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<span class=”d-block d-lg-none“>Youngseo Son</span>
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<h1 class=”mb-0“>Youngseo
<span class=”text-primary“>Son</span>
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<div class=”subheading mb-5“>New Computer Science 242, Stony Brook University
<a href=”#“>yson [at] cs [dot] stonybrook [dot] edu</a>
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<p class=”lead mb-5“> I am a PhD Candidate in Computer Science of Stony Brook University. <br />
I am very grateful for having <a href=’http://www3.cs.stonybrook.edu/~has/‘>H Andrew Schwartz</a> as my advisor. <br />
My key research focus is in the field of <b>Natural Language Processing (NLP)</b> for social media analysis, language modeling, information extraction and data analysis. I collaborate with psychologists and computational linguists for <b>Human-centered language modeling </b> to obtain higher accuracies of various NLP tasks from traditional tasks (e.g., sentiment analysis) to novel tasks such as discourse style analysis for psychological assessment and well-being measurement. I especially focus on <b>discourse relation parsing </b> to extract key information for targeted tasks such as <b><i> opinions or reasons for sentiment of reviews and a political stance</i></b>, and finding the correlations of discourse styles with human variables such as personality.</p>
<p> <a href=”YoungseoSon_CV.pdf“>[CV]</a> </p>
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<h2 class=”mb-5“>Experience</h2>
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<h3 class=”mb-0“>Data Scientist PhD Intern</h3>
<div class=”subheading mb-3“>Data Sciences and Analytics Group in Pacific Northwest National Laboratory</div>
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<li> I am very grateful for the experience working with <a href=”https://www.cs.jhu.edu/~svitlana/“>Svitlana Volkova</a> as my PI and <a href=”https://www3.nd.edu/~mglenski/“>Maria Glenski</a> as my mentor.</li>
<li> DARPA SocialSim Project </li>
<li> Developed SocialSim modules to analyze information / graph evolution and cross-platform misinformation / disinformation spreads on social media (e.g., Twitter, Reddit, Github) </li>
<li> Collaborated with <a href=”https://prashas.github.io/“>Prasha Shrestha</a> for detecting coordinated efforts and analyzing trends and spread mechanisms of cryptocurrencies over social media </li>
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<div class=”resume-date text-md-right“>
<span class=”text-primary“>Summer 2019</span>
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<h3 class=”mb-0“>Research Scientist Intern</h3>
<div class=”subheading mb-3“>World Well-Being Project (WWBP), the University of Pennsylvania</div>
<ul>
<li> Developed a discourse relation parser for social media to capture counterfactual thinking from tweets. </li>
<li> The NLP pipeline of the joint model of the rule-based model (regex with Tweet Brown Clusters) and the statistical model (Linear SVM with discourse unit extraction) </li>
<li> Led the project with the help of <a href=”http://www.cis.upenn.edu/~ungar/“>Prof. Lyle Ungar</a> and Anneke Buffone of the <a href=”http://www.wwbp.org/“>WWBP </a> team. </li>
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<span class=”text-primary“>Summer 2016</span>
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<h3 class=”mb-0“>Teaching Assistant</h3>
<div class=”subheading mb-3“>Stony Brook University</div>
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<li>Grduate Courses: Big Data Analytics (Fall 2016, Fall 2017)</li>
<li>Undergrduate Courses: Senior Software Engineering, Computer Science III, Advanced Game Programming, and Computer Music</li>
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<span class=”text-primary“>Spring 2014 – Fall 2017</span>
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<h3 class=”mb-0“>Software Engineer Intern</h3>
<div class=”subheading mb-3“>Dassault Systemes</div>
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<li>Development of the Product Lifecycle Management (PLM) Web Application (ENOVIA)</li>
<li>Worked on updating PLM chart display and data visualization functions with PLM Development senior software engineers and pre sales team members</li>
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<span class=”text-primary“>December 2012 – February 2013</span>
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<h3 class=”mb-0“>Information Technology Specialist (25B)</h3>
<div class=”subheading mb-3“>the ROK Army & the US Army</div>
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<li>Worked with 2nd Infantry Division 8th Army of the United States</li>
