William Herlands is a Ph.D. student at Carnegie Mellon University.
I am a Ph.D. student in a dual program in Machine Learning and Public Policy at Carnegie Mellon University.
I am a member of the Event Pattern Detection Lab run by my advisor, Daniel Neill. My work is partially funded by an NSF Graduate Research Fellowship and an ARCS Scholars Fellowship.
My research focuses on developing scalable Bayesian nonparametric methods for multidimensional modeling, prediction, and causal inference. These methods are particularly suited for analyzing complex data arising from human behavior, while still providing clear and interpretable results.
Using these methods I study dynamics of diseases, crime, and transportation. Ultimately, my research is oriented towards helping policy makers create more targeted and effective policy interventions. Through better analytics and clearer communication between researchers and policy makers I believe we can make government smarter, smaller, and more just.