报告人：Dr. Shuo Li 教授，加拿大西安大略大学教授
报告题目：Innovative Machine Learning for Medical Data Analytics
报告摘要：Medical data analysis is going through great changes with tremendous new opportunities showing up. The rise of machine learning and the rise of big data analytics, have brought wonderful opportunities to invent the new generation of machine learning tools for medical data analytics, not only to solve new problems appearing, but also to solve many years challenges in conventional medical image analysis and computer vision with much more satisfactory solutions. This talk will share our experience in developing the next generation of image analytics tools with newly invented machine learning tools to help physicians, hospital administrative to analyze the huge growing medical data and help them to make the right decision and early decision at the right timing.
Dr. Shuo Li is an associate professor in the department of medical imaging and medical biophysics at the University of Western Ontario and scientist at Lawson Health Research Institute. Before this position he was a research scientist and project manager in General Electric (GE) Healthcare for 9 years. He founded the Digital Imaging Group of London (http://digitalimaginggroup.ca/) in 2006, which is a very dynamic and highly multiple disciplinary collaboration group. He received his Ph.D. degree in computer science from Concordia University 2006, where his PhD thesis won the doctoral prize giving to the most deserving graduating student in the faculty of engineering and computer science. He has published over 100 publications; He is the recipient of several awards from GE, institutes and international organizations; He serves as guest editors and associate editor in several prestigious journals in the field; He serves as program committee members in highly influential conferences; He is the editors of six Springer books; He serves on the board of directors in prestigious MICCAI society. His current interest is development intelligent analytic tools to help physicians and hospital administrators to handle the big medical data, centred with medical images.