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Prof. Dr. Marius Kloft

  • Prof. Dr. Marius Kloft

    Marius Kloft has been a professor of computer science at RPTU Kaiserslautern-Landau since 2017, where he heads the Machine Learning Group. He is also a faculty member of the International Max Planck Research School on Trustworthy Computing (IMPRS-TRUST) and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). Before joining RPTU he held positions at HU Berlin and the University of Southern California and was a joint postdoctoral fellow at the Courant Institute of Mathematical Sciences and Memorial Sloan-Kettering Cancer Center in New York. He received his PhD from TU Berlin in 2011. Kloft is an expert on unsupervised deep learning (particularly anomaly detection and deep generative models). 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'.

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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, TPAMI, TNNLS) and area chairs of conferences (AAAI, AISTATS, ECML, ICML, ICLR, IJCAI, NeurIPS). 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 has been serving as PhD advisor of students at BASF and Bosch AI.

Activities as Editor

  • Associate Editor, IEEE Transactions on Pattern Analysis and Machine Intelligence (Core A*; Impact Factor: 20.8), since 2023
    Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (Core A*; Impact Factor: 14.26), 2020–2022
    Editorial Board Member, Transactions on Machine Learning Research (TMLR), ongoing
    Action Editor, Journal of Machine Learning Research (JMLR), 2015–2017
    Area Chair, International Conference on Machine Learning (ICML) (Core A*), 2026
    Area Chair, Conference on Neural Information Processing Systems (NeurIPS) (Core A*), 2026
    Area Chair, International Joint Conference on Artificial Intelligence (IJCAI) (Core A*), 2026
    Sister Conferences Track Chair, International Joint Conference on Artificial Intelligence (IJCAI) (Core A*), 2025
    Area Chair, International Conference on Learning Representations (ICLR) (Core A*), 2025 and 2026
    Senior Area Chair, International Conference on Artificial Intelligence and Statistics (Core A), 2024
    Senior Area Chair, AAAI Conference on Artificial Intelligence (Core A*), 2020
    Area Chair, AAAI Conference on Artificial Intelligence (Core A*), 2021, 2022, and 2026
    Area Chair, International Conference on Artificial Intelligence and Statistics (Core A), 2020–2022, 2025, and 2026
    Area Chair, European Conference on Machine Learning (Core A), 2019, 2021, 2024, 2025, and 2026
    Area Chair, European Conference on Artificial Intelligence (ECAI) (Core A), 2024
    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, (Semi) Supervised Learning, One-Shot Learning (OSL)/ Few-Shot Learning (FSL), Adversarial Learning, Reinforcement Learning (RL), Unsupervised Learning, Anomaly Detection, Density Estimation, Feature Engineering/Feature Extraction
    • Robotics: Sensory Acquisition and Perception
    • Technology Analysis: Social and Legal Framework, 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: Sociological Aspects, Technology Assessment, Initial and Continuing Education

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KI-Lernmaterialien

  • AI Insights in Brief: Monthly Background Newsletter from the AI Project Office for SMEs and Transfer Stakeholders
    AI Insights in Brief: Monthly Background Newsletter from the AI Project Office for SMEs and Transfer Stakeholders

    Logo AI Insights in Brief: Monthly Background Newsletter from the AI Project Office for SMEs and Transfer Stakeholders

    Since May 2026, the AI Project Office of the AI Alliance Rhineland-Palatinate,
    in cooperation with the Chair of Machine Learning at RPTU in Kaiserslautern,
    has been providing monthly AI background newsletters.

    The newsletters are aimed in particular at small and medium-sized enterprises as well as transfer stakeholders,
    but are also open to the interested public.

    They provide concise insights into current developments, application potential, and relevant issues
    surrounding artificial intelligence.

    The newsletter issues are available on the project page of the AI Project Office below:

    -- Steffen Reithermann, Managing Director of the AI Project Office of the AI-Alliance Rhineland-Palatinate


    Category Short Overview
    → Link to the Learning Materials