If you are fascinated with how self-driving cars are able to distinguish a tree from a person, or how self-driving cars are able to distinguish a tree from a person, or how computers are able to recognize your fingerprint or your eyes, come learn about current research in the field of visual perception. While there are many technological advancements in visual perception, much of this research has taken place in static situations. The open, dynamic, vast, and unpredictable world creates many more complexities. Dr. Shu Kong will discuss this topic from the aspects of data distributions, handling the unknowns, visual learning methods, and multimodal perceptions.

About the instructor: Dr. Shu Kong

Shu Kong is a postdoctoral fellow in the Robotics Institute at Carnegie Mellon University. He earned a Ph.D. in computer science at the University of California Irvine, ad his research interests span computer vision and machine learning as it pertains to autonomous vehicles and interdisciplinary research. His latest interdisciplinary research outcome include a high-throughput vision system for pollen analysis, which has been featured by the National Science Foundation for opening a “new era” of fossil pollen research.