Leila Wehbe Associate Professor
Machine Learning Department
& Neuroscience Institute
Carnegie Mellon University
I am an associate professor in the Machine Learning Department and the Neuroscience Institute at Carnegie Mellon University. I am also affiliated with the Department of Psychology and the Computational Biology Department .
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.
- Check out our github page for recent projects https://github.com/brainml.
- NIH R21 award for using Machine Learning to model depression in developmental data.
- NSF CAREER award on "Uncovering the brain circuitry of language and its interaction with other modalities".
- Human Frontier Science Program award on "Understanding the neural basis of early language development".
- Research on food featured in the NewScientist.
- Our lab is part of brAIn at CMU.
- Tutorial Chair for UAI 2022.
- Program Committee Member for Cognitive Computational Neuroscience (CCN) 2022.
- Co-organized an ICLR workshop titled How Can Findings About The Brain Improve AI Systems?
- NIH R01 award (CRCNS) on "Discovering Principles of Language Processing in the Brain using Neurocomputational Models".
- Co-organized a CVPR workshop titled Minds vs. Machines: How far are we from the common sense of a toddler?
- Co-organized a workshop on Context and Compositionality in Biological and Artificial Neural Systems.
- Received the Google Faculty Research Award.
- 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 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. You can read more here.