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Head of the Research Data Science and its Applications

Prof. Dr. Sebastian Vollmer

Contact +49 631 20575 7601 (Kaiserslautern)

https://dsa.dfki.de
Address (Kaiserslautern) Trippstadter Straße 122D-67663 Kaiserslautern
Prof. Dr. Sebastian Vollmer

Publications

Gerrit Großmann; Sumantrak Mukherjee; Sebastian Vollmer

In: Jianwu Wang; Sahara Ali; Yanan Xin (Hrsg.). 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM SIGSPATIAL International Workshop on Spatiotemporal Causal Analysis (STCausal-2024), 1st ACM SIGSPATIAL International Workshop on Spatiotemporal Causal Analysis, located at 32nd ACM SIGSPATIAL, October 29 - November 1, Atlanta, GA, USA, ISBN 979-8-4007-1154-1/24/10, Association for Computing Machinery, New York, NY, United States, 11/2024.

To the publication

Oliver Zielinski; Christiane Plociennik; Sebastian Vollmer

In: Nachhaltige Industrie, Vol. 4, Pages 1-7, Springer, Wiesbaden, 4/2022.

To the publication

Profile

Employment

  • Since 2021
    Professor for Application of Machine Learning at University Kaiserslautern-Landau (RPTU)
  • Since 2021
    Senior Researcher DFKI – Research Department Leader from 1.11
  • Since 2016
    University of Warwick
    Associate Professor at the Mathematics Institute and the Department of Statistics (tenured)
    setting research agendas, and delivering courses, staying across current academic research in data and statistics and supervising PhD and MSc students.
  • 2016-2021
    Alan Turing Institute

    1. Director for Data Study Groups since August 2017
    2. Lead for the Data Science for Social Good initiative since September 2018
    Co-Director for the Health & Medical Sciences Programme from August 2018
  • 2017-2018
    Public International Limited

    Senior Consultant helping start-ups to transform the public sector
    As part of this role, I advised the data strategy and technical questions for start-ups including Cera Care, Fly Notes and Rota Geek. Additionally, we delivered a training programme to CQC to upskill their staff on machine learning.
  • 2014-2016
    University of Oxford

    Departmental Lecturer at the Department of Statistics
    Junior Research Fellow Somerville College
  • 2013-2014
    University of Oxford

    Postdoc with Professor Arnaud Doucet and Professor Yee Whye Teh on Bayesian Inference for Big Data with Stochastic Gradient Markov chain Monte Carlo

Education

  • 2010-2013
    PhD in mathematics, University of Warwick

    ERC-studentship under the supervision of Professor Andrew Stuart and Professor Martin Hairer
    Title of PhD-thesis: “Consistent and Efficient Inference for Complex Models”
  • 2009-2010
    MSc in mathematics with distinction, University of Warwick

    due to excellent performance at undergraduate level at Georg-August-Universität Göttingen, Germany - no BSc required
  • 2014 Faculty prize of the University of Warwick for the best PhD thesis in mathematics
  • 2009-13 Scholarship of the German Academy Foundation
  • 2009 3rd prize at the International Mathematics Competition at university level in Bulgaria
  • Eventful

    timely models for individual & societal health

    This project focusses on the interconnected problems of measuring and predicting health and well-being. Health and well-being exist at two levels: within the individual, where we may observe or…

  • curAIknow

    curATime knowledge graphs

    Application of the Ontosight bioscience knowledge graph to support prediction, validation and better interpretation of Al-generated hypotheses based on data from patients and healthy controls in the…

  • Miracle2

    A Machine learning approach to Identify patients with Resected non-small-cell lung cAnCer with high risk of reLapsE

    MIRACLE comprises a consortium of academics and industry partners to understand how machine learning can be utilised to predict the risk of relapse for patients with resected non-small cell lung…

  • CurAISciD

    curATime AI science and development

    Das erste Ziel von curAIscid ist für den Anwendungsfall Atherothrombose und Atherosklerose dem Problem der kleinen Daten (small data) auf verschiedene Weise zu begegnen: durch Transferlernen (bei dem…

  • BiosignATure

    curATime: Systemorientierte, Multi-Omics-Identifikation von Biomarkersignaturen für die Detektion, Quantifizierung und Behandlung von Atherothrombose

    curATime: Systemorientierte, Multi-Omics-Identifikation von Biomarkersignaturen für die Detektion, Quantifizierung und Behandlung von Atherothrombose

    Partners

    Universitätsmedizin der Johannes…