Andrew Gordon Wilson is an assistant professor in the School of Operations Research and Information Engineering at Cornell University. He spent the last two years as a postdoctoral research fellow in the Machine Learning Department at Carnegie Mellon University. He received his PhD from the University of Cambridge in 2014, under the supervision of professor Zoubin Ghahramani. Andrew's research is focused on building scalable and interpretable frameworks for kernel learning and deep learning. His work has been applied to long range crime forecasting, public health, and policy relevant problems, time series, image, and video extrapolation, geostatistics, gene expression, natural sound modeling, Bayesian optimization, and NMR spectroscopy. Andrew has received best dissertation (G-Research, 2014), outstanding reviewer (NIPS, 2013), and best student paper (UAI, 2011) awards.
Been Kim is a Research Scientist at AI2. She is also an affiliated faculty in the Department of Computer Science & Engineering at the University of Washington. Her research focuses on interactive and interpretable machine learning models for human-machine collaboration. She received her PhD. from MIT. Prior to her PhD, she worked at the MathWorks as a software engineer.
William Herlands is a PhD student in Machine Learning and Public Policy at CMU. His 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, he studies dynamics of diseases, crime, and transportation. Ultimately, his research is oriented toward helping policy makers create more targeted and effective policy interventions. He has a wife who enjoys trolling his bio.