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Prof. Dr. Sebastian Vollmer

  • Prof. Dr. Sebastian Vollmer

    Sebastian Vollmer is a professor of applications of machine learning at the University of Kaiserslautern–Landau (RPTU) and the German Research Center for Artificial Intelligence (DFKI). He is also an associate professor in statistics and mathematics in the Mathematics Insitute and the Department of Statistics at the University of Warwick, UK, former director of Data Study Groups at the Alan Turing Institute and a co-director of the Health and Medical Sciences research programme.

    His research interests lie at the interface of computational statistics and machine learning, with a particular focus on real-world applications. He has worked in application areas ranging from transport, energy, and in particular health and well-being.

    Before joining the University of Warwick, Sebastian Vollmer was a lecturer in Statistics at the University of Oxford. Prior to this, he undertook postdoctoral research with Arnaud Doucet (Oxford) and Yee Whye Teh (Oxford). He completed his PhD in September 2013 in Mathematics at the University of Warwick, supervised by Andrew Stuart and Martin Hairer.

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Basic Research

  • Sebastian Vollmer and his group are interested in scalable methods for statistical inference and machine learning and its applications, including efficient use of 'small data' through effective use of prior knowledge, (Bayesian) active learning, foundation models and transfer learning.

    A particular focus is on the analysis of time-to-event or survival data and the use of spatio-temporal models, as well as active learning in digital health applications, the integration of prior biological knowledge into deep learning models, and development and evaluation of agentic AI systems for knowledge discovery.

Application related Research

  • As part of the curATime project, Sebastian Vollmer's group contributes to the development of cutting-edge methods for the discovery of biomarkers and drug targets in cardiovascular disease research, alongside pioneering work in development of biologically-informed methods for lung cancer relapse prognosis from high-dimensional, heterogeneous multi-omics datasets for the MIRACLE consortium.

    The AI4Nof1 project leverages cutting edge developments in (causal, neurosymbolic) reinforcement learning, psychometrics and digital epidemiology to build adaptive personalised treatment regimes for chronic conditions, simultaneously identifying phenotypes and causal pathways while minimising the time and number of measurements needed for patients to find a treatment that is right for them. The project combines active learning and Bayesian modelling with mobile health technology to facilitate simultaneous tailoring of treatments and discovery of population-level knowledge.

    The TrustifAI project aims to contribute a set of concrete solutions to improve trustworthiness of AI applications in health and wellbeing at various stages of development lifecycle. A quality platform for development of trustworthy AI applications enables users to create efficient and effective data science analytics pipelines through a human-in-the-loop approach with the goal of increasing trustworthiness.

    Following the Data Science for Social Good initative, the group collaborates with the German Red Cross (DRK) on reducing ambulance response times and with various government and non-government agencies to increase transparency in operations.

Special Expertise

  • Basic Research

    • Machine Learning (ML): Representation Learning, Transfer Learning, Domain Adaptation, Artificial Neural Network (ANN), Gaussian Processes, Reinforcement Learning (RL)
    • Knowledge-Based Systems: Reasoning, Causality, Agent Systems
    • Technology Analysis: Social and Legal Framework

  • Application related Research

    • Smart Assistant Systems: Predictive Analysis, Digital Medicine, Biotechnology (Biotech)
    • Autonomous Systems
    • Perception and Sensor Fusion: Diagnostics
    • Information Retrieval (Knowledge / Data Management and Analysis)
    • Technology Analysis: Initial and Continuing Education

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