<li>Managed networks, computers, peripheral devices, and the online portal of the battalion</li>
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<span class=”text-primary“>August 2010 – May 2012</span>
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<section class=”resume-section p-3 p-lg-5 d-flex flex-columnid=”news“>
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<h2 class=”mb-5“>News</h2>
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<h3 class=”mb-0“><b>Invited Talks at INFORMS 2019</b></h3>
<div class=”subheading mb-3“>Two Session Talks for NLP Applications for Decision Support and Social Media Mining</div>
<ul>
<li>Presenting <a href=”https://arxiv.org/pdf/1809.01202.pdf“>our causal explanation analysis research</a> at <b>Machine Reading and Comprehension for Science-Practice Knowledge Synthesis</b> session</li>
<li>Presenting <a href=”https://aclweb.org/anthology/D17-1119“>our human-centered NLP social media mining technique</a> at <b>Social Media Mining: Techniques and Applications</b> session </li>
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<div class=”resume-date text-md-right“>
<span class=”text-primary“><b>OCT 20-23, 2019</b></span>
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<section class=”resume-section p-3 p-lg-5 d-flex flex-columnid=”publication“>
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<h2 class=”mb-5“>Selected Publication</h2>
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<h3 class=”mb-0“><a href=”https://www.aclweb.org/anthology/W19-3005“><b>Suicide Risk Assessment with Multi-level Dual-Context Language and BERT</b></a></h3>
<div class=”subheading mb-3“>Matthew Matero, Akash Idnani, <b>Youngseo Son</b>, Salvatore Giorgi, Huy Vu, Mohammadzaman Zamani, Parth Limbachiya, Sharath Chandra Guntuku, H. Andrew Schwartz</div>
<p> Ranked No 1. for predicting reddit users’ suicide risk level using their SuicideWatch and Non-SuicideWatch posts (Task B). Developed user-factor-adapted RNN models with post-level attention using BERT and psychology language model representations of reddit posts</p>
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<span class=”text-primary“><b>NAACL 2019 CLPsych</b></span>
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<h3 class=”mb-0“><a href=”https://psyarxiv.com/8fpjn/“><b>The Language of Well-Being: Tracking Fluctuations in Emotion Experience through Everyday Speech</b></a></h3>
<div class=”subheading mb-3“>Jessie Sun, H.Andrew Schwartz, <b>Youngseo Son</b>, Margaret Kern, Simine Vazire</div>
<p> LDA Topic modeling to capture momentary emotions from language (validated by the replication in the second year). Exploration over Linguistic Inquiry and Word Count (LIWC) categories and open-vocabulary models for the correlation analysis between language and momentary emotion. </p>
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<div class=”resume-date text-md-right“>
<span class=”text-primary“><b>Journal of Personality and Social Psychology</b></span>
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<h3 class=”mb-0“><a href=”https://arxiv.org/pdf/1809.01202.pdf“><b>Causal Explanation Anlaysis on Social Media</b></a></h3>
<div class=”subheading mb-3“><b>Youngseo Son</b>, Nipun Bayas, H. Andrew Schwartz</div>
<p> The NLP pipeline of the joint model of the causality classifier (Linear SVM) and the causal explanation identifier (Bidirectional LSTM). The application of the pipeline to downstream tasks (Facebook Demographic Analysis and Yelp Review Sentiment Cause Detection) </p>
<p><a href=”data/EMNLP_Anonymized_Causality_Dataset.zip“>[Data]</a> <a href=”https://github.com/yson375/EMNLP_2018_Causal_Explanation_Analysis“>[Code]</a> <a href=”data/EMNLP_2018_Pretrained_Models.zip“>[Pretrained Models]</a></p>
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<div class=”resume-date text-md-right“>
<span class=”text-primary“><b>EMNLP 2018</b></span>
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<h3 class=”mb-0“><a href=”http://aclweb.org/anthology/D17-1119“><b>Human Centered NLP with User-Factor Adaptation</b></a></h3>
<div class=”subheading mb-3“>Veronica E. Lynn, <b>Youngseo Son</b>, Vivek Kulkarni, Niranjan Balasubramanian, H. Andrew Schwartz</div>
<p> Feature Adapatation of NLP models using human variables (age, gender, and personality) for downstream tasks (POS Tagging, PP-Attachment, Sentiment, Sarcasm, Stance) </p>
<p><a href=”https://github.com/StonyBrookNLP/user-factor-adaptation“>[Code]</a>
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<span class=”text-primary“><b>EMNLP 2017</b></span>
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<h3 class=”mb-0“><a href=”http://www.aclweb.org/anthology/P17-2103v2“><b>Recognizing Counterfactual Thinking in Social Media Texts</b></a></h3>
