Quantitative Skills + Qualitative Sensibility
Welcome!
I am a data scientist who uses computational methods to tackle complex and challenging data problems. I specialize in using artificial intelligence and machine learning to fill measurement gaps. Much of my work is on identifying and predicting geopolitical risks.
I describe my outlook as both qualitative and quantitative because I start from a place of appreciation that deep understanding of data is the route to identifying the insights and opportunities that my quantitative abilities can unlock.
I am curious and creative: I love to develop and adapt tools as they become available. As the same time, my qualitative sensibility drives me to create end-products that makes sense for the big-picture. To that end, several of my projects adapt quantitative and computational methods for nontraditional data in political science, particularly applied Bayesian statistics, network analysis, and text-as-data.
My current projects include using texts and networks to measure organizational transformations and evolution, as well as a project developing a bespoke Bayesian Item Response Theory model (IRT-M) that solves a long-running interpretability problem in an entire class of models. For this project, I am the lead designer of an R statistical package for the IRT-M model.
I am a data scientist who uses computational methods to tackle complex and challenging data problems. I specialize in using artificial intelligence and machine learning to fill measurement gaps. Much of my work is on identifying and predicting geopolitical risks.
I describe my outlook as both qualitative and quantitative because I start from a place of appreciation that deep understanding of data is the route to identifying the insights and opportunities that my quantitative abilities can unlock.
I am curious and creative: I love to develop and adapt tools as they become available. As the same time, my qualitative sensibility drives me to create end-products that makes sense for the big-picture. To that end, several of my projects adapt quantitative and computational methods for nontraditional data in political science, particularly applied Bayesian statistics, network analysis, and text-as-data.
My current projects include using texts and networks to measure organizational transformations and evolution, as well as a project developing a bespoke Bayesian Item Response Theory model (IRT-M) that solves a long-running interpretability problem in an entire class of models. For this project, I am the lead designer of an R statistical package for the IRT-M model.