When: February 8, 2017, 12:30 PM
Location: 3rd Floor Orchard View Room , Discovery Building
Contact: 608-316-4401, firstname.lastname@example.org
Network-based whole-brain representational similarity learning
Technologies such as functional magnetic resonance imaging (fMRI) provide huge amounts of data that could help improve our understanding of the human brain but they are often plagued by many complications, including noise, high-dimensionality, strong and unknown statistical correlations. In this talk I will present a new tool, called Network representational similarity analysis (NRSA), developed to tackle some of these issues with the aim of understanding how different regions of the brain function in concert to process complex information. NRSA is a whole-brain approach to discovering arbitrarily structured brain networks (possibly widely distributed and non-local) that encode similarity information. This tool has enabled discovering such structure for cases where the interesting cortical regions and networks have proven elusive.
Metric Learning from Comparative Judgments
We present ongoing work on learning sparse and low rank metrics from comparative judgments. This problem takes inspiration from psychology studies where researchers commonly wish to learn which items people find similar and why. As it is difficult for humans to provide fine grained information, a standard query is of the form “is item i more like j or k?”.
SILO is a lecture series with speakers from the UW faculty, graduate students or invited researchers that discuss mathematical related topics. The seminars are organized by WID’s Optimization research group and sponsored by generous support of the Advance Technology Group of the 3M Company and the Analytics Group of Northwestern Mutual.
SILO’s purpose is to provide a forum that helps connect and recruit mathematically-minded graduate students. SILO is a lunch-and-listen format, where speakers present interesting math topics while the audience eats lunch.