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Machine Learning Group, Department of Computer Science (DCS)

  • Logo Machine Learning Group, Department of Computer Science

    The Machine Learning Group at RPTU (then: TU Kaiserslautern) was established in 2017. The group currently comprises 2 professors, 1 postdocs, 18 PhD students, 3 administrative / technical support staff and 13 student assistants. The group is interested in theory and algorithms of statistical machine learning (especially deep learning) and its applications. Our research covers a broad range of topics and applications, where we try to unify theoretically proven approaches (e.g., based on learning theory) with recent advances (e.g., in deep learning or reinforcement learning). Topics we have been working on include unsupervised deep learning (particularly, anomaly detection), multi-modal learning, extreme classification, adversarial learning, explainable AI, and applications of ML in the life sciences, mechanical and chemical process engineering, and text analysis. Members of the group have received various awards, including the Google Most Influential Papers Award, the ICML and NIPS Best Reviewer Awards, the ANDEA Test-of-time Award, and the Emmy-Noether Career Award (DFG). The group contributes the community with helpful service. Members of the group have been reviewing for more than 50 conferences and 30 journals. They have been serving as associate editors for journals (JMLR, TNNLS) and area chairs of conferences (AAAI, AISTATS, and ECML). The group is committed to improving the diversity, equity, and inclusion in the field of ML. This is evidenced by the high percentage of women in the group, on all levels of academic qualification.


    Address

    University of Kaiserslautern-Landau
    Department of Computer Science
    Machine Learning Group
    Building 36, Room 325
    Paul-Ehrlich-Straße 36
    67663 Kaiserslautern
    0631 205 2635, 0631 205 3286
    ml@cs.uni-kl.de
    https://ml.cs.rptu.de/

Leading Researchers



Special Expertise

  • Basic Research

    • Machine Learning (ML): Representation Learning, Zero-Shot/One-Shot/Few-Shot Learning, Self-Supervised Learning, Adversarial Learning, Reinforcement Learning (RL), Unsupervised Learning, Anomaly Detection, Density Estimation, Feature Engineering/Feature Extraction
    • Robotics: Sensory Acquisition and Perception
    • Technology Analysis: Economic Effects, Human-AI Interaction

  • Application related Research

    • Smart Assistant Systems: Virtual Assistants, Predictive Analysis, Predictive Maintenance (PM), Smart Service Engineering, Digital Twins, Digital Medicine, Digital Farming, Smart Production, Biotechnology (Biotech)
    • Autonomous Systems: Smart Automation
    • Robotics: Industrial Robots
    • Image Recognition and Understanding
    • Perception and Sensor Fusion: Non-Destructive Testing
    • Virtual and Augmented Reality (AR): Simulation of Manufacturing Processes, Assistance Systems at the Workplace
    • Information Retrieval (Knowledge / Data Management and Analysis)
    • Technology Analysis: Technology Assessment

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