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I am a 5th year Economics PhD student and Computer Science MS student at Washington State University. I have personally designed this website (most of it is not based on a template), so enjoy! Use the sidebar to explore my site! 

  
    > Joe.interests
    ["econometrics", "machine learning", "programming", 
     "data visualization", "soccer", "esports"]

    > Joe.skills
    ["python", "javascript (d3.js)", "spark",
     "R", "stata", "git", "LaTeX", "SQL"]

    > Joe.contact_me
    {
      "email": "joseph.patten@wsu.edu",
      "github": "joepatten",
      "linkedin": "joepatten"
    }

    > Joe.about_me
    "joepatten.com/about"

    > ▮

I have been working on visualizing data in d3.js. The following link contains a visualization showing how rankings in the FBS have changed over the weeks.

Projects

Below are some selected projects that I have recently worked on in an academic setting or in my free time. I am currently working on a few of projects that are not listed below, which include: using collaborative filtering (using big data frameworks and techniques) on Amazon data to analyze co-purchasing trends, using d3.js to make a "graph theorist's sketchpad", and analyzing the effect of competitive balance on match viewership in Esports.

Community College Spending on For-Profit Enrollment

I estimate the causal impact of spending by community colleges on nearby for-profit colleges by exploiting variation of budget shocks and tuition freezes/caps by state legislatures.
Causal Inference, Two-Stage Least Squares

Impact of Drought on Herd Composition

Using autoregressive models, I estimate the causal impact of sustained drought on herd composition.
Causal Inference, Autoregressive

Esports Predictions Using Monte Carlo Simulation

Using a combination of Markov Chain and tree-based ensemble methods, I forecast the outcomes of esports matches and tournaments.
Machine Learning, Monte Carlo, Markov Chain

Theory of Corporate Whistleblowing

I develop a theoretical model to analyze the incentives of an employee to whistleblow on an employer.
Theory

Active Learning to Label Data

I show the benefit of using least confident sampling and margin sampling instead of random sampling to label data in order to more efficiently label data.
Machine Learning, Active Learning

Effect of S&P 500 Inclusion on Stock Price

Using random forests, we match stocks that barely make it into the S&P 500 with those that don't. We then use difference-in-differences to estimate the effect of S&P 500 inclusion on stock price.
Machine Learning, Difference-in-Differences

Affirmative Action and Human Capital Investment

We design an experiment to mimic Affirmative Action (AA). We then estimate the effects of AA on human capital investment. Featured on FiveThirtyEight.
Experiment


What Else I'm Doing


PhD Math Bootcamp

Economics

Rocket League Esports (RLCS) Simulated Predictions

Sports Analytics

My Experience with the Google Foobar Challenge

Computer Science

NABE Tec Conference 2019 - Seattle

Economics

Machine Learning Exploration

Machine Learning

Fun with Binary Decision Diagrams (BDDs)

Computer Science