Call for Tutorials
Keynote Date: September 15
Keynote Title: From Hypertext to Hyper-AI: Introducing Human-Centered AI Agents.
Abstract: The invention of hypertext revolutionized how we access and navigate information. Today, with the rise of Artificial Intelligence (AI), especially agentic AI, we stand on the verge of another transformation: imagine every hyperlink as an entry point to a live AI agent, and every network of links as a dynamic ecosystem of specialized AI agents. In this talk, I will introduce human-centered AI agents, a new generation of advanced agents designed to deeply understand their users and proactively collaborate with them to achieve shared goals. Through concrete examples and real-world applications, I will demonstrate how these agents go far beyond information retrieval. They augment human capabilities and help scale scarce resources, taking on valuable tasks that people find difficult or undesirable. I will also present the psychological foundations and computational models behind these agents, showing why they require multiple intelligences, including personal intelligence and interactional intelligence, beyond the language abilities of generative AI. Finally, I will discuss the tooling and design principles needed to empower non-technical professionals to create, manage, and safeguard human-centered AI agents as easily as they create and manage hyperlinks.
Bio: Dr. Michelle Zhou is the co-founder and CEO of Juji, Inc., an Artificial Intelligence (AI) company located in Silicon Valley, which has pioneered the creation of human-centered AI agents with generative + cognitive AI to automate diverse, complex business tasks involving nuanced personal interactions, such as customer advising, motivational interviewing/persuading, skills coaching, and patient counseling. Michelle is an expert in human-centered AI, an interdisciplinary field that bridges AI and Human-Computer Interaction (HCI), and an inventor of IBM Watson Personality Insights. Her thought leadership has been featured in major outlets, including The New York Times, Fast Company, Fortune, MIT Tech Review, Forbes, and more. She has published 100+ peer-reviewed, refereed scientific articles and 45+ patent applications. Michelle has successfully led the research and development of more than a dozen cutting-edge human-centered AI products and solutions in multiple industries, including Education, Healthcare, Federal Government, and Retail. Michelle has served as the Editor-in-Chief of ACM Transactions on Interactive Intelligent Systems (TiiS), a leading scientific journal on Human-Centered AI. She earned her Ph.D. in Computer Science from Columbia University and is an ACM Distinguished Scientist.
Keynote Date: September 16
Keynote Title: Human-AI Collaboration in Adaptive Information Access: From Adaptive Hypermedia to Recommender Systems.
Abstract:One of the challenges of modern Human-Centered AI is developing efficient approaches for Human-AI collaboration, a partnership where people and artificial intelligence systems work together, combining their strengths to achieve better outcomes than either could achieve alone. Among the areas where Human-AI collaboration approaches have already demonstrated their value is adaptive information access. Adaptive information access systems, such as adaptive hypermedia, personalized search, and recommender systems, attempt to model their users to help them in finding the most relevant information. Yet, their AI-based user modeling and personalization mechanisms might not always work as expected, resulting in errors, biases, and suboptimal behavior. Combining the decision power of AI with the ability of the user to guide and control it brings together the strong sides of artificial and human intelligence and could lead to better results. In this talk, I will review the work of our team and the broader research community on exploring the ideas of human-AI collaboration in adaptive information access systems and discuss lessons learned, prospects, and challenges of this direction of research.
Bio: Dr. Peter Brusilovsky is a Professor of Information Science and Intelligent Systems at the University of Pittsburgh, where he directs the Personalized Adaptive Web Systems (PAWS) lab. Peter has been working in the field of personalized learning, student and user modeling, recommender systems, and intelligent user interfaces for over 30 years. He published numerous papers and edited books on adaptive hypermedia, the adaptive Web, and social information access. He is a recipient of the Alexander von Humboldt Fellowship, the NSF CAREER Award, and the Fulbright-Nokia Distinguished Chair. Peter served as the Editor-in-Chief of IEEE Transactions on Learning Technologies and a program chair for several conferences.. He is the Past Chair of ACM SIGWEB and a board member of several journals including User Modeling and User Adapted Interaction, ACM Transactions on Social Computing, and International Journal of AI in Education. His current interests are focused on user-centered intelligent systems in the areas of adaptive learning, recommender systems, and personalized health.
Keynote Title: Communicating Science in the Digital Age.
Abstract: Today, most academics share their research online, while the public, journalists, and policymakers increasingly rely on digital media as a primary source of scientific information. In a landscape where science is often misunderstood, politicized, or sensationalized, it is more important than ever to understand how research is communicated and shaped across online platforms. This talk presents our work on several pressing and open questions: Does sharing scientific articles online influences their citation impact? If online dissemination affects citations—and by extension, academic evaluation—can all scholars participate on equal footing? How do researchers balance the pressure to promote their work with the risk that digital incentives prioritize virality over scientific rigor? Do retracted studies continue to circulate online? And how is the rise of AI transforming the dissemination and even the production of scientific knowledge? I will explore these challenges and reflect on how digital platforms might be designed or reimagined to support more effective, equitable, and responsible science communication.
Bio: Dr. Ágnes Horvát is an Associate Professor of Communication and Computer Science (by courtesy) at Northwestern University, USA. Her research in human-centered computing and network science investigates how online spaces operate and disseminate information. Her group strives to make digital tools more efficient for scientists, entrepreneurs, and creative artists. Their recent projects investigate the use of LLMs in scientific writing and music creation, study biases in online attention to science, identify cases of collective intelligence and opportunities for improved decision-making, and develop frameworks to examine persuasion and opinion change in online discussions. Professor Horvát’s work has been awarded an NSF CAREER award. Her doctoral advisees have received highly competitive prizes, including a Northwestern Presidential Fellowship and best student paper awards at international conferences. Her research has been featured recently in Nature, The New York Times, The Washington Post, Le Monde, Frankfurter Allgemeine, and The Economist.