Leila Wehbe

Leila Wehbe is an Associate Professor in the Machine Learning Department and Neuroscience Institute at Carnegie Mellon University (CMU), with affiliations in Psychology and Computational Biology. She leads a research group focused on understanding high-level brain representations of language and vision using machine learning techniques. Her work combines neuroimaging (fMRI/MEG) with computational models to investigate how the brain processes meaning and visual stimuli.

Education:

• PhD in Machine Learning from CMU, advised by Tom Mitchell
• BE in Electrical and Computer Engineering from the American University of Beirut
• Postdoc at UC Berkeley's Helen Wills Neuroscience Institute with Jack Gallant

Research Interests:

Her research bridges cognitive neuroscience and AI, focusing on:

• Decoding language and visual processing from brain activity
• Developing machine learning models aligned with brain representations
• Investigating semantic composition in language
• Exploring visual cortex selectivity for objects/food
• Improving neural decoding with advanced methods (e.g., transformers, generative models)

Awards & Recognition:

• NSF CAREER Award (2022)
• NIH R21/R01 Awards
• Human Frontier Science Program Award
• Google Faculty Research Award

Grants & Labs:

Leads the Wehbe Lab, part of brAIn at CMU. Active in grant programs including NSF and NIH, focusing on language-brain alignment and visual cortex studies. Co-organized workshops at ICLR and CVPR on brain-inspired AI.