Find Academic Supervisors

Michael FriedewaldView profile

Researcher

• Privacy Studies
• Information Security
• Technology Assessment
• +3 more

Dr. Michael Friedewald is a Researcher and Coordinator of the Business Unit Information and Communication Technologies at the Fraunhofer Institute for Systems and Innovation Research (ISI). He holds a doctoral degree from RWTH Aachen University, focusing on the intellectual and technical roots of personal computing. His work spans privacy and security technologies, surveillance systems, and innovation policy. Key projects include the PRISMS privacy assessment framework and studies on data protection in IoT ecosystems. Friedewald advises on EU policies and chairs initiatives like the Forum Privatheit und selbstbestimmtes Leben in der digitalen Welt. His research emphasizes balancing technological progress with ethical and legal safeguards. Education : Electrical Engineering, Economics, and History of Science & Technology (RWTH Aachen) Doctoral studies on personal computing history (RWTH Aachen) Research Focus : Dr. Friedewald’s expertise lies in privacy impact assessments, surveillance technologies, and governance of emerging technologies. He investigates how innovations like AI and IoT affect societal values and develops frameworks for responsible innovation. Recent work addresses challenges in deepfake detection, data-sharing ethics, and digital governance. Projects : PRISMS: Privacy and Security Mirrors for cross-border data flows EuDEco: Modeling the European Data Economy SAPIENT: Supporting fundamental rights in surveillance

Peter NeuhäuslerView profile

Research Fellow

• Innovation Economics
• Patent Analysis
• Bibliometrics
• +3 more

Peter Neuhäusler serves as Coordinator of the Business Unit Competitiveness and Innovation Measurement at the Fraunhofer Institute for Systems and Innovation Research ISI in Karlsruhe, Germany, while maintaining his position as Research Fellow at the Chair of Innovation Economics, Technical University of Berlin. His dual affiliation bridges applied research at Europe's largest application-oriented research organization with academic research at one of Germany's leading technical universities. Neuhäusler completed his Diploma in Social Sciences at the University of Mannheim (2008), specializing in micro- and macro-sociology, applied social research, and quantitative methodology. He earned his doctorate in Innovation Economics from the Technical University of Berlin in 2012 with a dissertation examining Intellectual Property Rights (IPR) and economic performance. His research focuses on industrial and social indicators, patent statistics, bibliometrics, and the value of patents, employing rigorous empirical methodologies to analyze innovation systems. Recent work shows increasing emphasis on digital innovation measurement, quantum technologies, and quality infrastructure assessment. His projects frequently address European innovation policy challenges while maintaining strong analytical rigor in patent and bibliometric analysis. Analysis of his recent publications reveals a strategic shift toward measuring emerging technologies and assessing non-economic impacts of innovation. His work increasingly connects traditional bibliometric and patent analysis with social media metrics and researcher visibility studies, demonstrating methodological innovation in science of science research. As part of the Department of Innovation and Knowledge Economy at Fraunhofer ISI, Neuhäusler leads projects examining European research systems, Chinese innovation development, and open source software ecosystems. His team specializes in developing robust indicators for research and innovation monitoring, with particular expertise in translating complex patent and bibliometric data into actionable policy insights for European and national decision-makers.

