Leila Wehbe Assistant Professor
Machine Learning Department
& Neuroscience Institute
Carnegie Mellon University

I am an assistant professor in the Machine Learning Department and the Neuroscience Institute at Carnegie Mellon University.

Before that, I was a postdoctoral researcher in the Helen Wills Neuroscience Institute at the University of California, Berkeley. I was working with Jack Gallant (here is the lab website).

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.

In 2009 I completed a BE in Electrical and Computer Engineering at the American University of Beirut. In 2008, I did an internship in the Kanwisher Lab at MIT.


- Our papers on using brain activity to interpret NLP models, to add brain-relevant bias to BERT, and to study the similarity of representations from different computer vision tasks have been accepted at NeurIPS 2019.

- Co-organizing a workshop on Context and Compositionality in Biological and Artificial Neural Systems at NeurIPS 2019.

- Received the 2018 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.

Investigating High-Level Representations in the Brain

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.



Note for prospective PhD students:
I work with students from multiple departments at CMU.
If you are outside CMU, you must first apply through
the centralized admissions process. For example, you can
consider applying to the Machine Learning PhD Program or
the PhD Program in Neural Computation.