Paul Liang

Carnegie Mellon University , 2024
Ph.D. , Computer Science
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Paul Liang is a Ph.D. student in Machine Learning at CMU, advised by Louis-Philippe Morency and Ruslan Salakhut-dinov. His research lies in the foundations of multimodal machine learning with applications in socially intelligent AI, understanding human and machine intelligence, healthcare, and education. He is a recipient of the Waibel Presidential Fellowship, Facebook PhD Fellowship, Center for Machine Learning and Health Fellowship, and the Alan J. Perlis Graduate Student Teaching Award. His research has been recognized by 3 best-paper awards at NeurIPS workshops and ICMI. He regularly organizes courses, workshops, and tutorials on multimodal machine learning and was a workow chair for ICML 2019. He was a research intern at DeepMind, Facebook, Nvidia, and Google, and visited Harvard, Berkeley, Stanford, and Riken through research collaborations. Previously, he received an M.S. in Machine Learning and a B.S. with University Honors in Computer Science and Neural Computation from CMU.


Carnegie Mellon University

Ph.D. , Computer Science

Class of 2024

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