Check out my work 👨🔧
Safety2Vec: An Introduction to Text Embeddings and their Applications in T&SJul. 2023
TrustCon2023 presentation on how embeddings models can enable Trust & Safety teams to better understand user behaviour on their platforms.
Automated vs. manual case investigation and contact tracing for pandemic surveillanceNov. 2022
Lead author on the first randomized control trial of COVID-19 contact tracing – accepted to The Lancet's subsidiary, eClinicalMedicine.
My Next PhaseAug. 2022
Life update: I’m joining OpenAI's Trust & Safety team!
Measuring Alignment of Grassroots Political Communities with Political CampaignsMay 2022
Lead author on this study, accepted to ICWSM 2022, that uses neural embedding techniques to analyze how grassroots political communities on Reddit align with their respective political campaigns.
Simulating Statistical Power in RMar. 2022
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.
Read MoreCausal Inference
Break Them up? The Case for Interoperability Among Direct Messaging PlatformsFeb. 2022
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.
Managing Online Rumour Proportions During Offline ProtestsAug. 2021
Master's thesis at the University of Oxford. An experimental analysis of misinformation and rumour sharing during ambiguous contexts. Awarded distinction and one of 4 'Highly Commended' thesis prizes.
Bitmoji Facial SegmentationJul. 2021
Utilized PyTorch for transfer learning (U-NET architecture/ImageNet base weights) to develop computer vision models for facial semantic segmentation of Bitmojis at Princeton University's Department of Psychology.
How COVID-19 has Changed our Music Listening HabitsMar. 2021
COVID-19 has brought enormous amounts of anxiety and uncertainty. This article shows how the pandemic has affected popular music listening habits.
Understanding Word2Vec through Cultural DimensionsJul. 2020
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.
Anything2Vec: Mapping Reddit into Vector SpacesJul. 2020
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.
Classifying Shakespeare with NetworksJun. 2020
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.
Do Neural Networks Ever Forget?Jun. 2020
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.
Analyzing Political Polarization: Topic CentralityMay 2020
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.
Analyzing Political Polarization: Topic ModellingMay 2020
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.
Analyzing Political Polarization: Engagement GraphsMay 2020
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.
Topic Centrality for Political MessagingApr. 2020
Using network science and unsupervised machine learning to quantify the "bridging" and "bonding" nature political messages.
Modelling Axes of Political EngagementMar. 2020
Do people care more about policy or politicians when choosing to retweet political content online? Undergraduate thesis developing random graph models to model drivers of political engagement.
Reinforcement Learning for Traffic FlowDec. 2019
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.
Read MoreReinforcement Learning