The 12th IEEE Symposium on Large Data Analysis and Visualization
in conjunction with IEEE VIS 2022, Oklahoma City, USA (Hybrid),
16-21 October 2022

Program

Sunday, October 16, 2022 (US Central Time)

9:00am – 9:15am Opening Remarks (Paul Navratil)
9:15am – 10:15am

Keynote Presentation

(Session Chair: Chaoli Wang)
Professor Han-Wei Shen, The Ohio State University
Machine Learning for Large Scale Scientific Data Analysis and Visualization
Details
10:15am – 10:45am Break
10:45am – 12:00pm

Session: Parallelization & Progressiveness

(Session Chair: David Pugmire)
  • Hybrid Image-/Data-Parallel Rendering Using Island Parallelism
    Stefan Zellmann, Ingo Wald, Joao Barbosa, Serkan Demirci, Alper Sahistan, Ugur Gudukbay
  • A Prototype for Pipeline-Composable Task-Based Visualization Algorithms
    Marvin Petersen, Kilian Werner, Andrea Schnorr, Torsten Wolfgang Kuhlen, Christoph Garth
  • High-Quality Progressive Alignment of Large 3D Microscopy Data
    Aniketh Venkat, Duong Hoang, Attila Gyulassy, Peer-Timo Bremer, Frederick Federer, Alessandra Angelucci, Valerio Pascucci
12:00pm – 2:00pm Break
2:00pm – 3:15pm

Session: Topology & Ensembles

(Session Chair: Markus Hadwiger)
  • Distributed Hierarchical Contour Trees
    Hamish Carr, Oliver Rübel, Gunther Weber
  • Topological Analysis of Ensembles of Hydrodynamic Turbulent Flows, an Experimental Study
    Florent Nauleau, Fabien Vivodtzev, Thibault Bridel-Bertomeu, Héloïse Beaugendre, Julien Tierny
  • Angular-based Edge Bundled Parallel Coordinates Plot for the Visual Analysis of Large Ensemble Simulation Data
    Keita Watanabe, Naohisa Sakamoto, Jorji Nonaka, Yasumitsu Maejima
3:15pm – 3:45pm Break
3:45pm – 4:50pm

Early Career Researcher Lightning Talks

(Session Chair: Kristi Potter)
  • Nils-Arne Dreier: In-situ analyses and visualization of large climate model data
  • Qi Wu: Instant Neural Representation for Interactive Volume Rendering
  • Yu Qin: Metric Learning for Topological Comparisons
  • Isaac Nealey: In-Transit Methods for Immersive Visualization
  • Cooper Maira: Large Image Viewer for Fast Annotation
  • Dylan Cashman: Senior Expert, Data Science and Advanced Visual Analytics
4:50pm – 5:00pm Closing Remarks (Kristi Potter)

Keynote

Machine Learning for Large Scale Scientific Data Analysis and Visualization
Professor Han-Wei Shen, The Ohio State University, USA

In this talk, I will discuss several of our recent developments on using machine learning for scientific data analysis and visualization. I will primarily focus on three directions that we have seen some promising results: visualization surrogates, latent representations for scientific data, and methods that optimize the scientific visualization pipeline. I will first discuss how visualization surrogates can help streamline the visualization and analysis of large-scale ensemble simulations and facilitate the exploration of their immense input parameter space. Three different approaches for constructing such visualization surrogates: image space, object space, and hybrid image-object space approaches will be discussed. Then I will discuss how neural networks can be used to extract succinct representations from scientific data for rapid exploration and tracking of features. The use of geometric convolution to represent 3D particle data, and how regions of interest can be used as important measures for more efficient latent generation will be discussed. Finally, I will describe how reinforcement learning can be used for automatic load balancing of parallel particle tracing, and how super-resolution architectures can be used for efficient representations of scalar and vector scientific data.

Speaker

Han-Wei Shen is a Full Professor at The Ohio State University. He is a member of IEEE Visualization Academy, an Associate-Editor-in-Chief for IEEE Transactions on Visualization and Computer Graphics (TVCG), and will serve as the Editor-in-Chief for TVCG starting January 2023. His primary research interests are scientific visualization and computer graphics. Professor Shen is a winner of National Science Foundation’s CAREER award and US Department of Energy’s Early Career Principal Investigator Award. He has published more than 50 papers in IEEE Transactions on Visualization and Computer Graphics and IEEE Visualization conference, the very top visualization journal and conference.

Posters