Mihail Popescu, University of Missouri

Mihalil Popescu, PhD, researches artificial intelligence including convolutional neural networks (CNNs) and large language models (LLMs) as well as biomedical intelligent monitoring systems and eldercare technologies. His current research focuses on developing decision support systems for eldercare and chronic disease management. Chronic diseases such as diabetes, heart failure or depression decrease the quality of life and increase the risk of re-hospitalization. Dr. Popescu is currently investigating new technologies for eldercare such as new sensors (radar, infrared and depth cameras), new monitoring technologies and new artificial intelligence algorithms for early illness recognition and aging in place. Dr. Popescu is developing knowledge extraction and representation methodologies using large language models to address information explosion and personalized medicine.

Nigel G. Ward, University of Texas at El Paso

While amazing AI agents are being developed, turning them into useful interactive collaborators for people remains a challenge.  Interaction through spoken language has promise, but today this is often on the clunky side, unlike from the swift and effortless nature of day-to-day human-human interaction.  One common cause is the implicit assumption that every referent and every fact is either known to the user or not.  In contrast, humans constantly probe and confirm what each other knows.  For example, I can say “let’s meet at EPCC” using uptalk-style prosody in English, and thereby acknowledging that you may not know what/where EPCC is, and implicitly  inviting you to request elaboration if needed.  To build systems that can do this, we need to model the likelihood of the user knowing certain things, of needing to know them, and of being able to figure them out from a brief mention.  These problems may not be amenable to the usual machine-learning techniques  that have succeeded so well for large language models.  We are exploring these challenges in the course of building a game-playing agent able to interact deftly with a human co-player in the face of novel items and obstacles and time pressure.  

Nigel G. Ward is Professor of Computer Science at the University of Texas at El Paso.  He received his Ph.D. from the University of California at Berkeley in 1991, with Lotfi A. Zadeh as one of his committee members.  He served on the faculty of the University of Tokyo for ten years, and 2015-2016 was a Fulbright Scholar and Visiting Professor at Kyoto University. He chaired the Speech Prosody Special Interest Group from 2018 to 2024. He is co-creator of the Prosody Tutorial video series and similar presentations for the ACL, Interspeech, and the LSA. and is the author of Prosodic Patterns in English Conversation.

Ward's research areas are at the intersection of spoken language and human-computer interaction. Current topics include the subtle non-lexical and prosodic signals that enable inference of a dialog partner's needs, intentions, and feelings at the sub-second level; and ways to model and exploit these phenomena to improve speech-to-speech translation systems, dialog systems, and language assessment and teaching. These projects apply multiple methods: statistical, qualitative, experimental, corpus-based modeling, and systems building.

Larry Lesser, University of Texas at El Paso

UTEP Distinguished Teaching Professor Dr. Larry Lesser will share ideas and examples of mathematical poetry