Find Academic Supervisors

• Multisensory Integration
• Knowledge Space Theory
• Cognitive Modeling
• +2 more

Katharina Naumann is a researcher in the Department of Psychology at the University of Tuebingen, specializing in multisensory integration, knowledge space theory, and cognitive modeling. She contributes to academic software development, including R Shiny apps for psychological model simulations. Dipl.-Psych., 2015, University of Tuebingen Erasmus Intensive Program: Quantitative Psychological Processes, 2014, University of Tartu, Estonia Erasmus Intensive Program: Quantitative Psychological Processes, 2013, University of Oulu, Finland Semester Abroad, 2011, University of Otago, New Zealand Her research focuses on integrating sensory data, modeling psychological processes mathematically, and advancing quantitative methods in education. She has developed tools like the nlsem R package for nonlinear structural equation mixture models and interactive Shiny apps for psychometric analysis. Her publications highlight interdisciplinary work bridging psychological theory with computational statistics. Notable contributions include modeling multisensory integration time-windows and identifiability in local independence frameworks. Naumann has taught courses like Praktikum Datenerhebung und Auswertung and Tutorium Forschungsmethoden der Psychologie, and participated in the Erasmus+ Project Tools for Teaching Quantitative Thinking (2017). Contact: katharina.naumann@uni-tuebingen.de , +49 (0)7071 29-78340, Room 4.536, Schleichstr. 4, 72076 Tübingen.

Michael KochView profile

Professor

• Robotics
• Additive Manufacturing
• Materials Science
• +3 more

Prof. Michael Koch is a Professor at Technische Hochschule Nürnberg, specializing in interdisciplinary research at the intersection of robotics, materials science, and industrial engineering. His work focuses on advancing additive manufacturing, robotics integration, and computational simulation in manufacturing processes. He holds a Dr.-Ing. and Dipl.-Wirt.-Ing., reflecting his expertise in engineering and applied sciences. Research interests include 3D printing optimization, cyber-physical systems, and safety analysis of materials under mechanical stress. He has contributed to innovations in robotics programming via augmented reality and developed frameworks for automated manufacturing processes. His studies on biomedical applications, such as ovarian cancer modeling, demonstrate a cross-disciplinary approach. Key projects involve robot-guided CT scanning for automotive industry 4.0, part orientation evaluation for additive manufacturing (Poeam), and safety protocols for explosives (PBX). His work emphasizes integrating real-world geometry data into simulations to improve accuracy and efficiency. Prof. Koch’s publications span over three decades, with a focus on manufacturing, robotics, and materials science. He has pioneered methods for energy-efficient CO2 capture systems and explored microstructural changes in materials under dynamic loading.

• Planetary Science
• Aeolian Geomorphology
• Mars Geology
• +4 more

Elena Favaro is a Research Fellow at the European Space Agency (ESA), specializing in planetary science with a focus on aeolian geomorphology. She utilizes geographic information systems and high-resolution imagery to study landforms (yardangs, periodic bedrock ridges) and bedforms (megaripples, dunes, transverse aeolian ridges) on Earth and Mars, with current emphasis on Oxia Planum—the designated 2030 landing site for ESA's ExoMars Rosalind Franklin rover mission. Her research reconstructs Martian climatic history through analysis of aeolian features using remote sensing, 3D image analysis, and GIS. Key interests include landscape evolution, sediment transport processes, and wind dynamics, with specific focus on how yardangs, periodic bedrock ridges, and dust devils record past and present environmental conditions. Current work integrates deep learning for terrain classification to support mission planning at Oxia Planum. Recent publications reveal concentrated research on Oxia Planum, featuring high-resolution geological mapping, wind regime modeling, and classification of aeolian bedforms. Studies emphasize periodic bedrock ridges as paleowind indicators, secondary cratering for stratigraphic dating, and megaripple architecture for sediment transport analysis. This work provides critical context for rover operations by linking surface features to ancient climate conditions and contemporary aeolian processes.

