(734) 763-3499

Applications:
Computer Science
Methodologies:
Artificial Intelligence, Bayesian Methods, Computing, Data Integration, Data Mining, Data Visualization, Databases and Data management, Graph-Based Methods, Image Data, Information Theory, Machine Learning, Mathematical and Statistical Modeling, Networks, Optimization, Statistics
Relevant Projects:

NSF, NIH


Long Nguyen

Professor

Statistics, LSA
EECS, College of Engineering

I am broadly interested in statistical inference, which is informally defined as the process of turning data into prediction and understanding. I like to work with richly structured data, such as those extracted from texts, images and other spatiotemporal signals. In recent years I have gravitated toward a field in statistics known as Bayesian nonparametrics, which provides a fertile and powerful mathematical framework for the development of many computational and statistical modeling ideas. My motivation for all this came originally from an early interest in machine learning, which continues to be a major source of research interest. A primary focus of my group’s research in machine learning to develop more effective inference algorithms using stochastic, variational and geometric viewpoints.