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portfolio.html
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<!DOCTYPE HTML>
<!--
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Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
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<html>
<head>
<title>Erin Conley</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
<link rel="stylesheet" href="assets/css/main.css" />
</head>
<body class="homepage is-preload">
<div id="page-wrapper">
<!-- Header -->
<div id="header-wrapper">
<div id="header" class="container">
<!-- Logo -->
<h1 id="logo"><a href="index.html">Welcome</a></h1>
<!-- Nav -->
<nav id="nav">
<ul>
<li>
<a href="#">All of the things</a>
<ul>
<li><a href="aboutme.html">About me</a></li>
<li><a href="resume.html">Resume</a></li>
<li><a href="portfolio.html">Portfolio</a></li>
<li>
<a href="blog.html">Words</a>
<!-- <ul>
<li><a href="#">Blog post 1</a></li>
<li><a href="#">Archived blog posts</a></li>
<li><a href="#">Specific blog series</a></li>
<li><a href="#">Upcoming blog ideas</a></li>
</ul> -->
</li>
</ul>
</li>
<li><a href="resume.html">Resume</a></li>
<li class="break"><a href="portfolio.html">Portfolio</a></li>
<li><a href="blog.html">More words from me</a></li>
</ul>
</nav>
</div>
<!-- Hero -->
<section id="hero" class="container">
<header>
<h2>Portfolio</h2>
<br />
</header>
</section>
<!-- Main -->
<div class="wrapper">
<div class="container" id="main">
<div class="row gtr-150">
<div class="col-8 col-12-narrower">
<!-- Content -->
<article id="content">
<header>
<h2>Writing Portfolio</h2>
<p>Experience with technical writing, documentation, writing for a general audience, and more</p>
</header>
<!-- <a href="#" class="image featured"><img src="images/pic01.jpg" alt="" /></a> -->
<p>
<h3>Published works from industry</h3>
<br />
<strong>Write your first Kubernetes charm for a Flask app</strong>
<ul>
<li>Charm SDK documentation. <a href="https://juju.is/docs/sdk/write-your-first-kubernetes-charm-for-a-flask-app">Link</a> </li>
<li>Build, deploy and customize a "Hello, world" Flask application using Canonical tooling</li>
<li>Similar tutorials available for <a href="https://juju.is/docs/sdk/write-your-first-kubernetes-charm-for-a-django-app">Django</a>, <a href="https://juju.is/docs/sdk/write-your-first-kubernetes-charm-for-a-fastapi-app">FastAPI</a>, and <a href="https://juju.is/docs/sdk/write-your-first-kubernetes-charm-for-a-go-app">Go</a>
</ul>
<h3>Published works from academia</h3>
<br />
<strong>Impact of cross-section uncertainties on supernova neutrino spectral parameter fitting in the Deep Underground Neutrino Experiment</strong>
<ul>
<li>Physical Review D, June 29 2023. <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.107.112012">Link</a></li>
<li>Technical paper summarizing simulation studies on supernova neutrino physics performed for the Deep Underground Neutrino Experiment</li>
</ul>
<strong>The Low-Energy Charged-Current Electron Neutrino Cross Section on Argon at the Spallation Neutron Source</strong>
<ul>
<li> ProQuest, May 27 2023. <a href="https://www.proquest.com/docview/2819251159">Link</a> </li>
<li> PhD dissertation on the neutrino-argon inelastic interaction cross section. The dissertation includes studies done for both the Deep Underground Neutrino Experiment and the COHERENT experiment. </li>
</ul>
<strong>Supernova neutrino burst detection with the Deep Underground Neutrino Experiment</strong>
<ul>
<li>European Physics Journal C, May 15 2021. <a href="https://link.springer.com/article/10.1140/epjc/s10052-021-09166-w">Link</a> </li>
<li>A summary of the general capabilities for supernova neutrino detection with the upcoming Deep Underground Neutrino Experiment. Personally contributed to the research studies and some text. </li>
</ul>
<strong>Searching for supernova neutrinos with DUNE</strong>
<ul>
<li>Fermi National Laboratory, August 18 2020. <a href="https://news.fnal.gov/2020/08/searching-for-supernova-neutrinos-with-dune/">Link</a> </li>
<li>Article summarizing the efforts on supernova neutrino research in the upcoming Deep Underground Neutrino Experiment.</li>
</ul>
<strong>Snowmass 2021 Community Survey Report</strong>
<ul>
<li>Fermi National Laboratory, March 14 2022. <a href="https://arxiv.org/abs/2203.07328">Link</a> </li>
<li>Summary of the design, conduct, and analysis of a survey to scientists in high-energy physics and astrophysics. As part of the Snowmass 2021 Community Planning.</li>
</ul>
<h3>Unpublished (internal) works from academia</h3>
<br />
<h4><strong>Technical notes, Wiki-style articles, etc.</strong></h4>
<ul>
<li><b>Wiki-style article about LTSpice:</b> Written to accompany the GitLab code repository. The article contained information about the LTSpice circuit simulation software and schematics used in the COHERENT experiment. Available on COHERENT's internal website and edited periodically between June-August 2023.</li>
<li><b>Article for release of Snowmass 2021 Community Survey:</b> Short announcement on the Fermilab News at Work website for the advertise and provide information about the survey. Originally published 2021 June 30. The announcement has now been removed from the FNAL News at Work website. </li>