Xiangbin TengView profile

Assistant Professor

• Neuroscience
• Cognitive Psychology
• Neurocognitive Science
• +3 more

Prof. Xiangbin Teng is an Assistant Professor in the Department of Psychology at the Chinese University of Hong Kong. Previously, he was a postdoctoral researcher at the Neurocognitive and Experimental Psychology lab in Frankfurt/Berlin (2021–2022), following his PhD in Cognition and Perception from New York University and M.Sc./B.Sc. degrees from Peking University and Shanghai Jiao Tong University. His research focuses on neurocognitive mechanisms underlying auditory perception, particularly speech and music processing. Key areas include neural oscillations, temporal dynamics, and multiscale auditory encoding. He investigates how rhythmic patterns in language (e.g., ancient Chinese poetry) and music influence perceptual and neural processes. Publications highlight contributions to understanding theta/gamma band neural coding, EEG modulation spectra, and phase precession mechanisms. His work bridges computational models (e.g., recurrent neural networks) with empirical neuroscience findings. ERC Consolidator Grant (2025): Leading the "TRANSFORM" project exploring visual system development from infancy to adulthood. Grants & Collaborations: Extensive research on auditory cortex fMRI methods and cochlear implant simulations. Labs/Teams: Active in CUHK's cognitive neuroscience group and previously at the Max Planck Institutes. His current lab investigates sensory-motor interactions and predictive coding in perceptual systems.

Lutz EcksteinView profile

Professor

• Automotive Engineering
• Automated Driving Systems
• Vehicle Dynamics
• +4 more

Univ.-Prof. Dr.-Ing. Lutz Eckstein is the Head of the Institute of Automotive Engineering (ika) at RWTH Aachen University and a leading expert in automotive engineering. He concurrently serves as President of the VDI Association of German Engineers, President of the VDI Scientific Advisory Board for the Federal Ministry of Digital and Transport (BMDV), and Scientific Director of the NRW Competence Network for Automated and Connected Mobility. His professional background includes senior roles at BMW and DaimlerChrysler, focusing on vehicle safety, driver assistance systems, and ergonomics. Eckstein holds a Doctorate from the University of Stuttgart (2000) and received the Artur Fischer Prize (1995) for academic excellence. His research interests span automated driving systems, human-machine interfaces, scenario-based testing, and mobility infrastructure. He leads high-profile projects like UNICARagil and coordinates the Mobility and Transport profile at RWTH Aachen. Over 80 patents and publications reflect his contributions to automotive innovation, including advancements in sensor fusion, digital twins, and cooperative intelligent transport systems (C-ITS). His recent work emphasizes safety assurance for automated vehicles, scenario databases, and scalable simulation frameworks. Eckstein's interdisciplinary approach bridges academia and industry, driving transformative solutions for future mobility challenges.

Ina Berenice FinkView profile

Researcher

• Cybersecurity
• Network Security
• Industrial Communication
• +5 more

Ina Berenice Fink is a Researcher and PhD candidate at RWTH Aachen University's Chair of Communication and Distributed Systems. She holds an M.Sc. in Computer Science (2019) and is affiliated with both the Security and Privacy Group and Cyber-Physical Systems Group. Her work focuses on securing communication in critical infrastructure and industrial environments. She has served as a part-time lecturer at FH Aachen and actively contributes to teaching through seminars, labs, and thesis supervision. Notable awards include the 2020 Women’s STEM Award for her master’s thesis on in-network security and the 2019 Female Student Travel Award. Her research spans projects like VeN2uS (low-latency critical infrastructure communication), CONSENT (smart home security), and myneData/TRINICS. She has published extensively on topics including network monitoring, resilient smart grid communication, and industrial IoT security. Her work combines programmable networking, protocol evaluation, and cybersecurity frameworks to address real-world challenges in distributed systems. In teaching, she supervises bachelor’s/master’s theses on network emulation, industrial security, and programmable data planes. Her advising record includes over a dozen theses with topics ranging from starlink performance evaluation to secure industrial MUD frameworks. She collaborates across RWTH departments and external institutions, contributing to both academic conferences and applied security solutions.

• Ecotoxicology
• Environmental Risk Assessment
• Pesticide Impact Analysis
• +9 more

Dr. Richard Ottermanns is a Professor at the Chair of Ecotoxicology and Environmental Risk Assessment at RWTH Aachen University. He is affiliated with the Personalrat der wissenschaftlich, ärztlich und künstlerisch Beschäftigten der RWTH Aachen (Academic Senate), gaiac - Forschungsinstitut für Ökosystemanalyse und ‑bewertung e.V., and the Fachgruppe Biologie (Biology Department Group). His research focuses on ecotoxicology, environmental risk assessment, pesticide impacts, and biodiversity conservation. Key research interests include the effects of chemical mixtures on ecosystems, zebrafish developmental biology as a model organism, computational modeling of ecological systems, and sustainable forest management strategies. He has contributed to projects like the DanTox initiative, which investigates sediment-bound pollutants using zebrafish models. His work spans interdisciplinary approaches, integrating molecular biology, environmental chemistry, and computational tools to address challenges in ecological risk assessment and sustainability science. He collaborates with institutions like the Three Gorges Reservoir Project in China and the Eifel National Park in Germany.

