Welcome to the Data Science for Engineering group

The Data Science for Engineering group focuses on the development of data-driven and machine learning methods in the context of engineering. In particular, we address the question how data from various sources may be used for the analysis, real-time control and optimization of complex systems. The latter case also includes the simultaneous treatment of multiple conflicting objectives such as maximizing the productivity while minimizing the consumption of resources.


Our research focuses on the following areas

1. Data-driven model reduction, optimization and control of complex dynamical systems

  • Koopman Operator-based approaches
  • Machine learning for prediction and control
  • Reinforcement learning

2. Multiobjective optimization

  • Acceleration of algorithms by structure exploitation
  • Efficient training algorithms for deep learning
  • Applications in the area of machine learning (e.g., sparse regression & neural network training)
  • Efficient solution of expensive problems using model order reduction

About us


We are an interdisciplinary team of computer scientists, mathematicians and engineers performing data science research in the context of complex engineering systems


We offer different data-science and ML-oriented courses for computer scientists and students from related fields.


Our research projects range from basic research to applications of data-driven and machine learning methods to complex engineering systems.



SAIL is an interdisciplinary and inter-institutional collaboration of Bielefeld University, Paderborn University, Bielefeld University of Applied Sciences and Arts (HSBI) and OWL University of Applied Sciences (TH OWL), funded by MKW NRW.


The Institute for Photonic Quantum Systems establishes a strategic cooperation model for interdisciplinary research and development of new quantum technologies. It bundles expertise from Paderborn University from the profile areas "Intelligent Technical Systems" and "Optoelectronics and Photonics".


The SICP - Software Innovation Campus Paderborn at Paderborn University is an interdisciplinary research and innovation network in which companies and science jointly research and implement digital innovations.

CoAI: Cooperative and Cognition-enabled AI

The Joint Research Center on Cooperative and Cognition-enabled AI is Germany’s leading center for interdisciplinary research and innovation on cooperative and cognition-enabled AI that features human-centered, embodied, and real-world agency in the sense that it is capable of acting together with humans in a meaningful and goal-directed manner.



Editor's choice award of the Journal Automatica

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New paper in the SIAM Journal on Scientific Computing

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New conference paper (NOLCOS)

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New preprint on sample efficiency in reinforcement learning for partial differential equations

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New preprint on convolutional reinforcement learning

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New preprint on error bounds for kernel-based Koopman operator approximations

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New paper in Automatica

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New paper in Journal of Nonlinear Science

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New AI Junior Research Group on Multicriteria Machine Learning

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New paper in Journal of Global Optimization

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New preprint on surrogate-based multiobjective optimization algorithms with nonlinear constraints

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New preprint on fast multiobjective gradient methods with Nesterov acceleration

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More news

Jun.-Prof. Dr. Sebastian Peitz

Data Science for Engineering

Room O4.213
Paderborn University
Pohlweg 51
33098 Paderborn