(734) 763-3499
Applications: Computer Science Methodologies: Algorithms, Artificial Intelligence, Bayesian Methods, Classification, Computational Tools for Data Science, Data Collection Design, Data Management, Data Mining, Data Visualization, Database Systems and Infrastructure, Decision Science, Deep Learning, Dynamical Models, Graph Theory and Graph-based Methods, Heterogeneous Data Integration, High-Dimensional Data Analysis, Image Data Processing and Analysis, Information Theory, Longitudinal Data Analysis, Machine Learning, Network Analysis, Optimization, Pattern Analysis and Classification, Predictive Modeling, Real-time Data Processing, Signal Processing, Sparse Data Analysis, Spatio-Temporal Data Analysis, Statistical Inference, Statistical Modeling, Statistics, Tensor Analysis, Time Series Analysis Relevant Projects:

NSF, NIH

Long Nguyen

Associate Professor, Statistics

Affiliation(s):

Electrical Engineering and Computer Science

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.