Monday, November 3rd, 2025
| 09:00am – 09:10am | Opening Remarks |
| 09:10am – 10:05am |
Keynote Presentation (Session Chair: Steffen Frey)Kwan-Liu Ma |
| 10:05am – 10:30am |
Papers Session 1 (Session Chair: Jianxin Sun) |
| 10:05am | Extracting Complex Topology from Multivariate Functional Approximation: Contours, Jacobi Sets, and Ridge-Valley Graphs Guanqun Ma, David Lenz, Hanqi Guo, Tom Peterka, Bei Wang |
| 10:17am | Extremely Scalable Distributed Computation of Contour Trees via Pre-Simplification Mingzhe Li, Hamish Carr, Oliver Rübel, Bei Wang, Gunther H. Weber |
| 10:30am – 11:00am | Break |
| 11:00am – 12:15pm |
Papers Session 2 (Session Chair: Tim Gerrits) |
| 11:00am | ChatVis: Large Language Model Agent for Generating Scientific Visualizations Tom Peterka, Tanwi Mallick, Orcun Yildiz, David Lenz, Cory Quammen, Berk Geveci |
| 11:12am | From Soup to Bricks: Fast Clustering of Fine-Grained AMR Hierarchies for Rendering on GPUs Stefan Zellmann, Ingo Wald |
| 11:24am | Lossy Parallel Visualization of Large-Scale Volume Data with Error-Bounded Image Compositing Yongfeng Qiu, Yuxiao Li, Xin Liang, Yafan Huang, Guanpeng Li, Sheng Di, Franck Cappello, Hanqi Guo |
| 11:36am | Managing Data for Scalable and Interactive Event Sequence Visualization Sayef Azad Sakin, Katherine E. Isaacs |
| 11:48am | Out of Core and Adaptive Image Blending Approach for Large Scale Image Mosaics Marcus Quincy, Steve Petruzza |
| 12:00pm | Free Lunch in In Situ Visualization: Leveraging Idle CPU Resources to Mitigate GPU Contention Victor A. Mateevitsi, Andres Role Sewell, Jens Henrik Göbbert, Mathis Bode, Paul Fischer, Joseph Insley, Ioannis Kavroulakis, Damaskinos Konioris, Yu-Hsiang Lan, Misun Min, Dimitrios Papageorgiou, Steve Petruzza, Silvio Rizzi, Ananias Tomboulides, Michael E. Papka |
| 12:15pm – 12:30pm |
Best paper award ceremony and closing remarks |
Emerging Topics in AI-Assisted Visualization of Large-Scale Scientific Data
Kwan-Liu Ma, University of California, Davis, USA
Advancing scientific understanding from large-scale simulations and high-throughput experiments requires transforming massive, high-dimensional outputs into forms that are both manageable and interpretable. While AI and machine learning are increasingly integral to the scientific discovery pipeline, visualization remains a uniquely powerful medium for synthesizing information, revealing patterns, and conveying critical insights. In this talk, I will present a few AI-assisted data reduction and visualization methods that enable faster, more reliable, and more effective exploration and validation of large, complex data. I will also highlight emerging research directions pointing toward the next generation of visualization systems designed to accelerate scientific discovery.
Kwan-Liu Ma is a distinguished professor of computer science at the University of California, Davis, where he leads the VIDI Research Group. Before joining UC Davis, he was a staff scientist at ICASE of the NASA Langley Research Center (1993-1999). Professor Ma received his PhD degree in computer science from the University of Utah in 1993. His research interests include visualization, computer graphics, human computer interaction, and high-performance computing. For his significant research accomplishments, Professor Ma has received many recognitions, including the NSF PECASE award in 2000, elected IEEE Fellow in 2012, the IEEE VGTC Visualization Technical Achievement Award in 2013, inducted into the IEEE Visualization Academy in 2019, and elected ACM Fellow in 2023. He has served as papers co-chair for SciVis, InfoVis, EuroVis, PacificVis, and Graph Drawing, and on the editorial board of IEEE TVCG (2007-2011), IEEE CG&A (2007-2019), and ACM TiiS (2021-present). Professor Ma presently serves on both the IEEE VIS Steering Committee and IEEE PacificVis Steering Committee.