CS Seminar: Paul Liang

Description

Paul Liang, a doctoral student in machine learning at Carnegie Mellon University, will give a talk titled "Foundations of Multisensory Artificial Intelligence" for the Department of Computer Science.

Abstract:

Building multisensory AI systems that learn from multiple sensory inputs—such as text, speech, video, real-world sensors, wearable devices, and medical data—holds great promise for many scientific areas in terms of practical benefits, such as supporting human health and well-being, enabling multimedia content processing, and enhancing real-world autonomous agents. In this talk, Paul Liang will discuss his research on the machine learning principles of multisensory intelligence, as well as practical methods for building multisensory foundation models over many modalities and tasks. In the first half of the seminar, Liang will present a theoretical framework formalizing how modalities interact with each other to give rise to new information for a task. These interactions are the basic building blocks in all multimodal problems and their quantification enables users to understand multimodal datasets and design principled approaches to learn these interactions. In the second half of the seminar, Liang will present his work in cross-modal attention and the multimodal transformer architectures that now underpin many of today's multimodal foundation models. Finally, he will discuss his collaborative efforts in scaling AI to many modalities and tasks for real-world impact on affective computing, mental health, and cancer prognosis.