Kaz Okada

Dr. 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.

He 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 Analysis
• Pattern Recognition
• Machine Learning
• Computer Vision

• Ken Fong Translational Research Fund, San Francisco State University (2016), Integrating Grid Sensing and Machine Learning for Neural-Controlled Artificial Arms, role: PI with PI Xiaorong Zhang (funded $20000)
• Center for Advancing Women for Technology (2016), Improving Diversity in the Computing Industry; Attracting Female Biology Students to Computer Science, Role: Co-PI with PI Dr. Yoon and Co-PIs Drs Kulkarni, Pennings and Domingo, Funded $354,450.
• Pilot Research Awards for Faculties of Children’s National and the George Washington University, Clinical and Translational Science Institute at Children’s National (2015), Molecular, Clinical and Imaging Biomarkers of Severity of Viral Respiratory Illness in Children, Role: Consultant, with Co-PI Dr. Gustavo Nino, Co-PI Dr. Marius G. Linguraru. Funded $49,975.
• NSF TUES program (2012-2015), Collaborative Research: Transforming the Understanding, Assessment and Prediction of Teamwork Effectiveness in Software Engineering Education using Machine Learning, role: Co-PI, with PI D. Petkovic and co-PI S. Huang (Florida Atlantic Univ), Total Fund $199,696

• A. Mansoor, G. F. Perez, K. Pancham, A. Jain, K. Okada, M.G. Linguraru, G. R. Nino, (2016), Automated Lung Segmentation and Longitudinal Air Trapping Quantification in Chronic Lung Disease of Prematurity, In proc. American Thoracic Society International Conference, pp A4575, 2016
• D. Petkovic, M. Sosnick-Perez, K. Okada, R. Todtenhoefer, N. Miglani**, A. Vigil**, (2016), Using the Random Forest Classifier to Assess and Predict Student Learning of Software Engineering Teamwork, In proc. Frontiers in Education Conference (FIE), 2016
• I. Donovan*, K. Valenzuela*, A. Ortiz*, S. Dusheko*, H. Jing, K. Okada, X. Zhang, MyoHMI: A Low-Cos and Flexible Platform for Developing Real-Time Human Machine Interface for Myoelectric Controlled Applications, In proc. IEEE Int Conf System, Man and Cybernetics (SMC), 2016
• K. Okada, S. Rysavy*, A. Flores*, MG. Linguraru, Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans, Medical Physics, 42(4): 1653-1665 (2015)