• Emotional Expression Recognition
• Social Psychology
• Nonverbal Communication
• +3 more

Prof. Dr. Ursula Hess is a Professor at the Humboldt University of Berlin in the Department of Social and Organizational Psychology within the Faculty of Life Sciences. She holds leadership roles as Director of Studies and Teaching and serves in the Executive Directorate of the Institute of Psychology. Her research focuses on emotional expression recognition, nonverbal communication, and the social and cultural influences on emotional perception and mimicry. She explores how contextual factors, such as social class, attire, and cultural norms, shape emotion decoding accuracy and interpersonal interactions. Her work also examines the role of emotional mimicry in social dynamics, including its modulation by stigma, virtual environments, and aging. Research interests include the interplay between emotion displays and social contexts, the impact of facial and bodily cues on first impressions, and the effects of mental health stigmatization on emotional processing. Key contributions address how socio-cultural frameworks and technological advancements (e.g., VR) influence emotional perception and communication. Hess’s studies often bridge psychology with interdisciplinary methodologies, incorporating behavioral observations, physiological measurements, and computational tools to understand human interaction mechanisms.

Detlef UrhahneView profile

Professor

• Educational Psychology
• Science Education
• Technology-Enhanced Learning
• +3 more

Prof. Detlef Urhahne is a Professor of Educational Psychology at the University of Passau. He previously served as a research assistant at the Chair of Biology Education at Ludwig Maximilian University (LMU) Munich from 2002 to 2008. His research focuses on technology-enhanced learning, science education, and motivational factors in educational contexts. Key areas include the design of effective learning environments using computer simulations, hypertext systems, and biodiversity databases, as well as the role of epistemological beliefs and self-concept in student learning. His work emphasizes empirical evaluations of instructional methods, such as comparing linear vs. hypermedia learning structures, and exploring how digital tools support conceptual understanding in chemistry and biology. He has contributed to understanding motivation in teacher training, particularly among biology educators, and developed validated questionnaires to measure interest in biodiversity topics. His studies often intersect cognitive psychology, educational technology, and didactic principles in science disciplines. Urhahne’s research also addresses interdisciplinary challenges like the 'right amount of instruction' in museum settings and the impact of animations on neurobiology learning. He collaborates internationally, evidenced by co-authored works in journals like the International Journal of Science Education and Psychological Review . While no awards are explicitly listed, his extensive publication record highlights sustained contributions to educational psychology and science pedagogy. His advising and grant activities remain unspecified in the provided texts. He has been involved in developing projects such as the EU-funded TREBIS initiative for biodiversity education, reflecting his commitment to bridging formal and informal learning environments through technology.

Katharina von KriegsteinView profile

Professor

• Neuroscience
• Cognitive Science
• Clinical Neuroscience
• +5 more

Katharina von Kriegstein is a Professor of Cognitive and Clinical Neuroscience at Technische Universität Dresden (since 2017). Previously, she held positions at Humboldt University Berlin (2013–2017) and led a Max Planck Research Group (2009–2018). Her research focuses on sensory processes underlying human communication, employing neuroimaging, neurostimulation, and behavioral techniques. She explores communication impairments in developmental disorders and neurological patients, particularly investigating auditory and visual thalamic dysfunction in dyslexia and autism. Her work is supported by an ERC Consolidator Grant (2015) and has contributed to understanding sensory pathway roles in speech-in-noise recognition and voice identity processing. Key findings include thalamic structural abnormalities linked to dyslexia and autism, as well as predictive coding mechanisms in auditory pathways. Education: Medical License (Approbation), 2003 Dr. med., summa cum laude, University of Göttingen (1994–2001) Philosophy studies, University of Göttingen (1995–1997) Studium generale, Leibniz-Kolleg, Tübingen (1993–1994) Key Projects: Leading Subproject A11 in CRC 940 (goal-directed behavior) Investigating thalamic roles in communication (e.g., auditory/visual speech perception) Her research integrates clinical, cognitive, and computational approaches. Recent work highlights multisensory learning benefits for auditory processing and the causal role of sensory cortices in vocabulary translation. Awards include the Max Planck Research Group (2009) and best doctoral thesis in Experimental Medicine (2002).

