My general research area is machine learning. My current work is focused on learning with graphical models, kernel methods and support vector machines.
I also have broad research interests in other areas, including bioinformatics and reinforcement learning.
Bio:
Yuhong Guo has recently earned her PhD degree from the Department of Computing Science at the University of Alberta.
She has broad research interests in the area of statistical machine learning. Her primary research has been developed in three directions: learning Bayesian networks from data, active learning and ensemble learning.
In particular, her doctoral dissertation has been focused on learning Bayesian networks from data, where she addressed the key challenges of learning good Bayesian network structures, learning accurate Bayesian network classifiers and training Bayesian networks in the presence of hidden variables. Besides machine learning, she has also been involved in research in bioinformatics, which is one of the main application areas for her current and future research.
