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<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.9.3">Jekyll</generator><link href="https://andrewpking.github.io/feed.xml" rel="self" type="application/atom+xml" /><link href="https://andrewpking.github.io/" rel="alternate" type="text/html" /><updated>2023-08-21T19:20:31+00:00</updated><id>https://andrewpking.github.io/feed.xml</id><title type="html">Drew King</title><subtitle>Data Science Portfolio</subtitle><author><name>Drew King</name><email>[email protected]</email></author><entry><title type="html">Allen School</title><link href="https://andrewpking.github.io/allen_school/" rel="alternate" type="text/html" title="Allen School" /><published>2022-12-06T00:00:00+00:00</published><updated>2022-12-06T00:00:00+00:00</updated><id>https://andrewpking.github.io/allen_school</id><content type="html" xml:base="https://andrewpking.github.io/allen_school/"><h2 id="allen-school-biography">Allen School Biography</h2>
<p>I’m a senior Computer Science student at the University of Washington with an expected graduation date of Fall 2024. Additionally I have been a teachers assistant for the Fall 2022 offering of <a href="https://courses.cs.washington.edu/courses/cse390z/22au/">CSE 390Z</a>. At the moment, I am a Student Relations Officer for <a href="https://ability.cs.washington.edu">Ability</a>. Studying and teaching Computer Science here has required me to embrace a growth mindset, learn meta-cognitive skills, and apply them when learning technical skills.</p>
<h3 id="meta-cognitive-skills-and-networking">Meta-cognitive Skills and Networking</h3>
<p>At UW, one meta-cognitive skill I have been applying is building connections with peers, staff, and grad students. In particular one grad student I met with is <a href="https://homes.cs.washington.edu/~jessejm/">Jesse Martinez</a>, a PhD student at UW and a contributor to the <a href="https://makeabilitylab.cs.washington.edu/member/jessemartinez/">Makeability Lab</a>. We met at <a href="https://ability.cs.washington.edu">Ability</a> Research Night and exchanged contact info. We scheduled a zoom meeting and we had a discussion about his contributions to the MakeAbility Lab at UW in creating accessible games for people to play. A sample from this meeting:</p>
<ul>
<li>Jesse uses the following methodology to determining accessibility needs for games.
<ul>
<li>Starting with the most simple case: he’s trying to play a game with a friend with disability, as the resident games person he’s trying to host Accessible Game Nights and get people to play a game. Usually choosing the game arbitrarily depending on what can be played accessibly vs what we want to play. He is finding ways to make the games he owns be more accessible to his friends. Some questions he asks are:
<ul>
<li>What are the different mechanics of the game?</li>
<li>What is not blind accessible?</li>
<li>What is not deaf accessible?</li>
<li>Thinking of tools to create vs easy fixes for games (carving letters into tiles for games)</li>
<li>Thinking of my friends specific needs</li>
</ul>
</li>
</ul>
</li>
<li>Jesse has done consulting work for Exploding Kittens including a play test.
<ul>
<li>He commented how difficult it is to get every disability perspective in a play test.</li>
<li>Some cognition of mechanics helps to break down the game and figure out what is necessary.</li>
<li>Choosing which accessibility barrier to look at.</li>
</ul>
</li>
<li>Jesse has done work with the MakeAbility lab add in accessibility options to Android educational games.
<ul>
<li>By looking at Android educational games and ability to access via accessibility switches (interact without touching the screen). Many mobile games don’t have any selectable targets so switches are useless for them.</li>
<li>This is accomplished using middleware to modify the game-play experience
<ul>
<li>This is built on top of software already built for non games to enhance touch targets to be selectable via an accessibility switch.</li>
</ul>
</li>
</ul>
</li>
</ul>
<h3 id="cornell-notes">Cornell Notes</h3>
<p>Another important skill to succeed at the Allen School is using <a href="https://lsc.cornell.edu/wp-content/uploads/2016/10/Cornell-NoteTaking-System.pdf">Cornell Notes</a>. What Cornell Notes do is heighten note taking to take advantage of <a href="https://cft.vanderbilt.edu/guides-sub-pages/blooms-taxonomy/">Bloom’s Taxonomy</a> by including questions and summaries into notes for a lecture. By taking notes in this manner, I have retained more information from lecture and been able to know when to apply the information appropriately on projects. An example of my Cornell notes on using loads in computer architecture are provided here.</p>
<p><img src="/images/cornell_notes.png" alt="Cornell Notes" />{ width=100px,height:400px }</p></content><author><name>Drew King</name><email>[email protected]</email></author><category term="education" /><category term="meta-cognition" /><category term="accessibility" /><summary type="html">Education, Meta-cognition, Accessibility</summary></entry><entry><title type="html">Solala App</title><link href="https://andrewpking.github.io/solala/" rel="alternate" type="text/html" title="Solala App" /><published>2022-07-22T00:00:00+00:00</published><updated>2022-07-22T00:00:00+00:00</updated><id>https://andrewpking.github.io/Solala</id><content type="html" xml:base="https://andrewpking.github.io/solala/"><h2 id="building-a-data-driven-mental-health-application-that-using-nodejs-on-azure">Building a data driven Mental Health Application that using Node.js on Azure</h2>
<h3 id="lee-janzen-christopher-roy-christoph-bendix-and-drew-king">Lee Janzen; Christopher Roy; Christoph Bendix; and Drew King</h3>
<h4 id="abstract">Abstract</h4>
