Decision Support with User-Centered Visual Analytics and AI
Prof. Dr.-Ing. Jörn Kohlhammer
Head of Information Visualization and Visual Analytics
Fraunhofer Institute for Computer Graphics, Germany
Domain experts are increasingly using the potential of visual analytics, the integration of automated methods and visualization, for data-driven decision making as this research field moves further from basic to applied research. At the same time, the integration of the rapidly evolving AI field is a challenge in many application areas. We are working with experts in several application fields, including security analysts, engineers, doctors, and business analysts. In any of these domains, visual analytics and AI solutions not only have to create user-, data- and task-centered visualizations: they use automated techniques and have to explain results to domain experts to ensure an effective division of labor between human and system. This talk will work towards an integrated view of user-centered approaches that bring together automated methods, AI, and visualization in a user-centered-way. It will go into some of our current research topics looking at the future role of visualization, visual analytics and AI in these domains.
Bio–Sketch
Kohlhammer is head of the Competence Center for Information Visualization and Visual Analytics, and Professor for User-Centered Visual Analytics at TU Darmstadt. He has a Ph.D. from TU Darmstadt and an MSc from the Ludwig-Maximilian University in Munich. His competence center develops solutions for several application domains, including visual business analytics, medical data analysis of electronic health records, decision support in the public sector, and cybersecurity. Jörn is regular member of program committees for conferences like IEEE VIS and EuroVis, and acts as a reviewer for many conferences and journals. His personal research interests include user-centered design and decision-centered visualization.
Built environment visualisation as a route towards community engagement
Professor Richard Laing
Professor of Urban Collaboration
Department of Architecture and Built Environment, Northumbria University, United Kingdom.
The importance of community engagement within the practice of urban development has been understood and established for many years. However, finding ways to move this from being an aspiration to being something which is a regular part of professional practice can be extremely challenging. Many studies have explored the ways in which some key aspects of how we design, deliver and maintain our built environment are very often stepped in technical procedures and language, meaning that actual participation rarely moves outside the domain of professional experts. This keynote presentation will explore some of the cities upon which we can draw, and through which information visualisation (including the use of data rich models) can be utilised as a key thread running through how we understand and even use the constructed environment around us. The presentation will also draw on experiences and findings from recent studies in the fields of transport and mobilities, and will seek to draw connections across disciplines and sectors.
Bio-Sketch
Richard Laing is Professor of Urban Collaboration at Northumbria University, UK. He has particular expertise in the areas of collaboration (between parties, groups and individuals) and participation (of stakeholders and the wide community) in research. This has often drawn on his knowledge of emerging digital technologies, where the associated research methods are driven by a desire to identify and make use of innovative ways to communicate with target groups, and often in the context of sustainable cities and the built heritage. He is a member of the pool of experts for both URBACT and the European Urban Initiative, and has been an evaluator for Horizon Europe proposals. More broadly, he is a Chartered Surveyor and a trained chairperson and assessor for the RICS Assessment of Professional Competence.
The Eyes Are the Windows to the Mind: Implications for AI-Driven Personalized Interaction
Professor Cristina Conati
University of British Columbia. Canada
Eye-tracking has been extensively used both in psychology for understanding various aspects of human cognition, as well as in human-computer interaction (HCI) for evaluation of interface design or as a form of direct input. In recent years, eye-tracking has also been investigated as a source of information for machine learning models that predict relevant user states and traits (e.g., attention, confusion, learning, perceptual abilities). These predictions can then be leveraged by AI agents to model their users and personalize the interaction accordingly. In this talk, Dr. Conati will provide an overview of the research her lab has done in this area, including detecting and modeling user cognitive skills and affective states, with applications to user-adaptive visualizations, intelligent tutoring systems, and health.
Bio-Sketch
Dr Conati is a Professor of Computer Science at the University of British Columbia, Vancouver, Canada. She received an M.Sc. in Computer Science at the University of Milan, and an M.Sc. and Ph.D. in Intelligent Systems at the University of Pittsburgh. Cristina has been researching human-centered AI and AI-driven personalization for over 25 years, with contributions in the areas of Information Visualization, Intelligent Tutoring Systems, User Modeling, Affective Computing,and Explainable AI. Cristina’s research has received 10 Best Paper Awards and the Test of Time Time Award 2022 from the Educational Sata Mining Society. She is a Fellow of AAAI (Association for the Advancement of AI) and of AAIA (Asia-Pacific Artificial Intelligence Association ), an ACM Distinguished Member, and co-Editor in Chief of the Journal of AI in Education. She served as President of AAAC, (Association for the Advancement of Affective Computing), as well as Program or Conference Chair for several international conferences.