<div class=”subheading mb-3“><b>Youngseo Son</b>, Anneke Buffone, Anthony Janocko, Allegra Larche, Joseph Raso, Kevin Zembroski, H Andrew Schwartz, Lyle Ungar</div>
<p> The NLP pipeline of the joint model of the rule-based model (regular expression capable of capturing social-media-specific variations of discourse connectives with Tweet Brown Clusters) and the statistical model (Linear SVM) </p>
<p><a href=”data/ACL2017_Counterfactual_Dataset.zip“>[Data]</a>
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<span class=”text-primary“><b>ACL 2017</b></span>
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<section class=”resume-section p-3 p-lg-5 d-flex flex-columnid=”education“>
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<h2 class=”mb-5“>Education</h2>
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<h3 class=”mb-0“>Stony Brook University</h3>
<div class=”subheading mb-3“>Doctor of Philosophy</div>
<div>Computer Science</div>
<p>GPA: 3.89/4.00</p>
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<div class=”resume-date text-md-right“>
<span class=”text-primary“>August 2015 – Present</span>
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<h3 class=”mb-0“>Stony Brook University</h3>
<div class=”subheading mb-3“>Bachelor of Science</div>
<div><b>Summa Cum Laude</b></div>
<div><b>Departmental Honors</b></div>
<div>Computer Science</div>
<p>GPA: 3.93/4.00</p>
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<div class=”resume-date text-md-right“>
<span class=”text-primary“>August 2013 – May 2015</span>
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<h3 class=”mb-0“>Ajou University</h3>
<div class=”subheading mb-3“>Bachelor of Engineering</div>
<div>Computer Engineering</div>
<p>GPA: 4.36/4.50</p>
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<div class=”resume-date text-md-right“>
<span class=”text-primary“>March 2009 – August 2013</span>
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<section class=”resume-section p-3 p-lg-5 d-flex flex-columnid=”projects“>
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<h2 class=”mb-5“>Recent Projects</h2>
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<h3 class=”mb-0“>9/11 World Trade Center Project</h3>
<div class=”subheading mb-3“>Project in collaboration with Stony Brook WTC Wellness Program and Stony Brook Medicine</div>
<ul>
<li> Analyzing interviews of people who were at the scene of 9/11 WTC Attack. </li>
<li> Correlating linguistic features of the subjects with their mental/physical health. </li>
<li> Using LDA topic clustering, discourse relation parsing, sentiment/emotion lexicons. </li>
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<div class=”resume-date text-md-right“>
<span class=”text-primary“>August 2017 – Present</span>
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<h3 class=”mb-0“>The Language of Well-Being Project</h3>
<div class=”subheading mb-3“>Project in collaboration with the University of California, Davis and the University of Melbourne</div>
<ul>
<li> Correlating linguistic features of people’s everyday language with the changes of their emotions. </li>
<li> Conducting LDA topic clustering over the transcripts of the participants’ daily speech for the emotion analysis. </li>
<li> Using N-gram, Linguistic Inquiry and Word Count (LIWC), sentiment/emotion lexicons. </li>
</ul>
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<div class=”resume-date text-md-right“>
<span class=”text-primary“>July 2017 – Present</span>
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<section class=”resume-section p-3 p-lg-5 d-flex flex-columnid=”awards“>
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<h2 class=”mb-5“>Awards &amp; Scholarship</h2>
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Special CS Department Chair Fellowship – August 2015</li>
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Stony Brook University Computer Science Award of Honor – May 2015</li>
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Stony Brook University URECA Stipend – Summer 2014</li>
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Stony Brook University Outstanding Academic Achievement Awards – Fall 2013, Spring 2014</li>
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Stony Brook University Dean’s List Nomination – Fall 2013, Spring 2014</li>
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Ajou University Superior Academic Performance Scholarship – [Fall 2009 – Spring 2013]</li>
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US Army Best Korean Augmentation to the United States Army (KATUSA) of 2012 – May 2012</li>
<li>
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US Army Best Warrior 2012 – April 2012</li>
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