Interactive Intelligent Computing
In this talk, I will introduce a concept of "interactive intelligent computing" which combines the statistical computing theories with the HCI-type of human intervention.
In past decades, we have seen burgeoning of many interesting computing paradigms such as pattern recognition, machine learning, and data mining to name a few. What they all share is their main goal to 1) learn statistical regularity from given data and 2) realize automation of certain computing tasks, such as segmentation, classification, and recognition, using the learned knowledge. Over years, we have seen that rigorous statistical and mathematical theories and algorithms have been developed for addressing these problems and that many have attempted to apply them to solve numerous difficult real-world problems. Today, we are realizing that alas! this paradigm of automation has serious limitations and many practical computing systems are incorporating clever but heuristic user interventions to achieve their end goals. What is really missing is a systematic theoretical and practical treatise in addressing these user factors within the existing computational theories for realizing reusable robust computing solutions.
This talk overviews these ideas by illustrating some examples from my research field: visual information computing, especially medical image analysis. My previous and current studies of one-click data segmentation and registration, conducted at Siemens towards this goal, will be described and demonstrated briefly. I will also describe their foundation: how kernel-based robust statistical theories and algorithms can be applied to and extended for realizing these solutions.
Dr. Okada (http://organic.usc.edu:8376/~kazunori) has been active in the medical image analysis, computer vision, machine learning and other related fields. His earlier work on face recognition has produced a winning system in the well-known FERET competition, setting the industry-standard. His recent work on lung tumor segmentation and detection in chest CT scans has resulted in a number of US, German, Chinese and Japanese pending patents. He is also interested in developing basic technology for representing and comparing heterogeneous data towards bioinformatics and data mining applications. He has received the Ph.D. degree in Computer Science from University of Southern California, and the M.Phil. degree in Human Informatics and the B.Eng. degree in Mechanical Engineering both from Nagoya University in Japan. Prior to his SFSU appointment, he was a research scientist at Siemens Corporate Research in Princeton, NJ.