Yoshinobu Kawahara is an associate professor at The Institute of Scientific and Industrial Research (ISIR), Osaka Univeristy (Osaka, Japan).
He received his Ph.D. degree from The University of Tokyo in March, 2008.
His primary research interests lie in combinatorial methods for machine learning and statistical modeling for time-series data.
I am particularly interested in submodularity in machine learning, learning dynamical systems, statistical causal analysis, change-point detection in time-series data, bioinformatics and data-mining for engineering systems.
- Our paper titled "A fault detection technique for the steel manufacturing process based on a normal pattern library" has been accepted to The 9th IFAC Symp. on Fault Detection, Supervision and Safety of Technical Processes (SafeProcess'15) (2015/3/15).
- Our paper titled "On approximate non-submodular minimization via tree-structured supermodularity" has been accepted to the 18th Int'l Conf. on Artificial Intelligence and Statistics (AISTATS'15) (2015/1/11).
- Our paper titled "A novel structural AR modeling approach for a continuous time linear Markov system" has been accepted for publication in ACM Trans. on Intelligent Systems and Technology (ACM TIST) (2014/12/26).
- Our paper titled "New monitoring technique for detecting buckling in the continuous annealing line using canonical correlation analysis" has been accepted for publication in SICE Journal of Control, Measurement, and System Integration (2014/11/18).
- Our paper titled "Toxicity Prediction from Toxicogenomic Data Based on Class Association Rule Mining" has been accepted for publication in Toxicology Reports (2014/10/21).
My past news is also found [here].