I am an assistant 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.
- Our lab is part of brAIn at CMU.
- Research on food featured in the NewScientist.
- Check out our github page for recent projects https://github.com/brainml.
- Program Committee Member for Cognitive Computational Neuroscience (CCN) 2022.
- Co-organizing a workshop titled How Can Findings About The Brain Improve AI Systems? at ICLR 2021.
- Co-organizing a workshop titled Minds vs. Machines: How far are we from the common sense of a toddler? at CVPR 2020.
- Co-organized a workshop on Context and Compositionality in Biological and Artificial Neural Systems at NeurIPS 2019.
- 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 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.