Kim Bjerge | Signal Processing | Best Researcher Award

Mr. Kim Bjerge, Aarhus University, Denmark.

Kim Bjerge is an Associate Professor at Aarhus University in the Department of Electrical and Computer Engineering, specializing in Signal Processing and Machine Learning. With a Ph.D. focused on Computer Vision and Deep Learning for Insect Monitoring, Kim combines academic expertise with significant industry experience. He has held various teaching and leadership positions at Aarhus University and has contributed to research projects in computer vision. His work has resulted in a notable H-index of 14 and 1080 citations on Google Scholar. Kim is dedicated to advancing technology in engineering education and research.

Publication Profiles

Googlescholoar

Education and Experience

Ph.D. in Computer Vision and Deep Learning for Insect Monitoring (Aarhus University, 2022 – present)
M.Sc. Eng. in Information Technology (Aarhus University, 2013)
B. Eng. in Electronics Engineering (Engineering College of Aarhus, 1989)
Associate Professor and Group Leader (Aarhus University, 2021 – present)
Associate Professor and Group Leader, Signal Processing (Aarhus University, 2009 – 2020)
Senior Consultant, IT-Development (Danish Technological Institute, 2007 – 2009)
Software Development Manager (TC Electronic A/S, 1999 – 2007)
System Developer (Crisplant A/S, 1996 – 1999)
System Manager (Sam-system A/S, 1989 – 1996)

Suitability For The Award

Mr. Kim Bjerge, Associate Professor at Aarhus University’s Department of Electrical and Computer Engineering, is an exemplary candidate for the Best Researcher Award due to his outstanding contributions to computer vision, deep learning, and signal processing. With a remarkable career spanning academia and industry, he has made groundbreaking advancements in the fields of artificial intelligence, embedded systems, and digital signal processing, impacting both research and application development globally.

Professional Development

Kim Bjerge has pursued extensive professional development through various programs. He completed the Pedagogical Programme in Engineering at the Center for Engineering Education Research and Development, earning 10 ECTS credits. Additionally, he participated in project management training at Provinu and various management courses at Aarhus Business College, enhancing his skills in human resources, organizational strategy, and software engineering. His commitment to ongoing learning ensures that he remains at the forefront of engineering education and technology.

Research Focus

Kim Bjerge’s research focuses on the intersection of computer vision, deep learning, and machine learning, particularly in the context of insect monitoring. His work aims to develop innovative solutions that enhance the understanding and management of ecological systems through advanced image analysis and artificial intelligence techniques. By leveraging his expertise in signal processing, he contributes to the development of cutting-edge technologies that have practical applications in various fields, including agriculture and environmental science.

Publication Top Notes

Deep learning and computer vision will transform entomology – Cited by: 362, Year: 2021
Towards the fully automated monitoring of ecological communities – Cited by: 141, Year: 2022
An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning – Cited by: 119, Year: 2021
Real-time insect tracking and monitoring with computer vision and deep learning – Cited by: 110, Year: 2021
A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony – Cited by: 85, Year: 2019
Accurate detection and identification of insects from camera trap images with deep learning – Cited by: 61, Year: 2023
A living laboratory exploring mobile support for everyday life with diabetes – Cited by: 40, Year: 2010
Hierarchical classification of insects with multitask learning and anomaly detection – Cited by: 26, Year: 2023
Enhancing non-technical skills by a multidisciplinary engineering summer school – Cited by: 19, Year: 2017