Beyond transparency: interactive explanations for user empowerment
Professor Katrien Verbert
HCI division of the Computer Science Department at KU Leuven, Belgium
Despite the long history of work on explanations in the Machine Learning, AI and Recommender Systems literature, current efforts face unprecedented difficulties: contemporary models are more complex and less interpretable than ever. As such models are used in many day-to-day applications, justifying their decisions to end users will only become more crucial. In addition, several researchers have voiced the need for interaction with explanations as a core requirement to support empowerment of users. Such interaction methods can enable users to steer models with input and feedback, and can support better model understanding. In this talk, I will present our work on interactive explanation methods tailored to the needs of end users, such as healthcare professionals and job seekers. In addition, I will present our work on combining data-centric and model-centric explanations to empower end users in refining predictive models. Our work emphasizes explanation methods that move beyond passive transparency to actively empower users in guiding and refining AI systems.
Bio-Sketch
Katrien Verbert is professor at the Department of Computer Science at KU Leuven. She obtained a doctoral degree in Computer Science in 2008 at KU Leuven, Belgium. She was a postdoctoral researcher of the Research Foundation – Flanders (FWO) at KU Leuven. She was an Assistant Professor at TU Eindhoven, the Netherlands (2013 – 2014) and Vrije Universiteit Brussel, Belgium (2014 – 2015). Her research interests include visualisation techniques, recommender systems, explainable AI, and visual analytics. She has been involved in several European and Flemish projects on these topics, including the EU ROLE, STELLAR, STELA, ABLE, LALA, PERSFO, Smart Tags and BigDataGrapes projects. She is also involved in the organisation of several conferences and workshops (program chair IUI 2025, program chair RecSys 2024, general chair IUI 2021, program chair LAK 2020, general chair EC-TEL 2017, program chair EC-TEL 2016, workshop chair EDM 2015, program chair LAK 2013 and program co-chair of the EdRecSys, VISLA and XLA workshop series, DC chair IUI 2017, DC chair LAK 2019).
AI and Visualization frontier: moving to Visual Knowledge Discovery
Professor Boris Kovalerchuks
Central Washington University, United States of America
The integration of artificial intelligence/machine learning (AI/ML) with visualization techniques has emerged as a transformative paradigm for analyzing high-dimensional data. By bridging computational analytics with human-centric visual reasoning, this interdisciplinary field—termed Visual Knowledge Discovery (VKD)—addresses critical challenges in interpretability, scalability, and cognitive augmentation for AI/ML. While classical orthogonal Cartesian coordinates have been a backbone of science for 400 years, they allow to visualize only 2D or 3D data. Parallel Coordinates removed this limitation for some types of data. The key new opportunity here is in a lossless visualization of high-dimensional data with emerging General Line Coordinates (GLC). Current advancements, trends and future challenges in the AI and Visualization frontier will be presented. It will include generalized coordinate systems for visualization of larger high-dimensional data, explainable, real-time, multimodal, immersive learning and visual analytics.
Bio-Sketch
Dr Boris Kovalerchuk is a professor of Computer Science at Central Washington University, USA. His publication activities include five books published by Springer: “Data Mining in Finance” (2000), “Visual and Spatial Analysis” (2005), “Visual Knowledge Discovery and Machine Learning” (2018), and “Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery” (2022), “Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery” (2024), chapters in the Data Mining/Machine learning Handbooks (2006, 2010, 2023) with total over 200 publications. He coined the term “Visual Knowledge Discovery” (VKD), which link AI and visualization frontiers. His research and teaching interests are in AI, machine learning, visual analytics, visualization, uncertainty modeling, image and signal processing, and data fusion. Dr. Kovalerchuk has been a principal investigator of research projects in these areas, supported by the US Government agencies. He delivered relevant tutorials and keynote talks at several conferences recently.