<p>Current mental health apps fail in several manners:</p>
<ul>
<li>User retention, 80% of users uninstall after first month</li>
<li>Large number of participants = good</li>
<li>Attrition -&gt; challenging</li>
</ul>
<p>Goal is to build a Mental health app design that increases retention and has meaningful results:</p>
<ul>
<li>Target audience: users experiencing depression, anxiety, work/school stress</li>
<li>Designed using research on app retention</li>
<li>Can identify emotional state of user</li>
<li>Offers activities proven to reduce stress</li>
<li>Issue: varying needs of users
Potential solution: use machine learning algorithms to identify emotion states</li>
<li>Combine results across procedures</li>
<li>Allows enhanced flexibility</li>
<li>Issue: no formal guidelines for combining certain procedures
Goal: establish guidelines around ensemble procedures
Potential impact in mental health research and real world impact on app users.</li>
</ul>
<h5 id="methodology-of-this-research-will-be-available-upon-app-publication-see-more-details-at-solalaapp">Methodology of this research will be available upon app publication, see more details at <a href="https://solala.app/">solala.app</a></h5>
<h4 id="results">Results</h4>
<p>Solala is an app designed by students with the intention of improving the mental health of burned out professionals and students.</p>
<ul>
<li>Built with universal design and accessibility principles in mind</li>
<li>UX Design process considers the needs of colorblind individuals</li>
<li>Additionally the design process considers user retention through gamification
Users will care for a plant that grows with them</li>
<li>The plant is intended to motivate users to take care of themselves and complete growth tasks</li>
<li>Plant encourages users to drink water, meditate, and take breaks
This work is backed by research into wellness and mental health for individuals under high stress.</li>
</ul>
<h5 id="solala-repository"><a href="https://github.com/Solala-App/Solala">Solala Repository</a></h5>
<p><strong>Next steps:</strong>
Conduct further research from consenting users including:</p>
<ul>
<li>User retention for Solala based on reward intervals and task intervals</li>
<li>Testing other algorithms that process emotion state data.</li>
<li>Building this experiment in mobile native languages such as
Darwin or Swift for better performance.</li>
</ul></content><author><name>Drew King</name><email>[email protected]</email></author><category term="mental health app" /><category term="software development" /><category term="web development" /><category term="mobile development" /><summary type="html">Mental Health App, Software Development, Data Visualization, Web Development, Mobile Development</summary></entry><entry><title type="html">SuperLearner Screens</title><link href="https://andrewpking.github.io/sl_screens/" rel="alternate" type="text/html" title="SuperLearner Screens" /><published>2022-07-13T00:00:00+00:00</published><updated>2022-07-13T00:00:00+00:00</updated><id>https://andrewpking.github.io/sl_screens</id><content type="html" xml:base="https://andrewpking.github.io/sl_screens/"><h2 id="investigating-the-use-of-screens-in-super-learner-ensembles">Investigating the use of screens in Super Learner ensembles</h2>
<h3 id="andrew-king1-brian-williamson-phd2-and-ying-huang-phd2">Andrew King<sup>1</sup>; Brian Williamson, PhD<sup>2</sup>; and Ying Huang, PhD<sup>2</sup></h3>
<h3 id="1seattle-central-college--2fred-hutchinson-cancer-research-center"><sup>1</sup>Seattle Central College &amp; <sup>2</sup>Fred Hutchinson Cancer Research Center</h3>
<h4 id="abstract">Abstract</h4>
<p>Clinical research trials often generate “big data”</p>
<ul>
<li>Examples: vaccine and cancer early detection research</li>
<li>Large number of participants = good</li>
<li>Large number of variables -&gt; challenging
Machine learning increasingly used in biomedical research</li>
<li>Helpful with a large number of variables</li>
<li>Can identify complex relationships with outcome of interest</li>
<li>Issue: performance can vary across algorithms
Potential solution: use an ensemble of algorithms</li>
<li>Combine results across procedures</li>
<li>Allows enhanced flexibility</li>
<li>
<p>Issue: no formal guidelines for combining certain procedures
Goal: establish guidelines around ensemble procedures
Potential impact in vaccine, cancer research</p>
</li>
<li>Methodology of this research will be available upon peer review*</li>
</ul>
<h4 id="results">Results</h4>
<p>Lasso + SL not always a helpful combo; surprising result.</p>
<ul>
<li>SL with screens or SL with lasso and screens = safe choice</li>
<li>Lasso by itself = unreliable in most cases</li>
<li>Lasso + SL with no screens = unreliable in many cases</li>
</ul>
<h5 id="code-will-be-available-on-github-once-the-paper-is-published">Code will be available on GitHub once the paper is published</h5>
<ul>
<li>Open source under the MIT license</li>
</ul>
<p><strong>Next steps:</strong>
Train researchers to follow our guidelines + practices.
Conduct further research including:</p>
<ul>
<li>Building a study to address why the lasso alone is not ideal for variable selection</li>
<li>Testing other algorithms that process biomedical data.</li>
<li>Building this experiment in other programming language such as C or Python for better performance</li>
</ul>
<p>Following publication, results will be replicable by others with scientific computing tools.</p></content><author><name>Drew King</name><email>[email protected]</email></author><category term="machine learning" /><category term="data science" /><category term="data visualization" /><summary type="html">Machine Learning, Data Science, Data Visualization</summary></entry></feed>