Members

The research of the Chair for Dynamical Systems and Data focuses on the data-driven analysis and forecast of complex (dynamical) systems. One particular aspect is reduced order modeling of such systems by developing new tools on the interface of dynamical systems, machine learning and data science. 

 The group of Prof. Lars Grüne is interested in the area of mathematical systems and control theory, which includes the use of ML techniques such as reinforcement learning. Moreover, They are also interested in the foundations of ML in this field. For instance, They are currently running a research project within the DFG Priority Research Programme 2298 "Theoretical foundations of Deep Learning", which investigates the ability of deep neural networks for the solution of high dimensional feedback control problems.

The group of Prof. Daniel Buschek works at the intersection of Human-Computer Interaction and AI. They empirically explore interaction with AI to empower people in creative tasks and shape the future of AI tools in a human-centred way. They are particularly interested in the future of interaction with text, both constructively by building prototypes (e.g. new document editing software) and critically by assessing the impact of AI on writers, the writing process, and the resulting texts.

The group of Prof. Janosch Hennig applies ML-tools to accelerate their research about transcription and translation regulation by RNA binding proteins. They use all experimental structural biology techniques and biophysics with a focus on NMR spectroscopy. Currently they are collaborating to develop an ML-tool to bring protein NMR to the next level.

The chair of Applied Computer Science 8 investigates applications of computer science in the life sciences. Major research topics are reinforcement learning and optimal control in biomechanical simulation, acoustic levitation, simulation of stem cell dynamics, biomechanical aspects of the interaction of humans with computers, as well as the analysis of fluorescence microscopy data with methods of artificial intelligence.

The chair of Polymer Engineering specializes in practical polymer research, bridging  the gap between science, real-world applications, and technology. Their work spans from fundamental research projects to close collaborations with industry partners. With a comprehensive understanding of processing, structure, and properties, they drive the development of innovative polymer materials and applications. Modern digital technologies enhance the speed and quality of their research, and allow to discover new technologies and materials, taking their research to new heights.

The chair of Physical Chemistry I works on ordered and amorphous functional nanostructures using a wide variety of materials. Their investigations aim to develop new and efficient energy materials that contribute to a sustainable future. They deal with various aspects of "heat" and do basic research in areas such as thermal insulation, thermal management, and thermal radiation. Their research starts with the design of colloidal particles as basic building blocks to create well-defined nano- and mesostructured materials. These building blocks are assembled into two- and three-dimensional arrays, providing an additional length scale of structuring and leading to a wide range of other applications. Of particular interest are the optical, mechanical, and thermal properties of latex particles, hollow spheres, and plasmonic nanoparticles. In this context they integrate data science and AI methods into their research projects. On the one hand, this helps to analyze and interpret our experimental data. On the other hand, this allows them to realize new properties and functions of colloidal superstructures.

The chair of Physical Chemistry III investigates new materials for solar energy conversion and electrocatalysis, including high-entropy materials. Here, ML and design-of-experiment approaches help to develop next-generation catalysts for green hydrogen production, CO2 reduction, and N2 conversion.

The Kuenneth Group at the University of Bayreuth develops and applies artificial intelligence to revolutionize materials science and engineering. From discovery and design to development and deployment, their holistic approach combines data management and curation, multi-scale materials representation, machine learning model development, and democratization via accessible tools and platforms, with a core focus on polymeric and sustainable materials.

The chair of Inorganic Chemistry III focuses on synthesising and characterising nanoporous functional materials for gas storage, ion conductions and photocatalysis. By combining solid-state nuclear magnetic resonance spectroscopy, diffusometry, diffraction and electrochemical impedance spectroscopy using Bayesian ML methods with quantum mechanical modelling, we analyse guest-host interactions and unravel confinement effects on the mobile guest components.

The chair of Physical Chemistry V: Theory and Machine Learning focuses on developing new methods for the modeling and design of new functional materials on an atomistic level. They are particularly interested in equivariant graph neural networks for atomistic systems and physics-inspired machine learning techniques that incorporate electronic structure information. They also aim to improve the data-efficiency of chemical machine-learning by developing active learning workflows and transfer-learning protocols.

The chair for Information Systems and Human-centric AI, specializes in the intersection of AI and its application in society and industry. They deeply believe that the interdisciplinary combination of technical know-how and human-centered methods is crucial to designing socio-technical systems that unfold their full potential and provide benefits in later applications. This interplay also reveals important research avenues that need to be further explored. These currently include human-AI teamwork, appropriate reliance on AI decisions, fairness in AI decision-making, and societal implications and benefits of AI.

