Explore My work 💻

A collection of some of my thoughts and projects over the years. Take everything with a grain of salt.

Include

Machine Learning logo
Machine Learning
Causal Inference logo
Causal Inference
Politics logo
Politics
Graph Theory logo
Graph Theory
NLP logo
NLP
Reinforcement Learning logo
Reinforcement Learning

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My Next Phase

Life update: I’m joining OpenAI as a Trust & Safety Analyst! Machine Learning logo

Machine Learning

Aug. 10, 2022

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Simulating Statistical Power in R

A tutorial (with code!) explaining the important concept underpinning the design of vaccine trials, the validity of A/B tests, and driving the “reproducibility crisis” in the social sciences. Causal Inference logo

Causal Inference

Mar. 24, 2022

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Break Them up? The Case for Interoperability Among Direct Messaging Platforms

Despite the dire need for regulation, direct messaging is the overlooked middle child of social media. While structural break ups of technology platforms is en vogue, it is unlikely to have meaningful impacts in this market, relative to interoperability among messaging platforms. Politics logo

Politics

Feb. 24, 2022

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How COVID-19 has Changed our Music Listening Habits

COVID-19 has brought enormous amounts of anxiety and uncertainty. This article shows how the pandemic has affected popular music listening habits. Machine Learning logo

Machine Learning

Mar. 27, 2021

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Understanding Word2Vec through Cultural Dimensions

Understanding the decisions AI make is critical in mitigating its downsides. This article explains what cultural dimensions are, and demonstrates how they can increase interpretability and quantify bias in word embeddings. Machine Learning logo

Machine Learning

Jul. 6, 2020

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Anything2Vec: Mapping Reddit into Vector Spaces

Word2Vec is a powerful machine learning technique for embedding text corpus' into vector spaces. While useful for NLP problems, this blog post shows how it can also be used to represent and better understand communities on Reddit. Machine Learning logo

Machine Learning

Jul. 2, 2020

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Classifying Shakespeare with Networks

What distinguishes Shakespeare's comedies from his tragedies? Without looking at a single line of dialogue, this article shows that it is possible to use networks to classify Shakespeare's plays. Posted on Towards Data Science. Graph Theory logo

Graph Theory

Jun. 18, 2020

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Do Neural Networks Ever Forget?

How machine learning throws a wrench in the 'right to be forgotten.' Bringing in some of the latest computational research on privacy, this post examines how the principles of GDPR collide with the realities of neural networks. Politics logo

Politics

Jun. 4, 2020

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Analyzing Political Polarization: Topic Centrality

Extending graph centrality to show how different political messages affect the flow of information. The final part in a series posted on the popular blog Towards Data Science. Graph Theory logo

Graph Theory

May 28, 2020

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Analyzing Political Polarization: Topic Modelling

Part two of three in a series that analyzes political polarization through network science. Modelling and extracting topics from political tweets. Posted on the popular blog Towards Data Science. NLP logo

NLP

May 21, 2020

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Analyzing Political Polarization: Engagement Graphs

Part one of a three in a series that breaks down my undergraduate thesis in an accessible and engaging format. Posted on the popular blog Towards Data Science. Graph Theory logo

Graph Theory

May 14, 2020

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Reinforcement Learning for Traffic Flow

A reinforcement learning agent that helps control the flow of traffic. Through this simple RL algorithm, we were able to reduce carbon emissions by a third, and cut time waiting at red lights in half. Reinforcement Learning logo

Reinforcement Learning

Dec. 1, 2019