Scholar
Alexandru Paul Condurache
Google Scholar ID: UTQb4jIAAAAJ
University of Luebeck / Robert Bosch GmbH
Citations & Impact
Citations
907
H-index
12
i10-index
17
Publications
20
Co-authors
14
list available
Contact
No contact links provided.
Publications
Resume (English only)
Academic Achievements
- Involved in multiple research projects including Autonomous Driving and Driver Assistance, Biometric Authentication, Surveillance and Event Detection, Feature Space and Discriminant Analysis, Medical Image Analysis, Computer Vision Applications in Biomedical Engineering, Automated Visual Inspection, and Computer Assisted Quality Control.
Research Experience
- Was with the Institute for Signal Processing, University of Lübeck, as a Research Associate from 2002 to 2013, where besides teaching duties, he oversaw several cooperation projects in the fields of medical and industrial machine vision; until 2007 was a doctoral student, specializing in medical image analysis; afterwards conducted postdoctoral research in information forensics (biometric authentication, surveillance, and event detection) and discriminant feature analysis (unsupervised representation learning and classifier design); now with Robert Bosch GmbH and since 2019 Guest Lecturer at the University of Lübeck, leading a research group on efficient machine learning for robotics, focusing particularly on Autonomous Driving and Driver Assistance Systems.
Education
- Received the Dipl.-Ing. degree in electrical engineering from the Politehnica University of Bucharest, Romania in 2000, awarded the Werner von Siemens Excellence Award for his diploma thesis; obtained the Diploma of Advanced Studies in biomedical engineering also from the Politehnica University of Bucharest, Romania, in 2001; received the Dr.-Ing. degree in computer science from the University of Luebeck, Germany in 2007, supervised by Prof. Til Aach; habilitated in computer science at the University of Luebeck in 2014.
Background
- Research interests include improving generalization under constraints such as limited labeling effort, scarce data availability, and restricted model capacity, as well as resource-efficient training and inference. Specializes in Autonomous Driving and Driver Assistance Systems.