The research of the chair for Business & Information Systems Engineering and Process Analytics relies on data-driven analysis and explanation of processes (process mining), based on artificial intelligence, and methods for predicting process behavior. They are also interested in methods for privacy-preserving analysis and minimizing the re-identification of process data. At the center of their research is a process analytics pipeline aiming to efficiently process the complete chain from raw data (time series, sensor event data, and video data) to process discovery. The applications of such a data pipelines can be found in many disciplines such as medicine, agricultural sciences, geology, geography, material sciences or marine sciences.

The research of the chair for Intelligent Energy Management focuses on the operation, planning, and control of multi-energy grids (power grids coupled with district heating, hydrogen, and e-mobility). These tasks are addressed by various technologies such as artificial intelligence (AI), digital twins, the Internet of Things (IoT), and quantum computing. The integration of these technologies          enhances renewable energy integration and optimizes energy flows, contributing to a more efficient and sustainable energy system.

Maximilian Röglinger ist Geschäftsführender Direktor des FIM Forschungsinstituts für Informationsmanagement der Universität Bayreuth sowie stellvertretender Institutsleiter des Fraunhofer FIT. In seiner Forschung am Lehrstuhl für    Wirtschaftsinformatik und Wertorientiertes Prozessmanagement beschäftigt er sich unter anderem damit, wie Künstliche Intelligenz im Prozessmanagement eingesetzt werden kann. Dies beinhaltet z.B. die Automatisierung der Prozesssteuerung und von kognitiv anspruchsvollen Aufgaben, das Messung und Steuerung der Datenqualität von Ereignislogs für Process-Mining-Anwendungen sowie den Einsatz von generativen KI-Verfahren für die Unterstützung der Prozessverbesserung.

The chair of Philosophy I studies questions of rationality in strategic interaction, opinion diffusion and changes in social networks, the structure and justification of moral and legal norms, and the philosophy of collective agency. For this they use methods from logic, computer science, and decision and game theory.

Prof. Lena Kästner is professor for philosophy, computer science and AI with a specialization in philosophy of science and philosophy of mind and a background in cognitive science and cognitive neuroscience. She is a founding member of RAIS² and leads the column “AI in Society”. Her current research focuses on explainable AI (XAI), ethics of AI, and the societal impact of modern information technologies more generally. She’s also head PI of the FoGG project which investigates the role of deepfakes in criminal prosecution.

AI systems have become a part of everyday life, from improving our search results to decision support systems in critical areas like medicine. While   this technology can enhance the way we detect security threats, automate responses, and improve the speed and accuracy of defense mechanisms, AI also introduces new risks, such as the potential for malicious use in attacks or critical vulnerabilities in AI-driven systems. The research Cybersecurity Group focuses on the impact of AI systems on the security of systems, especially considering the human factor in the interaction with those Systems, and evaluates their usage for defensive mechanisms.

The research of Dr. Timo Speith focuses on the intersection of philosophy and computer science, particularly in the areas of explainability, trust, and fairness in AI and ML systems. Among other things, he explores how explainability functions as a requirement in software engineering, its relationship with trust and user experience, and how it can be effectively evaluated. Furthermore, he is interested in the philosophical (especially ethical) implications of AI decisions, including the attribution of reasons to AI systems.

Prof. Mirco Schönfeld works in the field of human-centered data science and is particularly concerned with the modeling of complex data for applications based on AI and machine learning. Among other things, he investigates the interactions between curated knowledge and its processing by AI models. On the one hand, this leads to a better understanding of AI models and, on the other, it opens up a holistic perspective on humans in the data processing process.

Prof. Patricia Rich works in the areas of game and decision theory, epistemology, and the philosophy of science. For example, an ongoing project studies the spread of fake news on online social media platforms, considering individuals' strategic choices, potential manipulation by artificial agents, and algorithms for content selection. Within philosophy of science, I have explored the possibility of using AI methods to automate or facilitate cognitive science research.

The research interests of Prof. Meng Lu include statistical spatial and temporal data analysis, remote sensing, air quality modelling, change detection. She has been specialising in the application of ML and AI technology in modelling spatiotemporal data as well as addressing environmental modelling problems.