Trustworthy AI Lab @ Ruhr West University of Applied Sciences

Prof. Dr. Anselm Haselhoff
Vehicle Information Technology, Machine Learning, Computer Vision
Office: 01.208
Lab: 04.104
Phone: +49 208 88254-829
Mail: anselm.haselhoff@hs-ruhrwest.de


Anselm Haselhoff is a Professor for Vehicle Information Technology at the Computer Science Institute of Ruhr West University of Applied Sciences. He completed his Bachelor’s and Master’s degrees in Information Technology at the University of Wuppertal, with additional studies at Queensland University of Technology. Professor Haselhoff’s PhD research, supervised by Prof. Dr. Anton Kummert, focused on machine learning and computer vision for vehicle perception.

Currently leading the Trustworthy AI Laboratory at Ruhr West University of Applied Sciences, he has also had the opportunity to collaborate with leading researchers globally, including a stint as a visiting researcher at Sydney AI Center (Director Tongliang Liu) at the University of Sydney. His professional journey includes practical industry experience at Delphi Deutschland GmbH (now Aptiv PLC), where he contributed to advancements in computer vision for autonomous vehicles. 

Professor Haselhoff’s research interests encompass machine learning, deep learning, autonomous driving, and computer vision, specifically targeting areas such as:

  • explainable machine learning,
  • uncertainty quantification and calibration,
  • probabilistic and generative models,
  • sensor data fusion,
  • object detection and tracking.

Quantifying Local Model Validity using Active Learning
Publications

Quantifying Local Model Validity using Active Learning

Machine learning models in real-world applications must often meet regulatory standards, requiring low approximation errors. Global metrics are too insensitive, and local validity checks are costly. This method learns model error to estimate local validity efficiently using active learning, requiring less data. It demonstrates better sensitivity to local validity changes and effective error modeling with […]

Segmentation-guided Domain Adaptation for Efficient Depth Completion
Publications

Segmentation-guided Domain Adaptation for Efficient Depth Completion

Complete depth information and efficient estimators have become vital ingredients in scene understanding for automated driving tasks. A major problem for LiDAR-based depth completion is the inefficient utilization of convolutions due to the lack of coherent information as provided by the sparse nature of uncorrelated LiDAR point clouds, which often leads to complex and resource-demanding […]

Confidence calibration for object detection and segmentation
Publications

Confidence calibration for object detection and segmentation

Calibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis. However, although the task of confidence calibration has been investigated on classification problems, thorough investigations on object detection and segmentation problems are still missing. Therefore, we focus on the investigation of confidence calibration for […]

Bayesian Confidence Calibration for Epistemic Uncertainty Modelling
Publications

Bayesian Confidence Calibration for Epistemic Uncertainty Modelling

Modern neural networks have found to be miscal- ibrated in terms of confidence calibration, i.e., their predicted confidence scores do not reflect the observed accuracy or precision. Recent work has introduced methods for post-hoc confidence calibration for classification as well as for object detection to address this issue. Especially in safety critical applications, it is […]

Multivariate Confidence Calibration for Object Detection
Publications

Multivariate Confidence Calibration for Object Detection

Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates. Though there is a variety of methods for classification, the field of object detection has not been addressed yet. Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence […]

Dependency Decomposition and a Reject Option for Explainable Models
Publications

Dependency Decomposition and a Reject Option for Explainable Models

Deploying machine learning models in safety-related domains (e.g. autonomous driving, medical diagnosis) demands for approaches that are explainable, robust against adversarial attacks and aware of the model uncertainty. Recent deep learning models perform extremely well in various inference tasks, but the black-box nature of these approaches leads to a weakness regarding the three requirements mentioned […]