Research
Interpretable Maximal Discrepancies Metrics for Analyzing and Improving Generative Models
Project funded by the Office of Naval Research
Learn MoreAdvancing Machine Learning for Neuroimaging Through Topology-Aware Signal Processing
Project funding from a University of Delaware Research Foundation-Strategic Initiative grant
Learn MoreImproving Reference Prioritisation With PICO Recognition
Machine learning can assist with multiple tasks during systematic reviews to facilitate the rapid retrieval of relevant references during screening and to identify and extract information relevant to the study characteristics, which include the PICO elements of patient/population, intervention, comparator, and outcomes.
Learn MoreQuantifying the Informativeness of Similarity Measurements
In this paper, we describe an unsupervised measure for quantifying the 'informativeness' of correlation matrices formed from the pairwise similarities or relationships among data instances.
Learn MoreLearning Recurrent Waveforms Within EEGs
We explore a modeling approach that automatically learns recurrent temporal waveforms within EEG traces.
Learn MoreNeural Decoding With Kernel-Based Metric Learning
Machine learning (optimizing feature weightings and projections using kernel-based dependence) for enhancing neural data analysis, applied to a somatosensory neural decoding task.
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