Teaching
I have been involved in the following courses
- Discrete Mathematics and Mathematical Thinking
- Mathematical Modelling 2 (machine learning, reinforcement learning)
- Multivariable Calculus and Vector Calculus
- Cryptography and Coding Theory
- Technical Information and Communication
- Database Theory (logic and set theory)
- Applied Machine Learning
Research
My research interests are computational algebra, dynamical systems and artificial intelligence. In particular, reinforcement learning which involves maximization of future rewards for an agent acting in an environment. In this regard, I study dynamical systems induced by approximate operators. Iterated convergence here can generate super-human strategies with the core idea of tabula rasa. That is, we ask the machine to find an optimal solution without human interference.
My research groups
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AI and machine learning for optimization and operations research Under this research theme, we develop both mathematical theory and applications for artificial intelligence and machine learning in…
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Computational Mathematics for Predictive Digital Twins (PreDiTwin) In recent years, remarkable progress in mathematics, process-based modeling, data science, and sensor technology has opened up…
My ongoing research projects
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Project: Algorithms for reinforcement learning Reinforcement learning is a hot area in machine learning and artificial intelligence. It is the basis for development in robotics, image recognition,…
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Project: HPC for SME The aim of the project is to provide small and medium-sized enterprises (SMEs) in the Linnaeus region with the opportunity to enhance their data-driven capabilities with the…
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Project: Optimization for sustainable energy supply and food production in rural areas The project is about how energy systems on farms can be optimized to realize the potential that farms have to be…
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Seed project: Machine learning stabilized steady-state advective-diffusive heat transport This seed project aims to explore and use the strengths of Scientific Machine Learning (SciML) to solve the…
Publications
Article in journal (Refereed)
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Lindenberg, B., Nordqvist, J., Lindahl, K. (2020). Distributional reinforcement learning with ensembles. Algorithms. 13 (5). 1-13.
Status: Published -
Lindenberg, B. (2018). Stabilization bounds for linear finite dynamical systems. Journal of Algebra. 511. 516-534.
Status: Published
Conference paper (Refereed)
- Lindenberg, B., Nordqvist, J., Lindahl, K. (2022). Conjugated Discrete Distributions for Distributional Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 7516-7524.
Doctoral thesis, comprehensive summary (Other academic)
- Lindenberg, B. (2023). Reinforcement Learning and Dynamical Systems. Doctoral Thesis. Växjö, Linnaeus University Press. 38.