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% Based on `template.tex' by Xavier Danaux (xdanaux@gmail.com) as part of the
% moderncv LaTeX package.
\documentclass[11pt,letterpaper]{moderncv}
\moderncvstyle{classic} % style options are 'casual' (default), 'classic', 'oldstyle' and 'banking'
\moderncvicons{awesome}
\moderncvcolor{green} % color options 'blue' (default), 'orange', 'green', 'red', 'purple', 'grey' and 'black'
\nopagenumbers{}
% metadata
\AfterPreamble{\usepackage{hyperref}
\hypersetup
{
pdfauthor={Laurel Kona Goodhart},
pdfsubject={Resume},
pdftitle={Laurel Kona Goodhart's Resume},
pdfkeywords={resume, cv, natural language processing, machine learning, nlp, ml}
}}
% character encoding
\usepackage[utf8]{inputenc}
% style adjustments
\usepackage{droidsans}
\renewcommand*{\namestyle}[1]{{\namefont\usefont{T1}{fdr}{bx}{ui}\textcolor{color1}{#1}}}
\renewcommand*{\titlestyle}[1]{{\titlefont\usefont{T1}{fdr}{bx}{ui}\Large\textcolor{color2}{#1}}}
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% adjust the page margins
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\usepackage[margin=13mm]{geometry}
%----------------------------------------------------------------------------------
% personal data
%----------------------------------------------------------------------------------
\name{Laurel}{Goodhart\vspace*{-4mm}} %sorry this part is a bit hacky
\title{\hspace{.2in}{Software Engineer \& Computational Linguist}}
% could also put phone in
\email{hi@konahart.com}
\homepage{konahart.com}
\social[linkedin]{konahart}
\social[github]{konahart}
%----------------------------------------------------------------------------------
% content
%----------------------------------------------------------------------------------
\begin{document}
%----- resume ---------------------------------------------------------
\hspace{.25in}\begin{minipage}[b]{.95\textwidth}
\makecvtitle
\end{minipage}
\vspace*{-13mm}
% Experience
\section{Experience}
\cventry{2018}{Natural Language Processing Specialist}{Woebot Labs, Inc.}{San Francisco, CA}{}{
\begin{itemize}
\item Improved Natural Language Understanding in the Woebot mental health chatbot
\begin{itemize}
\item Created data collection and annotation strategy
\item Trained, evaluated, and deployed language models for classification
\end{itemize}
\item Applied expertise in chatbot conversational design to existing product
\begin{itemize}
\item Identified common pitfalls and suggested and implemented solutions
\item Conducted analysis of how natural language understanding issues affect retention during app onboarding
\end{itemize}
\item Assisted in creation of new chatbot authoring environment for use by non-technical users
\end{itemize}}
\cventry{2015---2017}{Artificial Intelligence Engineer}{PullString, Inc. (formerly ToyTalk, Inc.)}{San Francisco, CA}{}{
\begin{itemize}
\item Worked on \textit{PullString Author}, a software platform for authoring human-fidelity AI conversation
\item Designed, implemented, and improved Natural Language Processing features
\begin{itemize}
\item Features included negation detection, number conversion, user and author input normalization
\end{itemize}
\item Established large-scale testing to support data-driven decision-making
\item Collaborated with technical and non-technical peers on product features
\begin{itemize}
\item Expressive word/pattern matching syntax for use by non-technical users
\item Natural Language Processing and Machine Learning -based synonym and phrase suggestion
\end{itemize}
\end{itemize}}
\cventry{2014}{Research Assistant}{University of Washington - CSE}{Seattle, WA}{}{
\begin{itemize}
\item Created a Java training data pipeline for multi-linguistic relation extraction system, MultiR
\item Worked with large corpora:
\begin{itemize}
\item Gigaword English, Spanish, and Chinese corpora: over 9 million documents
\item Freebase: currently over 2 billion facts
\end{itemize}
\item Utilized Stanford's CoreNLP package for tokenization, Named Entity Recognition, parsing, etc.
\end{itemize}}
% Education
\section{Education}
\cventry{2018}{Deep Learning Specialization}{deeplearning.ai}{Coursera}{}{
\begin{itemize}
\item Self-motivated continuing education
\item 5 courses: Neural Networks and Deep Learning;
Improving Deep Neural Networks; %: Hyperparameter tuning, Regularization and Optimization;
Structuring Machine Learning Projects;
Convolutional Neural Networks;
Sequence Models
\end{itemize}}
\cventry{2013---2014}{Professional Master of Science in Computational Linguistics}{\hfill \newline University of Washington}{Seattle, WA}{}{
\begin{itemize}
\item Completed intensive, graduate-level program in 1 year
\item Focused on Natural Language Processing (NLP), Machine Learning (ML), and language modeling
\begin{itemize}
\item Specific topics included Machine Translation, Information Retrieval, and Question Answering
\end{itemize}
\end{itemize}}
\cventry{2009---2013}{Bachelor of Arts in Applied Linguistics}{Portland State University}{Portland, OR}{}{
\begin{itemize}
\item Minor in Computer Science
\item Honors College, undergraduate thesis: ``The Linguistics of Sentiment Analysis''
\end{itemize}}
% Technical Skills
\section{Technical skills \& selected projects}
\cvitem{\textbf{ML}}{kNN, Decision Trees, Na\"{i}ve Bayes, MaxEnt, and SVM (Python)}
\cvitem{\textbf{NLP}}{HMM-based Part-of-Speech tagger, Word Sense Disambiguation system, CKY parser, tokenization, Context Free Grammars incorporating features and semantic representations}
\cvitem{\textbf{Languages}}{Python, C++, Java}
\cvitem{\textbf{Tools}}{Jupyter, FastText, TensorFlow, NLTK, spaCy, Stanford CoreNLP, Git, Mercurial, Bash, \LaTeX }
% Publications
\section{Publications}
\cvitem{2013}{Hart, Laurel ``\textbf{The Linguistics of Sentiment Analysis}'' Portland State University \textit{PDX Scholar}. 2013: \protect\httplink[http://pdxscholar.library.pdx.edu/honorstheses/20]{http://pdxscholar.library.pdx.edu/honorstheses/20}}
%\cvitem{2012}{Hart, Laurel, Hassan Alam and Aman Kumar, ``\textbf{Revisiting Arabic Semantic Role Labeling using SVM Kernel Methods}'' \textit{Proceedings of COLING 2012}. December, 2012: 207—14. \protect\httplink[http://aclweb.org/anthology//C/C12/C12-3026.pdf]{aclweb.org/anthology//C/C12/C12-3026.pdf}}
\clearpage
\end{document}
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