Research Highlights
Out-of-Distribution Learning
Out-of-Distribution learning is concerned with developing methods where the distribution of the data processed at test time may be different from the distribution of the data used during the training of a model.
Immersive Analytics
Recent advanecs in augmented, mixed, and virtual reality, coupled with the need to perform analysis and decision-making on large-scale collections of volumetric images stimulate the research in immersive analytics.
Novelty and Anomaly Detection
Detecting the presence of outliers, like novelties or anomalies, with respect to a particular distribution has numerous applications in computer vision and in nearly every area of data science.
Recent Posts
Multiple Postdoc Positions - Computer Vision in Digital Health
Applicants are invited for multiple postdoc positions to work with researchers at the center. We are looking for a postdoc with a strong background in comput...
WVU-led Dolly Sods GPU cluster to drive new frontiers of computational research in physics and astronomy, drug discovery, data science and more
A $1.1M grant awarded to WVU by the NSF MRI program will allow building a HPC cluster based on GPUs for cutting-edge Deep Learning reseasch.
UPE recognizes two Vision and Learning Group members
UPE recognizes Ram Zaveri and Matthew Keaton for their outstanding academic and service excellence.
WVU-led Team to Build a Bridge to Better Health using AI and Digital Health
A $3M grant awarded to WVU by the NSF NRT program will train the new generation of engineers, scientists and healthcare professionals able to better bridge t...