Dr.-Ing. Marco Rudolph
Leibniz Universität Hannover
Institut für Informationsverarbeitung
Appelstr. 9A
30167 Hannover
phone: +49 511 762-5326
fax: +49 511 762-5333
office location: room 1330

Marco Rudolph studied computer science at the Leibniz University Hannover. In 2016 he was awarded by the university the 'Preis des Präsidiums' for his special academic archievements. He attained the master degree in October 2018. In his thesis 'Coding of Human Body Shapes using Neural Networks' he dealt with autoencoders on body meshes.

In November 2018 he started at the TNT to work towards his PhD. The main focus is on image-based anomaly detection for industrial defect detection. This is mostly based on statistical modeling of image features with normalizing flows. Based on his work in this area, he gave a keynote for the workshop "Industrial Machine Learning" at ICPR 2022.  As secondary research areas, he deals with human pose estimation and interpretable machine learning.

Mr. Rudolph is also active in teaching for the TNT for several years. In 2021, he was nominated by the Faculty of Electrical Engineering and Computer Science of the LUH for the "APFEL" award for excellent teaching for his supervision of the course "Machine Learning". Furthermore, he has supervised more than 10 final theses and student projects.

Other activities

Supervised Theses

Human Motion

  • Deep-Learning-based probabilistic 3D Human Pose Estimation (later accepted to ICCV 2021)
  • Deep-Learning-based Completion of Human Motion
  • Baby Motion Capture
  • Human Shape Estimation using Sparse Data

Anomaly Detection

  • Deep-Learning-based Anomaly Detection for Robot Applications using Machine Data
  • Feature Extraction for Deep-Learning-Based Anomaly Detection
  • Active Learning for Deep-Learning-based Anomaly Detection
  • Machine Learning for Optical Defect Detection in Industrial Production of Chassis Components


  • Enhancement of Graph Neural Networks for Session-based Recommender Systems
  • Visualization and Analysis Tools for Normalizing Flows
  • Development of an Image-Based System for Object Detection with Semi-automatic Labeling of Training Data
  • Retrospective Estimation of Head Motion from Structural MRI