Over the course of my career, I’ve 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.
Ethics and Responsible Data Science
I’m not an Ethicist — Episode 008 (Guest on The Podcast of Small Differences 2018)
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’s lives. The episode provides practitioners with concrete tools for approaching complex ethical challenges.
Network Science and Complex Systems
Network Science for Fun and Profit (for Open Source Bridge 2014)
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.
Machine Learning, Methods, and Applied Practice
Machine Learning in the Open (for Open Source Bridge 2012)
An approachable introduction to key concepts in machine learning and data mining for a broad technical audience. Topics included data preparation, supervised and unsupervised methods, model evaluation, scoring functions, and optimization. Emphasis was placed on demystifying algorithms and demonstrating their application through open-source tools.
Data Science in the Open (for Open Source Bridge 2011)
A conceptual overview of the foundations of data science. The talk highlighted the structure of analytical problems, common methodological pitfalls, and strategies for extracting value from real-world data. It served as an early primer for developers and analysts transitioning into DS/ML roles.
Methodological Research
Computing Spaces (Private Google Project Site) Deprecated
A research monograph examining how the structural properties of problem and solution spaces influence the reliability and efficiency of learning methods. The project connected ideas from category theory, empirical process theory, learning theory, domain theory, and recursion theory to build a unifying view of methodological choice. While internal and exploratory, the work shaped my thinking about model selection, inductive bias, and the limits of computational methods.
Foundational Academic Work
These papers reflect the earliest stage of my intellectual development, prior to my transition into applied ML and identity science. They illustrate longstanding interests in logic, epistemology, cognition, and the structure of explanation.
Ockham’s Razor in Russell’s Philosophy
An examination of Bertrand Russell’s use of simplicity across On Denoting, logical atomism, and neutral monism, with attention to how simplicity considerations shape the resolution of key philosophical puzzles.
A critical analysis of Daniel Dennett’s intentional stance and the notion of “real patterns,” assessing whether such patterns support a robust realist, instrumentalist, or intermediate philosophical position.
Steve Austin Versus the Symbol Grounding Problem (co-written with Dr. Scott Burgess) Published in Selected Papers from the Computers and Philosophy Conference (CAP2003).
A conceptual analysis of Stevan Harnad’s symbol grounding problem, illustrated through narrative scenarios involving the Six Million Dollar Man. The paper explored the role of transducers in bridging symbolic representations and embodied cognition.