Jonathan Weber
Université de Haute-Alsace, Centre de Recherche en Informatique
Papers
3
Total Citations
239
H-Index
3
About
Dr. Jonathan Weber is a leading researcher in surgical data science and deep learning for time series analysis. His primary contributions lie at the intersection of machine learning and surgical skill assessment, where he has pioneered the use of convolutional neural networks (CNNs) to objectively evaluate surgical proficiency from kinematic data. His seminal 2018 work, "Evaluating Surgical Skills from Kinematic Data Using Convolutional Neural Networks" (120 citations), and its 2019 follow-up (105 citations) introduced fully convolutional architectures that provide both accurate and interpretable evaluations, addressing the critical need to replace subjective, costly manual feedback from senior surgeons with automated, objective systems. More recently, Dr. Weber has advanced the field of model efficiency through his 2022 study on knowledge distillation in fully convolutional networks for time series classification, demonstrating how compact models can retain high performance. With over 239 citations across his most-cited works, Dr. Weber’s research is directly impacting surgical training by enabling scalable, data-driven feedback. His work is essential reading for anyone interested in applied deep learning for healthcare, surgical robotics, or interpretable AI in high-stakes environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3