Bert Huang is an assistant professor in the Department of Computer Science at Virginia Tech, where he directs the Machine Learning Laboratory. He earned his Ph.D. from Columbia University in 2011, and spent three years as a postdoctoral research associate at the University of Maryland, College Park before joining Virginia Tech in 2015. His research investigates machine learning, with a focus on analyzing complex systems. His work addresses topics including structured prediction, probabilistic graphical models, and computational social science. His papers have been published at conferences including NIPS, ICML, UAI, and AISTATS, and he is an action editor for the Journal of Machine Learning Research.
URL: http://berthuang.com/Bert_Huang/Research.html Contact: Bert Huang The Machine Learning Laboratory investigates the methodology, algorithms, and theory of machine learning. It especially focuses on the problem of machine learning for complex phenomena, where data is best modeled as multi-relational, multi-modal networks. It emphasizes the joint exploration of theoretical, algorithmic, and applied research and aims to exploit connections within these views to strengthen understanding of each.