About Me

I am passionate researcher specializing in economics, finance, and data science. I hold a B.S. in Economics from the University of Oregon. And I am currently pursuing an M.S.E. in Data Science from UPenn, specializing in AI, Machine Learning, and Big Data

As a Research Analyst at The Wharton School, I develop finance algorithms, conduct data analysis on big data, and optimize models using languages such as R, Python, and SQL.

Through my previous internships at MIT and Princeton, I have gained expertise in text mining, NLP, and Causal Inference models for research. With my strong skill set and dedication, I am poised to make meaningful contributions to the field of Economics, Household Finance, and Machine Learning.

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Selected Projects

Big Data Analytics Project, Penn Engineering

Predicted Heart Disease Mortality by Zip Code using Logistic Regression and Random Forests; with the aid of PCA and Regularization methods.

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Summarization for Classification, Penn Engineering

Investigated whether classification algorithms suffer a decrease in performance when classifying text summaries as opposed to the original text.

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Encoder-Decoder w/ Attention, Penn Engineering

Implemented a Seq2Seq Model for Semantic Parsing using a Long Short-Term Memory (with and without Attention).

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Part of Speech Tagger & HMM, Penn Engineering

Incorporated Part-of-Speech Tagging using Hidden Markov and Maximum Entropy Models; e.g., Beam-k Search, Viterbi, and Greedy Classifiers.

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Complex-Word Prediction, Penn Engineering

Used Adaboost, Naive Bayes, Logistic Regression, and Simple Baseline Classifiers to identify and categorize words that are "complex."

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Natural Language Processing, M.I.T. Summer Research

Used RegEx, Cloud Computing, OCR, NLP techniques to scan and webscrape over 120,000 PDF documents.

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Junior Summer Institute, Princeton University

Learned and worked with applied IVs, Regression Discontinuities, Diff-in-Diff, KNN, K-Means, and Machine Learning in a public policy context.

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Predicting SPAM Emails w/ ML, University of Oregon

Used five classification methods to identify spam emails from non-spam through word frequency.

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Text Mining & Webscraping, University of Oregon

Webscraped, cleaned, and analyzed data from a Wikipedia page using rvest, RegEx, and ggplot2.

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