<li><b>Supporting documentation for the COHERENT Protons-on-Target calculations and official plot:</b> Technical note (11 pages) and Wiki-style article detailing how to produce the official Protons-on-Target (POT) plot for the COHERENT experiment. This plot shows the number of protons delivered to the Spallation Neutron Source (SNS) and all of the COHERENT detectors (both decommissioned and active). Created and edited periodically between April 2020-2023.</li>
<li><b>Supporting documentation for the COHERENT data monitoring on Grafana:</b> Technical note (13 pages) and Wiki-style article detailing the front and back ends of the Grafana data visualization and monitoring for several detectors in the COHERENT experiment. Created and edited periodically between April 2020-2023.</li>
<li><b>Documentation of DUNE machine learning studies:</b> Technical note (30 pages) summarizing machine learning applications for supernova neutrino physics in the Deep Underground Neutrino Experiment (DUNE). The studies focused on boosted decision trees using scikit-learn in Python, and the input was up-to-date simulations of various low-energy particles interactions. Released internally to the DUNE collaboration on 2020 September 23. </li>
<li><b>Documentation of DUNE supernova spectral parameter fitting studies:</b> Technical note (28 pages) summarizing supernova neutrino simulation studies performed for the Deep Underground Neutrino Experiment. Distributed internally in the collaboration; edited periodically from April 2019 to December 2020. </li>
</ul>
<h4><strong>Code repositories from academia</strong></h4>
<ul>
<li><b>GitLab repository for LTSpice:</b> Produced a code repository containing schematic files for the LTSpice circuit simulation software along with documentation containing general information about how to use the schematics provided and specific details about the real circuit involved in the project. Produced and maintained from June-August 2023.</li>
<li><b>GitHub repository to accompany peer-review publication:</b> To accompany the <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.107.112012">2023 publication for DUNE spectral parameter fitting studies</a>, I produced a code repository with the scripts used to produce the plots featured in the paper. The scripts were written in C++ and predominantly use the ROOT physics library. Originally committed to GitHub on 2021 January 8. <a href="https://github.com/erinecon/forward-fitting">Link to repository</a>
</li>
<li><b>GitLab repository Protons-on-Target plotting code:</b> Produced a code repository using inherited code in Python and Bash to produce the POT plot for the COHERENT experiment. This included organizing the various scripts based on specific particle detectors and writing detailed documentation outlining the steps involved in making the official plot. Produced and maintained from April 2021-2023.</li>
<li><b>GitLab repository for data monitoring backend code:</b> Maintained and contributed to a code repository (written in Bash and Python) for the backend of the data processing in preparation for the visualization in Grafana. My biggest contribution was producing scripts to provided real-time data monitoring for one of the COHERENT detectors. Maintained from April 2020-2023.</li>
</ul>
</p>
</article>
</div>
<div class="col-4 col-12-narrower">
<!-- Sidebar -->
<section id="sidebar">
<section>
<header>
<h3>Graphics</h3>
</header>
<a href="#" class="image featured"><img src="images/snowmass_graphics_banner.png" alt="" /></a>
<p>Experience with making graphics in PowerPoint and Adobe for applications in technical and social sciences.</p>
<!--
<ul class="actions">
<li><a href="#" class="button">Full achive of graphics</a></li>
</ul>
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</section>
<section>
<a href="#" class="image featured"><img src="images/pic02.jpeg" alt="" /></a>
<header>
<h3>Figures made for my Ph.D. dissertation</h3>
</header>
<p>To accompany the text in my thesis, I created a variety of flowcharts, infographics, cartoons, and other visualizations.</p>
<!--
<ul class="actions">
<li><a href="#" class="button">More about my thesis figures?</a></li>
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</section>
</section>
<!-- adding some padding here -->
<p> <br> </p>
</div>
</div>
<header> <h3>Graphics made for the Snowmass 2021 Community Survey</h3> </header>
<div class="row features">
<section class="col-4 col-12-narrower feature">
<div class="image-wrapper first">
<a href="#" class="image featured"><img src="images/snowmass1.png" alt="" /></a>
</div>
<header>
<h4>Overall Statistics</h4>
</header>
</section>
<section class="col-4 col-12-narrower feature">
<div class="image-wrapper">
<a href="#" class="image featured"><img src="images/snowmass2.png" alt="" /></a>
</div>
<header>
<h4>Career Statistics</h4>
</header>
<ul class="actions">
<li><a href="blog.html" class="button">More about the Snowmass graphics</a></li>
</ul>
</section>
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<div class="image-wrapper">
<a href="#" class="image featured"><img src="images/snowmass3.png" alt="" /></a>
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<h4>Workplace Statistics</h4>
</header>
</section>
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</div>
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