Explore My work 💻
AI and network science; politics and policy. Below are some of my ramblings. This is meant to be a more accessible outlet for the exciting but all too often inaccessible research done in the world of computational social science. All opinions are my own.
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.
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.
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.
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.
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.
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.
Part one of a three in a series that breaks down my paper, Measures of Topic Centrality for Online Political Engagement, in an accessible and engaging format. Posted on the popular blog Towards Data Science.