Over the course of my career, I\u2019ve contributed talks, papers, and long-form projects that reflect my core professional interests: data science, machine learning, fraud and identity, methodological rigor, and the science of complex systems. While much of my recent work in device intelligence, identity signals, risk scoring, and real-time fraud detection is proprietary, the projects below highlight the public-facing dimension of my thinking and approach.<\/p>\n\n\n\n
I’m not an Ethicist — Episode 008<\/a> (Guest on The Podcast of Small Differences 2018) <\/span><\/strong><\/p>\n\n\n\n A discussion on applying ethical frameworks to practical data science work. We explored how to reason about harm, fairness, incentives, and responsibility in domains where technical decisions can materially affect people\u2019s lives. The episode provides practitioners with concrete tools for approaching complex ethical challenges.<\/p>\n\n\n\n Network Science for Fun and Profit<\/a> (for Open Source Bridge 2014)<\/strong><\/p>\n\n\n\n An introduction to network science as a framework for analyzing relational data and complex systems. I demonstrated how graph-based approaches help uncover patterns in user behavior, device relationships, and other types of connected data. The talk provided practical examples using R and emphasized the relevance of network structure to applied problems.<\/p>\n\n\n\n Machine Learning in the Open<\/a> (for Open Source Bridge 2012)<\/strong><\/p>\n\n\n\n
\n\n\n\nNetwork Science and Complex Systems<\/h3>\n\n\n\n
Machine Learning, Methods, and Applied Practice<\/h3>\n\n\n\n