Leila Wehbe
Associate Professor
Department of Machine Learning
Carnegie Mellon University
To participate virtually, CLICK HERE
Research Overview
Wehbe uses functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG) to investigate how the brain represents complex meaning in everyday life.
FMRI and MEG record brain activity but yield very high dimensional, noisy images that are expensive to acquire. The number of data points in a typical experiment is therefore many orders of magnitude smaller than the number of data dimensions. Furthermore, there is a considerable subject-to-subject variability of brain anatomy. Combining data from multiple subjects is consequently a hard problem. Part of my work is finding machine learning solutions to these problems.
Another part of my work is defined by the complexity of language and the non-existence of a comprehensive model of meaning composition: we do not know how the meaning of successive words combine to form the meaning of a sentence. Investigating the brain representation of a sentence is therefore a complex task because we are both looking for the neural signature and trying to approximate the composition function. However, with appropriate experimental design and computational models, we can study both problems: we can use existing models of language to study the brain representation of meaning, and we can use brain data to evaluate different meaning composition hypotheses. This research direction naturally intersects with new AI models of language and other modalities.
This seminar is co-sponsored by the Center for Computational Cognition (CoCo)
Faculty Host: Anna Ivanova