Tilman MichaeliView profile

Professor

• Computer Science Education
• Debugging in Classroom
• AI/ML Literacy
• +4 more

Prof. Dr. Tilman Michaeli is a Professor of Computer Science Education at the Technical University of Munich (TUM), affiliated with the TUM School of Social Sciences and Technology. His academic career includes a doctorate from Friedrich-Alexander University of Erlangen-Nuremberg (2020) and prior research roles at Erlangen and the Free University of Berlin under Prof. Ralf Romeike. He leads the Professorship for Computer Science Education, focusing on didactics, debugging pedagogy, AI/ML literacy, quantum computing education, and digital education transformation. Education Background: Studied Computer Science Teaching at FAU until 2017, completed state examinations, and earned his doctorate in 2020. Appointed to TUM in 2021. Research Interests: Prioritizes empirical and design-based research in computing education. Key areas include debugging strategies in classrooms, AI literacy for K-12, collaborative programming, and integrating quantum computing into curricula. His work emphasizes practical, student-centered approaches. Publications: Over 50 peer-reviewed articles since 2017, focusing on educational technology, teacher training, and curriculum development. Notable works include studies on debugging frameworks, MOOCs for AI literacy, and quantum computing in schools. Grants & Awards: Recipient of the 2019 Doctoral Award from the German Informatics Society for contributions to computer science didactics. Active in securing research grants for projects like DigiProMIN and the AI Training Initiative. Teaching: Leads courses such as Didactics of Computer Science 1/2 and Theoretical Computer Science for Vocational Education. Supervises advanced seminars and doctoral candidates. Teams: Directs a research group including employees like Sven Baumer, Luisa Gebhardt, and Heike Wachter, focusing on collaborative projects like InstaClone and Smerge tools.

Mathis FrickeView profile

Researcher

• Analysis
• Fluid Dynamics
• Wetting Dynamics
• +3 more

Dr. Mathis Fricke is a researcher in the Department of Mathematics at TU Darmstadt, affiliated with the Mathematical Modeling and Analysis (MMA) Institute. His work focuses on computational fluid dynamics, wetting processes, and mathematical modeling of multiphase systems. He specializes in numerical methods such as the Volume-of-Fluid (VOF) technique and Level-Set approaches, with applications to capillary dynamics, surfactant transport, and interface tracking in complex geometries. His research bridges experimental validation with numerical simulation, addressing challenges in fluid mechanics, energy systems, and biomedical engineering. Key areas of expertise include dynamic contact angle modeling, multiphase flow simulations using OpenFOAM modules, and optimization of fluid transport in microfluidic systems. He has contributed to advancing numerical frameworks for unstructured meshes and has collaborated on projects involving Bayesian model calibration and error quantification for energy economy systems. Dr. Fricke's work is characterized by interdisciplinary collaboration, combining analytical mathematics with high-performance computing. His publications span topics such as capillary rise dynamics, tribology measurements, and eigenmode analysis of slip-induced flows. Despite no explicitly listed awards, his contributions to computational methodologies have been recognized through his active role in the MMA Institute and academic collaborations. He maintains a lab/office in L2|06 411, Peter-Grünberg-Straße 10, Darmstadt, and is accessible via fricke@mma.tu-darmstadt.de.

Ömer GencView profile

Researcher

• Mathematics Education
• Educational Technology
• Engineering Mathematics
• +3 more

Ömer Genc is a Researcher at the Department of Geometry and Approximation, Technische Universität Darmstadt. He specializes in Mathematics Education and Educational Technology, focusing on digital tools for STEM learning. His work emphasizes integrating web-based learning platforms (e.g., STACK) into first-year engineering mathematics courses to enhance student performance and reduce tutor workload. He leads the TU-WAS project, which develops digital task collections for engineering students. Key activities include organizing math preparatory courses (VEMINT), designing adaptive diagnostic tests, and conducting teacher training programs for mathematics educators. He collaborates with institutions like the Hessian Teachers Academy and participates in statewide initiatives like DigLL-HessenHub to promote digital education. His research explores effects of digital tasks on student learning behavior and tutor efficiency, with publications in mathematics education journals and conference proceedings. Genc also contributes to interdisciplinary projects like WiGeMath-Transfer, studying support measures for mathematics learning in introductory university phases.

