High-performance visual computing for large-scale biomedical image analysis

Speaker: Prof. Won-Ki Jeong

Keynote Speaker: Prof. Won-Ki Jeong

High-resolution, large-scale image data play a central role in biomedical research, but they also pose challenging computational problems for image processing and visualization in terms of developing suitable algorithms, coping with the ever-increasing data sizes, and maintaining interactive performance. Massively parallel computing systems, such as graphics processing units (GPUs) and distributed cluster systems, can be a solution for such computation-demanding tasks due to their scalable and parallel architecture. In addition, recent advances in machine learning can be another solution by shifting the time-consuming computing process into the training (pre-processing) phase and reducing prediction time by performing only one-pass deployment of a feed-forward neural network. In this talk, I will introduce several examples of such research directions from our work on large-scale biomedical image analysis using high-performance computing and machine learning techniques, for example, how to leverage parallel computing architecture and machine learning algorithms to accelerate tera-scale microscopy image processing and analysis for biomedical applications.


Won-Ki Jeong is currently a full professor in computer science and engineering at Korea University. He was an assistant and associate professor in the school of electrical and computer engineering at UNIST (2011-2020), a visiting associate professor of the neurobiology at Harvard Medical School (2017–2018),and a research scientist in the Center for Brain Science at Harvard University (2008–2011). His research interests include visualization, image processing, and parallel computing. He received a Ph.D. degree in Computer Science from the University of Utah in 2008 where he was a member of the Scientific Computing and Imaging (SCI) institute. He hosted the NVIDIA GPU Research Center at UNIST in 2014. He co-authored chapters in GPU Gems published in 2011 and published more than 60 refereed research articles.


Paper Types:
(F) : Full Paper
(S) : Short Paper

All sessions use the CEST (UTC+2) timezone.

9:00 - 9:25


9:25 - 10:40


Won-Ki Jeong

11:00 - 11:25

HyLiPoD: Parallel Particle Advection Via a Hybrid of Lifeline Scheduling and Parallelization-Over-Data (S)

Roba Binyahib, David Pugmire, and Hank Childs

11:25 - 11:55

Machine Learning-Based Autotuning for Parallel Particle Advection (F)

Samuel D. Schwartz, Hank Childs, and David Pugmire

11:55 - 12:25

Scalable In Situ Computation of Lagrangian Representations via Local Flow Maps (F)

Sudhanshu Sane, Abhishek Yenpure, Roxana Bujack, Matthew Larsen, Kenneth Moreland, Christoph Garth,Chris R. Johnson, and Hank Childs

14:00 - 14:25

Evaluation of PyTorch as a Data-Parallel Programming API for GPU Volume Rendering (S)

Nathan X. Marshak, A. V. Pascal Grosset, Aaron Knoll, James Ahrens, and Chris R. Johnson

14:25 - 14:55

Faster RTX-Accelerated Empty Space Skipping using Triangulated Active Region Boundary Geometry (F)

Ingo Wald, Stefan Zellmann, and Nate Morrical

14:55 - 15:20

Performance Tradeoffs in Shared-memory Platform Portable Implementations of a Stencil Kernel (S)

E. Wes Bethel, Colleen Heinemann, and Talita Perciano

16:00 - 16:30

UnityPIC: Unity Point-Cloud Interactive Core (F)

Yaocheng Wu, Huy Vo, Jie Gong, and Zhigang Zhu

16:30 - 17:00

Interactive Selection on Calculated Attributes of Large-Scale Particle Data (F)

Benjamin Wollet, Stefan Reinhardt, Daniel Weiskopf, and Bernhard Eberhardt

17:00 - 17:25