I am currently a Computer Science Ph.D. student @ Carnegie Mellon University and resident in the Frank-Ratchye STUDIO for Creative Inquiry. My research practice centers around the study of emergent phenomena in interactive human-in-the-loop systems, including (but not limited to) algorithmic bias, model dynamics, and learning effects (in both humans and machines). If I was doing my Ph.D. in the ‘50s you might call this Cybernetics

Through a combination of large-scale sequential experimentation, dynamical systems theory, and probabilistic choice models, I am working to further our understanding of the potential unintended consequences of deploying machine learning in high-stakes scenarios. Additionally, by developing new mixed methods approaches to evaluating these systems, I hope to help others approach technical research with a critical lens in order to build more usable and humane technology.

Previously, I co-founded Zipfian Academy (an immersive data science training program acquired by Galvanize), taught classes at the University of San Francisco, and built a Data Visualization MOOC with Udacity. In addition to my academic teaching, I have also run data science trainings for a Fortune 100 company and taught workshops at Strata, PyData, & DataWeek (among others). In a former life I worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Going all most of the way back… I first discovered my love of all things data while studying Computer Science and Physics @ UC Berkeley.

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