I am a data scientist with a passion for rigorous statistics, effective data visualization, and clean, readable code.

At Oscar Health, I build machine learning models to systematically detect and diagnose excess medical spending. As a Masters student at Stanford, I worked on supervised learning projects in bioinformatics, natural language processing, and causal inference. Previously, at Cornerstone Research, I used tools like R, Python, SAS, and SQL for data-intensive analyses in antitrust, patent infringement, and market manipulation cases.

Aside from data science, I am passionate about teaching; at U. Penn, I was an economics teaching assistant in the brick-and-mortar classroom and also in an online flipped classroom and a Coursera MOOC. At Cornerstone, I developed training material and mentored analysts in best practices for coding and working with data.

When I’m not at work, you can find me baking pies, glass blowing, or swing dancing.

Skills

Programming

I write elegant, efficient, and readable code in Python, R, SQL, C++, and more. I work collaboratively to build testable, well-documented code bases.

Data Wrangling

I draw on my four years of experience in consulting to untangle messy data and suss out anomalies.

Statistics

I leverage my masters education in Statistics to identify the right techniques for each setting.

Data Engineering

I construct robust data pipelines to parallelize the processing of large datasets.

Communication

I communicate technical content to non-technical audiences to make data science clear and approachable.

Teaching

I have 8+ years of experience teaching in both academic and corporate settings, and love every minute of it.

Projects

Argument Visualization

Causal Inference

My team conducted a reanalysis of the data from a published study using causal inference techniques to account for the lack of randomization in the experimental design.

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Second Order LSTMs

Natural Language Processing

We test the hypothesis that second-order LSTM architectures have the improved ability (relative to a simple LSTM) to learn long distance dependencies within an input.

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Dance Classification

Video Action Recognition

We assess the ability of sequential neural networks to capture dynamic movement and predict dance styles from video.

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