Nora Tien is a computer science student at Haverford College with experience in data science, full stack development, and machine learning. She has worked on projects involving natural language processing, data mining, and visualization. Currently she is interning at Audible where she is performing text analysis and sentiment classification on audiobooks. Previously she interned at MITRE developing a recommendation tool using naive Bayesian modeling.

1. LinkedIn: http://linkedin.com/pub/nora-tien/a4/b36/a4a/ GitHub: https://github.com/ntien
NORA TIEN
(614) 209-0302 | ntien@haverford.edu
EDUCATION
Bachelor of Computer Science, Haverford College, Haverford, PA Expected May 2017
GPA: 3.36, Relevant Coursework: Probability & Statistics, Data Mining, Compilers, PL, Computer
Architecture, Mobile Development
SKILLS
Programming Languages: Python, C++, Javascript, D3.js, JQuery, Java for Android,
HTML/CSS
NLP: Tokenization, Lemmatization, Topic Modeling, Sentiment Analysis, Text Similarity
Software: Django, Android Studio, ArcGIS, SQL, MySQL, Redshift, AWS, EC2
OS, Tools: Linux, Windows, Mac, Vim, Eclipse, Bash Shell, Git (github, bitbucket)
Languages: Fluent Spanish, Intermediate Chinese, Basic French, Basic Arabic
RELEVANT EXPERIENCE
Audible Inc. Data Science Research Assistant Intern (Newark, NJ: May 2016 – present)
 Classified German audiobooks by genre using Latent Dirichlet Allocation and K-Means clustering
 Performed text pre-processing including tokenization, stopping, and lemmatization
 Automated selection of captivating book samples using supervised approach involving sentiment
analysis and n-gram similarity
 Analyzed similarity of customer genre preferences across long form content and short form content
MITRE Full Stack Software Developer Intern (Mclean, VA: May 2015 - December 2015)
 Built a JavaScript and JQuery recommendation tool to support novice healthcare fraud analysts,
using efficient matrix operations (operating as naïve Bayesian model) to rank recs based on form
responses
 Implemented this rec tool without a database, as required by security constraints
 Abstracted code for easy customization for fields other than healthcare fraud
 Researched visualizations and software frameworks (Ember.js, AngularJS) for future development
Dark Reaction Project Full Stack Developer (Haverford, PA; June 2014 - present)
 Created interactive force-graph visualization using D3.js
 Predicted successful chemical reactions using KNN clustering
 Optimized code for speed and integrated vis into Django pipeline
 Wrote validation tests for database operations
 Debugged and maintained website and codebase
PROJECTS AND AWARDS
 Richard Nathan 1977 Endowed Scholarship Fund, Haverford College (2013-2016)
 Village Android Application (available on github https://github.com/cfalk/EnvironmentalApp
 Salutatorian of IES Historiador Chabas, Denia, Alicante, España (2010-2011), all instruction in
Spanish