Using a combination of large-scale sequential experimentation, dynamical systems theory, and probabilistic choice models, I investigate the emergent behavior of human-in-the-loop algorithmic systems and work towards better understanding (and anticipating) the unintended consequences of deploying machine learning in high-stakes scenarios. Additionally, by developing new mixed methods approaches to evaluating machine learning 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.