Estela SuarezView profile

Professor

• High Performance Computing (HPC)
• Heterogeneous HPC Architectures
• Modular Supercomputing Architecture
• +4 more

Estela Suarez is a Professor of High Performance Computing at the University of Bonn's Computer Science Department and holds leadership roles at the Jülich Supercomputing Centre (JSC) in Forschungszentrum Jülich. Her work focuses on advancing modular supercomputing architectures, heterogeneous systems, and application optimization for exascale computing. She currently serves as Chair of the EuroHPC Joint Undertaking's Research and Innovation Advisory Group (RIAG) and leads the Next Generation Architectures and Prototypes research group at JSC. Education includes a PhD in Physics from the University of Geneva (2010) and a Master's in Physics (Astrophysics specialization) from Universidad Complutense de Madrid (2004). She has held senior research positions at JSC since 2010 and has led major EU-funded projects like DEEP, DEEP-ER, and DEEP-SEA. Research interests include HPC system design, hardware-software co-design, and energy-efficient supercomputing architectures. She was awarded the University of Bonn's 2023/2024 teaching award and actively contributes to initiatives like the European Processor Initiative (EPI) and the NUMERIQS consortium. Current projects include optimizing exascale systems for Earth system modeling (IFCES2), AI-driven data analytics (AIDAS), and modular supercomputing software (DEEP-SEA). During her 2024/2025 sabbatical, she is not accepting new students. Her work is published in journals like Geoscientific Model Development and conferences such as ISC and EuroHPC.

Ina Kathrin SchieferdeckerView profile

Professor

• Digitalisation & Sustainability Transformation
• Software-based Innovation
• Technological Sovereignty
• +7 more

Prof. Ina Schieferdecker is a renowned academic and researcher with over 30 years of experience in mathematical computer science, digital transformation, and technological innovation. Currently serving as an Honorary Professor at Technische Universität Berlin since 2021, she has held leadership roles such as Director at Fraunhofer FOKUS, General Director at the Federal Ministry of Education and Research, and co-founder of the Weizenbaum-Institut für die vernetzte Gesellschaft. Her research focuses on digitalization and sustainability, software quality engineering, IoT testing, and urban data platforms. Education: She earned a Diploma in Mathematical Computer Science from Humboldt-Universität (1985–1990), followed by a PhD in performance-oriented communication protocol specifications at TU Berlin (1991–1994). She also completed a research stay at the International Computer Science Institute in Berkeley (1996). Research Interests: Her work spans digital sustainability transformation, software-based innovation, technological sovereignty, and quality engineering. Key areas include model-based testing, security testing, smart city frameworks, and open data governance. She pioneered the UML Testing Profile and contributed to the development of TTCN-3 testing standards. Key Achievements: Co-founded the Weizenbaum Institute to explore societal impacts of digital technologies. Led initiatives in AI, quantum computing, and 6G research at the Federal Ministry. Recognized with the Alfried Krupp von Bohlen und Halbach-Stiftung award for advancing automated test design and model-based testing. Awards: Recipient of the Alfried Krupp von Bohlen und Halbach-Stiftung award for her contributions to testing methodologies and software quality. Grants and Labs: Founded Fraunhofer FOKUS’s Test Lab and led the Berlin Center for Digital Transformation. Active in industry partnerships through roles like CSO at Testing Technologies IST GmbH and board memberships in innovation foundations. Labs/Teams: Headed the System Quality Center at Fraunhofer FOKUS and collaborated internationally on testing frameworks with teams in Sophia-Antipolis, Budapest, and beyond.

Heiko JankerView profile

Researcher

• Energy-Efficient Systems
• Real-Time Systems
• Embedded Systems
• +3 more

Heiko Janker is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg. His research focuses on energy-efficient systems, real-time systems, and embedded computing. He contributes to projects like PAX (Power-Aware Critical Sections), SEEP (Energy-Aware Programming), BATS (Bats Tracking Systems), and MeasureAlot (Energy Measurement Device). Research Interests: Energy-Aware System Design Real-Time Scheduling and WCET Analysis Optimized Embedded Systems Operating System Kernels for Energy Efficiency Teaching activities include exercises for the System-Level Programming in C course (Summer Term 2015). His academic work includes supervising his own diploma thesis on Autonomous Energy Analysis for Heterogeneous Hardware Platforms and contributing to multi-OS hardware abstraction layer projects. Contact details: janker@cs.fau.de, located in Room 0.040-113 at Informatik 4, FAU.

