Okada has broad research interests in the areas of intelligent computing: computer
vision, pattern recognition, machine learning, artificial intelligence and
data mining. He has been active in the research fields of medical image
analysis, statistical data analysis, cognitive vision and face recognition.
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 patents.
has received the Ph.D. and M.S. degrees 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. He is currently an associate professor of computer science at San
Francisco State University and leads Biomedical Image & Data Analysis
Lab (BIDAL). Prior to his academic appointment, he was a research scientist
at Siemens Corporate Research in Princeton, NJ. He is a member of IEEE,
ACM, SPIE and MICCAI.
Biomedical Image &
Data Analysis Lab
CSC872 Pattern Analysis
and Machine Intelligence
Biomedical Imaging & Analysis
CSC101 Computers For