">
<div class="speakerphoto">
<img src="assets/img/vcs.jpg">
</div>
<div class="card">
<a class="talkdate" href="./vcs_lecture"> Monday, 10/16/2017</a> <br>
<span class="speaker">Victoria Stodden</span> <br>
<span class="speakerposition">iSchool, UIUC</span>
</div>
</div>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/Percy_liang.jpg"></div>
<div class="card">
<a class="talkdate" href="./percy_lecture">Monday, 10/23/2017</a><br>
<span class="speaker">Percy Liang</span><br>
<span class="speakerposition">Stanford</span>
</div>
</div>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/travis_oliphant.jpg"></div>
<div class="card">
<a class="talkdate" href="./travis_lecture">Monday, 10/30/2017</a><br>
<span class="speaker">Travis Oliphant</span><br>
<span class="speakerposition">Anaconda</span>
</div>
</div>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/riccardo_murri.jpg"></div>
<div class="card">
<a class="talkdate" href="./2019/murri_lecture.html">Monday, 11/06/2017</a><br>
<span class="speaker">Riccardo Murri</span><br>
<span class="speakerposition">University of Zurich</span>
</div>
</div>
<div class="speaker-wrap">
<div class="speakerphoto"><img src="assets/img/Andy_konwinski.png"></div>
<div class="card">
<a class="talkdate" href="./konwinski_lecture">Monday, 11/13/2017</a><br>
<span class="speaker">Andy Konwinski</span><br>
<span class="speakerposition">Databricks</span>
</div>
</div>
-->
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<h2 id="lecture-slides"><a href="./lecture_slides">Lecture slides</a></h2>
<h2 id="plan-your-visit"><a href="speaker_visit">Plan your visit</a></h2>
<h2 id="visit-previous-iteration-of-stats285-2018"><a href="2018">Visit previous iteration of Stats285 (2018)</a></h2>
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