In the research on evaluating badminton players, used reinforcement learning to quantitatively evaluate the value of badminton strokes, aiming to identify which strokes directly or indirectly lead to points lost or gained during rallies.
Research Experience
Currently, Assistant Professor at the Tamaki Lab, Media Information Field, Graduate School of Engineering, Nagoya Institute of Technology; involved in projects such as estimating control areas in badminton doubles matches using a drone image dataset.
Education
April 2021 - March 2024, Graduate School of Information Science, Nagoya University, Intelligent Systems Major, Takeda Lab, Doctoral Degree; April 2019 - March 2021, Graduate School of Information Production Systems, Waseda University, Information Architecture Major, Furuki Lab, Master's Degree; September 2013 - June 2017, Department of Automation, Guangdong University of Technology, Electronic Information Science and Technology, Bachelor's Degree.
Background
Research Interests: Evaluation and visual analysis of athletes in racket sports. Focuses on deep reinforcement learning for evaluating badminton players considering both technical and tactical contexts.