Holger StarkView profile

Professor

• Cryo-Electron Microscopy
• Structural Biology
• Biochemistry
• +3 more

Holger Stark is a Professor in Molecular Electron Cryomicroscopy at the University of Göttingen and Director at the Max-Planck-Institute for Multidisciplinary Sciences. His research focuses on 3D structural determination of macromolecular complexes using cryo-EM, including spliceosomes, ribosomes, and metabolic enzymes. He leads the Structural Dynamics department, developing novel methodologies for complex stabilization and computational image analysis. Education includes a Dr. rer. nat. in Biochemistry from Freie Universität Berlin (1996). Key career milestones include postdoctoral research at Imperial College London, junior and BioFuture group leadership roles at Max-Planck-Institute, and directorship since 2015. His work bridges biochemistry, biophysics, and computational biology, achieving world-record cryo-EM resolutions. Research interests emphasize structural mechanisms of mRNA processing and metabolic pathways. Collaborative projects include spliceosome dynamics and fatty acid synthesis. His lab also develops imaging and computational tools, such as the COW software package.

• Formal Methods
• Software Engineering
• Programming Languages
• +13 more

Michael Leuschel is a Professor in the Institute of Computer Science at Heinrich-Heine-Universität Düsseldorf, Faculty of Mathematics and Natural Sciences. He leads research in formal methods, programming languages, and software engineering, with a focus on the development and application of the ProB model checker and constraint solver. He is co-founder of Formal Mind and scientific advisor to Nobreach, and actively contributes to the formal methods community through editorial and organizational roles. His research interests include formal verification, model checking, logic programming, partial evaluation, and the B and Event-B methods. He has pioneered techniques for animating and validating formal models, with applications in safety-critical systems such as railway operations. His work bridges theoretical foundations with industrial practice, enabling rigorous software construction. The recent publications highlight a strong trend in applying formal methods to AI safety, particularly in railway systems (e.g., KI-LOK project), integrating reinforcement learning with safety shields, and validating AI-based components. Other themes include interactive simulation, domain-specific validation documents, and the use of ProB for TLA+ and railML. The work combines theoretical advances in model checking with practical tool development. Michael Leuschel has supervised numerous students and collaborators, including Jens Bendisposto, Sebastian Krings, Philipp Körner, and Joshua Schmidt. He has been involved in significant research grants and projects, such as KI-LOK and the development of ProB, often in collaboration with industry and other academic institutions. His work has led to the creation of widely used tools and methodologies in formal methods. He is a key member of the Software Engineering and Programming Languages research group. His team develops and maintains ProB, works on integrating formal models into applications, and explores new frontiers in AI validation using formal techniques. The group is active in both theoretical research and practical tool building.

Maximilian BleschView profile

Researcher

• Structural Modeling
• Labor Economics
• Public Finance
• +3 more

Maximilian Blesch is a PhD candidate in Economics at the Berlin School of Economics and a Research Associate at Humboldt University and the German Institute for Economic Research (DIW Berlin). His work spans structural modeling in labor economics, public finance, and industrial organization, with a focus on dynamic decision models and behavioral biases. Bachelor's in Mathematics and Business Mathematics, Julius-Maximilians-University Würzburg Master of Science in Mathematics and Economics, Rheinische Friedrich-Wilhelms-University Bonn His research includes collaborations with institutions like the University of Copenhagen and Georgetown University, contributing to projects on pension systems, wage expectations, and robust decision-making frameworks. Selected publications highlight his contributions to pension policy (2025), labor supply biases (2023-2024), and methodological innovations in dynamic modeling. He actively engages in software development through GitHub repositories.

Florian ReitzView profile

Researcher

• Computer Science
• Database Systems
• Information Retrieval
• +5 more

Florian Reitz is a Scientific Staff member at Schloss Dagstuhl - Leibniz Center for Informatics, specializing in the dblp computer science bibliography. His current role involves enhancing bibliographic data infrastructure and managing projects like SmartER. He holds a PhD from the University of Trier (2018), focusing on historical metadata analysis. Previously, he was a research assistant in the Databases and Information Systems group under Prof. Bernd Walter. His work centers on metadata quality, author disambiguation, and leveraging historical corrections for test collections. Research interests include information retrieval, database systems, and digital libraries. He contributes to projects like Scalable Author Disambiguation and has co-authored numerous publications on topics such as LLM applications, knowledge graphs, and FAIR data principles. Contact: florian.reitz@dagstuhl.de.

