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
VLG organized the 2024 Bioinspired Machine Learning Workshop
VLG organized the Bioinspired Machine Learning Workshop, which brought together scientists worldwide to discuss research at the intersection between neurosci...
WVU research merges virtual reality and artificial intelligence to reveal and analyze gigantic images in stunning detail
NIH funding will allow the development of fast and adaptive AI-based immersive analytics tools for large volumetric microscopy data quantification.
WVU scientists spice up genetic research through habanero peppers and AI
New AI tools for pattern association discovery will be developed for crop phenomics, which could later support the prevention and treatment of genetic diseas...
Top WVU seniors named, eight honored with 2023 Order of Augusta
Congratulations to Ram Zaveri for being named among the 2023 Top WVU Seniors!