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
Join the Vision and Learning Group
The Vision and Learning Group is always looking for outstanding prospective PhD students, either in Computer Science or Electrical Engineering.
Video Understanding
Video understanding is concerned with the parsing of the image data flow for the semantic understanding of the objects in the scene, but also their actions a...
CS674 Computational Photography - Fall 2017
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CS691A Machine Learning - Fall 2016
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