Resume
PDF version of my resume here
Education
- M.S. in Data Science, New York University, 2020
- B.S. in Statistics, University of California - Los Angeles, 2018
- B.A. in Economics, University of California - Los Angeles, 2018
Technical Skills
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| Programming Languages | Python, R, SQL, Stata, Tableau, LaTeX, Microsoft Excel |
| Big Data Tools | SQL, Hadoop, Spark, Beam, MapReduce, Tensorflow Extended, AWS SageMaker, S3, EC2, Git |
| Machine Learning | Natural Language Processing, Deep Learning, Computer Vision, Regression, Classification, Feature Engineering, Clustering |
| Statistical Methods | Data Mining, Linear Models, Regression, Hypothesis Testing and Confidence Intervals, Principal Component Analysis and Dimensionality Reduction |
| Design and typesetting | LaTeX, Markdown, WordPress, Adobe InDesign |
Relevant Coursework
- Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Data Engineering, Pattern Recognition, Probability, Analysis, Computational Statistics, Cognitive Modeling, Macro & Microeconomic Theory, Econometrics, Game Theory, Firm Organization, Experimental Design, Behavioral Economics
Work Experience
- Amazon, Applied Scientist Intern – Summer 2019
- Developed a natural language processing knowledge extraction product that allows users to obtain an easily digestible summary of hundreds of thousands of associate and informational documents, providing key topics, named entities, sentiments and question answering.
- Amenity Analytics, Data Scientist – Oct. 2018 - April 2019
- Worked within Text.AI team to develop natural language processing and machine learning models using company’s proprietary software on unstructured financial text documents to extract key insights and deliverables.
- Spot.IM, Data Scientist Intern – Summer 2017
- Built and designed a churn rate algorithm to instantaneously detect when customers would disable the product, allowing the business operations team to reach out and bring back customers that churned.
Selected Projects
- NYU Capstone: Fashion Market Segmentation and Analysis with Trendalytics
- Efficiently gain an understanding of retailer behaviors and establish a methodology to allow for market segmentation. Market segmentation can be defined as a way to classify retailers. To summarize the outcome of our feature engineering and clustering, we provide a Score Card that succinctly captures all trend behavior labels and provides a comparison across retailers and competitors.
- Deep Learning/Computer Vision: Predicting Object Bounding Boxes and Road Map Layouts in a Traffic Environment
- Given 6, 360 degree real-time traffic environment images, we apply a pre-processing step of stitching together the images into a single representation, then apply the YOLOv3 architecture for the task of object detection of nearby cars, trucks, and other vehicles. Predicting roadmap layouts and overlaying bounding boxes on objects.
- Data Science: Text Classification with SMS Messages
- Analyzed SMS text messages to classify them as ‘spam’ or ‘ham’. As online communication has adapted and shifted from email to various forms of direct messaging, phishers have adjusted where and how they target individuals with spam. Users want to know that their accounts and data are secure, and they do not have time to be bothered by receiving spam messages.
- Big Data: Music Recommendation System
- Developed recommender model using alternating least squares (ALS) for implicit feedback in PySpark on Last.fm dataset. Experimented with different configurations of count data.
- Computer Vision: Least Square GANs
- Least squares generative adverserial networks (LSGANs) for image generation. Typical GAN’s utilize a sigmoid loss function, which can often lead to mode collapse and the failure of the model to produce authentic images. Evaluate the success of this improved model by training over the Fashion-MNIST dataset.
- Dashboard: COVID-19 Analysis
- Amidst the COVID-19 pandemic, we found common interest in applying advanced python methods to build a visualization dashboard that investigates its effects. Built a reporting dashboard to display the effects of COVID-19 globally.
