High-resolution, large-scale image data play a central role in biomedical researches, but they also pose very 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 and distributed cluster systems, can be a solution for such computation-demanding tasks due to its scalable and parallel architecture. In addition, recent advances in machine learning can be another solution because the learning-based approach can accelerate computation 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 recent development on large-scale biomedical image analysis using high-performance computing and machine learning techniques, such as cellular-level connectomics image analysis and compressed sensing MRI reconstruction.

Bio: Dr. Won-Ki Jeong, Associate Professor at Ulsan National Institute of Science and Technology (UNIST), School of Electrical and Computer Engineering, South Korea. Currently Visiting Associate Professor at Harvard Medical School, Department of Neurobiology, USA.