Andrew Gordon Wilson joined the faculty at Cornell in August 2016 as an assistant professor in the School of Operations Research and Information Engineering (ORIE). He received his Ph.D. at the University of Cambridge in 2014, and spent the following two years as a postdoctoral research fellow at the Machine Learning Department in the School of Computer Science at Carnegie Mellon University. His research is in machine learning, with a focus on scalable Gaussian processes, deep learning, Bayesian methods, and kernel learning. Professor Wilson has received best dissertation (G-Research 2014), outstanding reviewer (NIPS 2013), and best student paper (UAI 2011) awards.
Professor Wilson's methodological research interests are at the intersection of scalable Bayesian nonparametrics, kernel methods, and deep learning. From an applied perspective, he is interested in building interpretable machine learning approaches leading to new scientific insights. He is also interested in policy relevant applications, including smart cities, and crime forecasting.
Professor Wilson teaches foundational aspects of machine learning, including model construction, Bayesian inference, variational methods, Gaussian processes, and deep learning methodology.
Professor Wilson has been on reviewing committees for Biometrika, Neurocomputing, Journal of Machine Learning Research (JMLR), Electronic Journal of Statistics, Journal of Artificial Intelligence Research (JAIR), IEEE Transactions on Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, Neural Information Processing Systems (NIPS), International Conference on Machine Learning (ICML), Artificial Intelligence and Statistics (AISTATS), Uncertainty in Artificial Intelligence (UAI), International Joint Conference on Artificial Intelligence (IJCAI), International Conference on Learning Representations (ICLR).
- 2016. "Stochastic Variational Deep Kernel Learning." Paper presented at Neural Information Processing Systems (NIPS) .
- 2015. "Kernel Interpolation for Scalable Structured Gaussian Processes." Paper presented at International Conference on Machine Learning (ICML) .
- 2013. "Gaussian Process Kernels for Pattern Discovery and Extrapolation." . .
- 2015. "The Human Kernel." Neural Information Processing Systems (NIPS). .
- 2011. "Generalised Wishart Processes." Uncertainty in Artificial Intelligence (UAI) .Best Student Paper. .
Selected Awards and Honors
- Outstanding Dissertation Award (G-Research) 2014
- Outstanding Reviewer Award (Neural Information Processing Systems (NIPS)) 2013
- Best Student Paper Award (Uncertainty in Artificial Intelligence (UAI)) 2011
- NSERC Postgraduate Doctoral Scholarship 2010
- John Collison Memorial Scholarship in Mathematics 2008
- B. Sc. ((Hon)Mathematics and Physics), University of British Columbia, 2008
- Ph D (Machine Learning, Department of Engineering), Trinity College, University of Cambridge, 2014