Speaker Series

This series aims to bring together thought leaders and experts in the field of Natural Language Processing (NLP) to share their knowledge, research, and insights with our community. The talks are designed to foster collaboration, spark new ideas, and enhance learning for students, researchers, and professionals in NLP.

Talks

March 14, 2025

This talk is held as a part of USF’s Institute for AI+X Seminar.

  • Talk Title: Personas, Roleplaying, and the Imitation of Human Cognition in Large Language Models
  • Presenter: Stephen Steinle
  • Location: ENB 118, 1:00OPM - 2:00PM
  • Abstract: One of the growing trends in the use of large language models (LLMs) is to use descriptions of people and their preferences to influence response generation. These descriptions, known as personas, have been shown to substantially impact performance on a variety of tasks and continue to remain an area of rapidly advancing research. This discussion will introduce the concept of personas, implementations in research, an example use case in published work, and some potential pitfalls. Cognitive modeling will also be briefly discussed to highlight the distinction between LLM roleplaying and human cognition.
  • Bio: Stephen Steinle is a second year Ph.D student in the Computer Science department at the University of South Florida. He works in the Advancing Machine and Human Reasoning (AMHR) Lab under the guidance of Dr. John Licato. His work has concerned cognitive modeling, next word prediction, and the methods used to make LLMs act more similarly to individuals in the field of Digital Twinning. His current projects involve multi-agent interactions during adversarial and collaborative tasks such as board games and wargames.

April 7, 2025

Bonnie Dorr

  • Talk Title: Navigating NLP in the Generative AI Era: Challenges, Risks, and New Frontiers
  • Presenter: Bonnie Dorr, Ph.D., Professor, Department of Computer and Information Science and Engineering, University of Florida
  • Location: Virtual - Microsoft Teams
  • Abstract: This talk explores the future of Natural Language Processing (NLP) in the Generative AI (GenAI) era, highlighting the need for hybrid approaches that integrate linguistic principles with neural models to enhance interpretability, capture implicit meanings such as beliefs and intentions, and ensure transparency. Representative examples of GenAI output illustrate areas requiring further exploration, particularly in relation to task-specific goals, such as machine translation and social engineering detection. Recent research in UF’s NLP&Culture Laboratory further exemplifies these principles: (1) addressing ambiguities with external knowledge to produce more robust and explainable inferences; (2) using semantic role labeling to detect and address communication divergences; (3) combining structured chunking with neural techniques to optimize entity and relationship recognition; and (4) leveraging NLP-driven metrics to assess how communication dynamics impact vulnerability management. By embedding structured linguistic insights into GenAI models, these systems can become more reliable, interpretable, and adaptable to diverse linguistic contexts, tackling key challenges while unlocking new opportunities for NLP applications.
  • Bio: Bonnie J. Dorr is a Professor in the Department of Computer and Information Science and Engineering at the University of Florida, where she directs the Natural Language Processing & Culture (NLP&C) Laboratory. She is also an affiliate of the Florida Institute for National Security, former program manager of DARPA’s Human Language Technology programs, and Professor Emerita at University of Maryland. Dorr is a recognized leader in artificial intelligence and natural language, specializing in machine translation and cyber-aware language processing. Her research explores neural-symbolic approaches for accuracy, robustness, and explainable outputs. Applications include cyber-event extraction for detecting and mitigating attacks, detecting influence campaigns, and building interpretable models. She is a NSF PECASE Fellow, a Sloan Fellow, and a Fellow of AAAI, ACL, and ACM.

April 18, 2025

This talk is held as a part of USF’s Bellini College of Artificial Intelligence, Cybersecurity and Computing RISE Speaker Series. Cindy Bearfield

  • Talk Title: Designs to Support Better Visual Data Communication
  • Presenter: Cindy Bearfield, Ph.D., Assistant Professor, School of Interactive Computing, Georgia Tech
  • Location: Virtual - Microsoft Teams
  • Abstract: Well-chosen data visualizations can lead to powerful and intuitive processing by a viewer, both for visual analytics and data storytelling. When badly chosen, visualizations leave important patterns opaque or misunderstood. So how can we design an effective visualization? I will share several empirical studies demonstrating that visualization design can in@luence viewer perception and interpretation of data, referencing methods and insights from cognitive psychology. I leverage these study results to design natural language interfaces that recommend the most effective visualization to answer user queries and help them extract the ‘right’ message from data. I then identify two challenges in developing such an interface. First, human perception and interpretation of visualizations is riddled with biases, so we need to understand how people extract information from data. Second, natural language queries describing takeaways from visualizations can be ambiguous and thus dif@icult to interpret and model, so we need to investigate how people use natural language to describe a speci@ic message. I will discuss ongoing and future efforts to address these challenges, providing concrete guidelines for visualization tools that help people more effectively explore and communicate data.
  • Bio: Cindy Xiong Bearfield is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. Bridging the fields of psychology and data visualization, she aims to understand the cognitive and perceptual processes that underlie visual data interpretation and communication. Her research informs visualization design that elicits critical thinking and calibrated trust in complex data. She received her Ph.D. in Cognitive Psychology and M.S. in Statistics from Northwestern University. Her research has been recognized with an NSF CAREER award. She has received paper awards at premier psychology and data visualization venues, including ACM CHI, IEEE PacificVis, Psychonomics, and IEEE VIS.

May 19, 2025

  • Presenter: TBD
  • Bio: TBD
  • Talk Title: TBD
  • Abstract: TBD

July 28, 2025

  • Presenter: TBD
  • Bio: TBD
  • Talk Title: TBD
  • Abstract: TBD

September 29, 2025

  • Presenter: TBD
  • Bio: TBD
  • Talk Title: TBD
  • Abstract: TBD

November 24, 2025

  • Presenter: TBD
  • Bio: TBD
  • Talk Title: TBD
  • Abstract: TBD