Resume

You can get a PDF version of my resume here.

Education

• New York University – May 2020
  • Master of Science in Data Science (GPA: 3.96/4.00)
  • Selected coursework: Deep Learning, Natural Language Processing, Probabilistic Time Series Modeling, Natural Language Understanding and Computational Semantics, Text as Data, Responsible Data Science, Machine Learning, Big Data, Optimization and Computational Linear Algebra
• University of California, Los Angeles – June 2018
  • Bachelor of Science in Mathematics of Computation with Minor in Neuroscience
  • Selected coursework: Information and Power, American Television History, Algorithms in Bioinformatics, Mathematical Modeling

Skills and qualifications

Selected projects

• Capstone: Applying a General Language Model to Medical Text – Applied multilabel text classification strategies to dataset of 6M+ NYU Langone Health medical notes using deep natural language representation models in PyTorch. We experimented with benefits of fine-tuning in a transfer learning context for transformer-based models such as BERT and XLNet.
• Data Science: Text Classification with News Articles – Compared sentiment analysis methods, including Naive Bayes, bag-of-words logistic regression, BOW support vector machines (SVM), and neural networks, for dataset of articles about technology companies from The New York Times.
• Big Data: Recommender system with PySpark – Developed recommendation model using alternating least squares (ALS) for implicit feedback in PySpark on Last.fm dataset. We experimented with cold start problem using a latent feature regression model for unknown items.
• Natural Language Understanding: Building a Semantic Parser to Handle Queries about Song Data – Built a semantic parser using the SEMPRE framework which can answer queries about a subset of the Million Song Dataset.
• Numerical Linear Algebra: Implementing an Efficient Matrix Completion Algorithm in Python – Implemented SoftImpute-ALS algorithm in Python based on the original paper with testing on MovieLens-100K for recommendations. We compared RMSE results with those obtained by block coordinate descent.

Work and research experience

• Jan. 2019 - Dec. 2019: Data scientist intern at NBCUniversal Media
  • Statistical testing and analysis
    • Conducted statistical testing to measure differences in viewer per viewing household age group distributions over time, under both independence and autocorrelation assumptions
    • Developed Retool application to visualize statistical trends and simulate reallocation across the distribution, with presentation to Corporate Decision Sciences senior leadership
  • Data engineering and transformation
    • Prototyped PySpark methods to aggregate and transform Nielsen base data with 1M+ rows for modeling
    • Methods were adopted and put into production pipeline by data engineering team
  • Developing forecasting models
    • Fine-tuned linear regression models for forecasting NBC viewer demographic distributions on a single-show level
    • Produced 18-month forecasts using Python machine learning methods (ARIMA, linear regression, random forest) for shortform digital content including YouTube, Hulu, etc.
  • Evaluating forecasting models
    • Developed processes using MLFlow and R to save 800+ forecast models, evaluate based on MAPE, APE, visualization, anomaly detection, etc., and update MySQL tables for the models
    • Designed interactive Retool interface for senior data scientists to track model performance, which I folded into the production process
• Sept. 2017 - Aug. 2018: Undergraduate research assistant studying data on homelessness in Los Angeles with UCLA Mathematics Department
  • Extracted and cleaned data from Google Maps Geocoding API and public datasets (American Community Survey, LA city data from DataLA)
  • Visualized and analyzed Los Angeles city census tracts with principal component analysis, nonnegative matrix factorization, geographic mapping, and correlation analysis
  • Implemented and evaluated machine learning models (linear regression, logistic regression) for predicting changes in homeless populations in Python and Tensorflow
  • Experimented with simulations and single-layer artificial neural networks in MATLAB and Tensorflow
  • Communicated results to UCLA faculty and students through multiple presentations and reports
• Sept. 2017 - Nov. 2017: Customer data analytics intern at GuidedChoice
  • Produced insightful visualizations and demographic breakdowns of state of Florida account for retirement planning financial services firm
• June 2015 - Dec. 2017: Research assistant for Swartz Center for Computational Neuroscience at University of California, San Diego
  • Participated in design and proposal of experiment on how competition modulates neural correlates of insight
  • Coded Python scripts to collect subject response times and psychological survey data
  • Developed MATLAB code for preprocessing subject electroencephalogram (EEG) data using EEGLAB software and independent component analysis
  • Supported grant applications with detailed plots and statistical trend information

Journalism and leadership experience

• June 2016 - June 2017: Copy chief at The Daily Bruin
  • Led weekly meetings with eight slot editors and monthly training sessions with 30 contributors
  • Responsible for ensuring all content for The Daily Bruin (~9,000 copies/day circulation) and DailyBruin.com (~ 400,000 views/month) is edited for clarity, accuracy, and style, and has concise and engaging headlines
  • Developed detailed copy-editing schedules for nearly 150 days of production, breaking, and longform journalistic content
  • Contributed to comprehensive Daily Bruin style guide for grammar, style, and sensitivity
  • Maintained all the same duties as a slot editor
• March 2015 - June 2016: Slot editor at The Daily Bruin
  • Edited all content for one section of the paper two days per week
  • Participated in regular meetings on style and terminology related to racial, gender, and cultural sensitivity