Lasse Lensu

Lasse Lensu is a Professor of Machine Vision and Data Analysis at Lappeenranta-Lahti University of Technology LUT (LUT University), Finland. He is affiliated with the Department of Computational Engineering within the LUT School of Engineering Sciences. His research spans multiple domains including computer vision, pattern recognition, and machine learning with applications in marine biology, medical imaging, and spectroscopy.

Prof. Lensu received his M.Sc. degree in data communications in 1991, followed by Lic.Sc. and D.Sc. degrees in computer science in 2001 and 2002 respectively, all from LUT. His academic journey has been deeply rooted in the same institution where he now serves as a professor.

His primary research interests focus on computer vision and pattern recognition with machine learning approaches. Specifically, he has made significant contributions to plankton recognition using deep learning techniques, medical image analysis particularly in retinal imaging, and signal processing in Raman and CARS spectroscopies. Prof. Lensu has also contributed to energy forecasting and battery state-of-charge prediction using advanced machine learning models.

Analysis of his recent publications reveals a strong trend toward applying computer vision and deep learning to specialized domains. His work spans from fundamental research in machine learning techniques to practical applications in marine biology (plankton recognition), medical diagnostics (retinal imaging), and chemical analysis (spectroscopy). A significant portion of his recent work focuses on addressing challenges in open-set recognition, domain adaptation, and uncertainty quantification in deep learning models.

Prof. Lensu is an active member of the Computer Vision and Pattern Recognition Laboratory at LUT. He has been instrumental in technology transfer initiatives, contributing to the creation of multiple spin-off companies from the university. His research has practical implications across several industries including environmental monitoring, healthcare diagnostics, and energy management.