• Nuclear Magnetic Resonance
• Magnetic Resonance Imaging
• Biomedical Physics
• +3 more

Dr. Isabell Katzmann serves as a Researcher at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, where she is embedded within the Methods and Development Group for Nuclear Magnetic Resonance. Her role centers on advancing NMR techniques for cognitive and brain science applications, operating from the institute's Stephanstraße 1A address in the Sachsen region. Her research specializes in refining Nuclear Magnetic Resonance methodologies to enhance neuroimaging precision, with a focus on developing novel hardware and software solutions for human brain studies. This work intersects biomedical physics and cognitive neuroscience, targeting improvements in spatial resolution, acquisition speed, and analytical frameworks for non-invasive brain imaging. As part of the institute's dedicated NMR research team, Dr. Katzmann contributes to interdisciplinary projects that bridge physics, engineering, and neuroscience. Her technical development efforts directly support the institute's mission to decode human brain function through cutting-edge imaging innovation, though specific leadership roles or collaborative networks beyond the NMR group are not detailed in available materials.

• Uncertainty and Sensitivity Analysis
• Computational Modeling
• Non-Invasive Brain Stimulation
• +3 more

Dr. Konstantin Weise is a Scientific Researcher at the Max Planck Institute for Human Cognitive and Brain Sciences , leading the Methods and Development Group Brain Networks . His work focuses on uncertainty and sensitivity analysis of computational models , developing the pygpc Python package , and advancing numerical models for non-invasive brain stimulation techniques like Transcranial Magnetic Stimulation (TMS) . He specializes in improving localization precision, optimizing stimulation parameters, and addressing microscopic tissue effects in brain stimulation simulations. Research interests include: Modeling TMS-induced electric fields and their cortical effects High-resolution EEG/MEG forward modeling Uncertainty quantification in biomedical simulations Neurostimulation applications for neuropsychological disorders His contributions bridge computational methods with clinical neurotechnology, emphasizing electrical field-based dosing and adaptive stimulation optimization . Recent work addresses microscopic field distortions' impact on neuronal activation thresholds and develops novel protocols for precise motor cortex mapping. Labs/Teams: Core member of the Brain Networks Group , collaborating on interdisciplinary projects integrating engineering, neuroscience, and clinical applications.

David GackstetterView profile

Researcher

• Deep learning in remote sensing and earth observation
• Fusion of multimodal, multitemporal and multispectral data
• Meta Learning
• +1 more

David Gackstetter, M.Sc., is a Researcher at the Technical University of Munich (TUM) within the Central Institute Hans Eisenmann Forum for Agricultural Sciences (HEF). He holds roles as a Project Engineer and Science Manager, focusing on data infrastructure development and interdisciplinary project coordination in Digital Agriculture. He is also pursuing a doctoral degree with the Chair of Remote Sensing Methodology and the Assistant Professorship for Precision Agriculture. Education: B.Sc. in Environmental and Resource Management from BTU Cottbus-Senftenberg (focus: environmental modeling), M.Sc. in Environmental Engineering from TUM (focus: machine learning in transportation engineering). Research interests include deep learning applications in remote sensing and Earth observation, fusion of multimodal/multitemporal data, meta-learning, and machine learning in agricultural/environmental sciences. Recent publications explore autonomous field management, crop type mapping, and digital twin frameworks for agricultural landscapes. Publications highlight contributions to FAIR data principles in agriculture, IoT interoperability, and distributed digital twin systems. No scientific awards are explicitly mentioned. He coordinates projects like SRADI and FAIRagro, contributing to data infrastructure and open standards in digital agriculture. His work integrates sensor networks and machine learning to address sustainability and precision agriculture challenges. He is affiliated with the Chair of Remote Sensing Technology at TUM, collaborating on projects involving satellite imagery analysis and agricultural data systems.