Prof. Dr. Marius Kloft

  • Prof. Dr. Marius Kloft

    Marius Kloft is a professor of machine learning and deep learning at TU Kaiserslautern. He is an expert on unsupervised deep learning (particularly anomaly detection and deep generative models), multi-label classification, transfer learning, adversarial learning, explainable AI, and integration of heterogeneous data types in machine learning. He received the Google Most Influential Papers Award, the DFG Emmy-Noether Career Award, and the ANDEA Test-of-time Award for the most influential paper in anomaly detection in the last ten years (2012-2022). His award-winning paper 'Deep One-class Classification' was the first paper published at a top-tier machine-learning venue that uses deep learning for anomaly detection, a field now known as deep anomaly detection. Marius Kloft is spokesperson of the DFG Research Group FOR 5359 'Deep learning on sparse chemical process data'.


Basic Research

  • Marius Kloft and group are interested in theory and algorithms of statistical machine learning (especially deep learning) and its applications. Their research covers a broad range of topics and applications, unifying theoretically proven approaches (e.g., based on learning theory) with recent advances (e.g., in deep learning or reinforcement learning). Topics they 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. Kloft has been serving as associate editor for journals (JMLR, TNNLS) and area chairs of conferences (AAAI, AISTATS, and ECML). Marius Kloft is spokesperson of the DFG FOR 5359 and co-organizer of the SPPs 2331 and 2364.

Application related Research

  • Marius Kloft has been working on applications of machine learning and deep learning in computer vision, natural language processing, computer security, plant research and agriculture, biomedical applications, mechanical engineering, and chemical process engineering. He co-developed the REMIND intrusion detection system and the CLEF 2012 Photo Annotation Challenge-winning image categorization system. As a former postdoc at Sloan-Kettering Cancer Center in New York, he is experienced in applications in the biomedical domain and oncology (e.g., analysis of histopathological, CT, and NMR data using deep learning and GWAS). Since joining TU Kaiserslautern in 2017, he has become increasingly engaged in mechanical and chemical engineering applications. For instance, he is the spokesperson of the DFG research group FOR 5359 ("Deep learning on sparse chemical process data"), the Carl-Zeiss project "Process Engineering 4.0", and he is a co-organizer of the DFG SPPs 2331 and 2364 at the interface of machine learning and chemical engineering. He is currently the primary PhD advisor of students at BASF and Bosch AI.

Activities as Editor

    • Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (Core A*), since 2020
    • Area Chair, AAAI Conference on Artificial Intelligence (Core A*), 2022 und 2022
    • Senior Area Chair, AAAI Conference on Artificial Intelligence (Core A*), 2020
    • Area Chair, International Conference on Artificial Intelligence and Statistics (Core A), 2020–2022
    • Area Chair, European Conference on Machine Learning (Core A), 2019, 2021, und 2022
    • Workshop Selection Committee Member, NeurIPS (Core A*), 2020

Activities as a Reviewer

    • Editorial Board Member, Transactions on Machine Learning Research, since 2022
    • Editorial Board Member, Machine Learning Journal (Core A), 2020
    • Program committee membership or reviewer for >30 journals and >50 scientific conference 
    • Regular reviewer for DFG proposals (including in-kind grants and research groups) for multiple committees (computer science, mathematics, manufacturing, process engineering)
    • Regular reviewer for DAAD applications (scholarships)
    • Reviewer for ERC Consolidator Grants

Existing Memberships

    • IEEE, Senior Member
    • GI, Member

Received Awards, Prizes, Honors

    • Google Most Influential Papers Award (2013)
    • DFG Emmy-Noether Career Award (2014)
    • ANDEA Test-of-time Award (2022)

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

  • 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
    • Image Recognition and Understanding
    • Perception and Sensor Fusion: Non-Destructive Testing
    • Virtual and Augmented Reality (AR): Simulation of Manufacturing Processes
    • Information Retrieval (Knowledge / Data Management and Analysis)
    • Technology Analysis: Technology Assessment

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