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.
(F) : Full Paper
(S) : Short Paper
All sessions use the CEST (UTC+2) timezone.