I received my PhD from the Machine Learning Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University. I was a member of the Brain Image Analyis Group led by Tom Mitchell.
- Received the 2019 Google Faculty Research Award.
- Scheduled to speak at the 2019 workshop on Semantic processing and semantic knowledge at Dartmouth.
- Scheduled to speak at the Machine Learning in Neuroscience: Fundamentals and Possibilities SfN online conference.
- Scheduled to speak at the 2019 Cognitive Modeling and Computational Linguistics NAACL workshop.
- Spoke at the 2018 Workshop on Analyzing and interpreting neural networks for NLP.
- Gave a full day tutorial at the 2018 Society for Psychophysiological Research Annual Meeting.
- Was interviewed for the book Artificial Intelligence: Teaching Machines to Think Like People.
I use functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG) to investigate how the brain represents the meaning of words, sentences and stories.
FMRI and MEG record brain activity. They yield very high dimensional, noisy images. These images are also 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 brain image problems.
Another part of my work is defined by the complexity of language and the inexistence 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 settings and computational models, we can study both problems: we can use existing models of language to study the brain representation of meaning, or we can use brain data to evaluate different meaning composition hypotheses.