Fumie Costen

Fumie Costen is a Lecturer in Electrical Engineering at The University of Manchester. Her research interests include computation in electromagnetics, Ultra-WideBand systems, and machine learning applications in medical imaging and clinical data analysis. She has supervised several PhD students and is involved in various research projects related to high-speed computational modeling and advanced telecommunications. Her academic background includes a Doctor of Science and Master of Engineering from Kyoto University, with a focus on high-speed computational modeling and image reconstruction. She is also affiliated with the School of Electrical and Electronic Engineering and has contributed to research on topics such as time domain processing and boundary conditions.

Fumie Costen is a Lecturer at the University of Manchester, specializing in Maxwell's equation solver and high performance computing. Her research focuses on developing efficient numerical methods for solving electromagnetic problems, including finite difference time domain (FDTD) techniques. She has contributed to the advancement of FDTD methods for dispersive media, uncertainty quantification, and time-reversal imaging. Her work also explores parallel computing and adaptive algorithms to enhance computational efficiency. Costen's research includes applications in medical imaging, wave modeling, and subcell modeling for frequency-dependent media. Her publications highlight the integration of deep learning approaches in visual question answering and the use of conformal models in dosimetry.