• Computational Materials Science
• Energy Storage Materials
• Solid-State Batteries
• +4 more

Dr. Yang Bai is a Project Group Leader at the Max Planck Institute for Sustainable Materials, leading the Computational Energy Storage Materials and Computational Sustainable Metallurgy groups within the Department of Microstructure Physics and Alloy Design. His research focuses on computational modeling of energy materials, solid-state batteries, and sustainable metallurgical processes, emphasizing chemo-mechanical interactions and phase-field simulations. Key projects include analyzing silicon anode degradation in solid-state batteries and hydrogen-based reduction of iron oxides. He contributes to interdisciplinary approaches combining materials science, electrochemistry, and multi-physics modeling to advance sustainable technologies. Publications highlight trends in solid-state battery interfaces, phase-field modeling of material transformations, and porous structure analysis. His work bridges computational methods with experimental validation, addressing challenges in energy storage and metallurgical efficiency. Active collaborations and contributions to ontology-based workflows for steel industries underscore his commitment to industrial application of advanced materials science.

Michael ObersteinerView profile

Researcher

• Molecular Dynamics
• High-Performance Computing (HPC)
• Sparse Grids Methods
• +3 more

Dr. Michael Obersteiner is a researcher at the Technical University of Munich (TUM), affiliated with the TUM School of Computer Science and Information Technology and the Department of Computer Science. He is part of the Chair of Scientific Computing in Computer Science (SCCS), led by Prof. Hans-Joachim Bungartz. His research focuses on high-dimensional numerical methods, sparse grids, the combination technique, molecular dynamics, and machine learning applications. He has contributed to the development of scalable algorithms for exascale computing, including the DisCoTec and sparseSpACE frameworks. Education: Michael Obersteiner began his studies in Biochemistry in 2010 before switching to Computer Science in 2011. He completed his M.Sc. in Computer Science in 2016 and is currently pursuing his doctoral dissertation with a focus on high-dimensional numerics and the sparse grid combination technique. Research Interests: His work spans molecular dynamics simulations, high-performance computing (HPC), spatially adaptive sparse grid methods, and applications in uncertainty quantification, machine learning, and plasma physics. He has developed open-source software tools like DisCoTec (for exascale PDE simulations) and sparseSpACE (for adaptive sparse grid operations). Teaching: Obersteiner has been involved in teaching numerical programming, scientific computing, and discrete structures at TUM. His roles include leading exercises for courses like 'Algorithms for Scientific Computing' and 'Numerisches Programmieren.' Projects: He leads projects such as DisCoTec and sparseSpACE, which focus on scalable sparse grid techniques for high-dimensional problems. His research also includes applications in plasma turbulence modeling, hydrology simulations, and machine learning with density estimation. Grants and Collaborations: His work has been supported by projects like EXAHD (funded by DFG/SPPEXA). He collaborates with institutions like BGCE, CSE, and IGSSE. Open student projects are available in areas like adaptive quadrature, machine learning with sparse grids, and uncertainty quantification.

Kislaya RaviView profile

Researcher

• Multifidelity Uncertainty Quantification
• Gaussian Process
• Sparse Grids Methods
• +2 more

Kislaya Ravi is a Doctoral Candidate at the Technical University of Munich (TUM), affiliated with the Chair of Scientific Computing within the TUM School of Computation, Information and Technology. She holds an M.Sc. in Computational Science and Engineering from TUM and degrees in Mechanical Engineering from the Indian Institute of Technology (BHU). Her research focuses on multifidelity uncertainty quantification, Gaussian processes, sparse grids methods, machine learning, and stochastic optimization. Education: M.Sc., Computational Science and Engineering, Technical University of Munich (202X) M.Tech., Machine Design, Indian Institute of Technology (BHU) (20XX) B.Tech., Mechanical Engineering, Indian Institute of Technology (BHU) (20XX) Research Interests: Kislaya’s work bridges computational methods and stochastic systems, emphasizing efficient uncertainty quantification techniques in physics and engineering. She explores multifidelity approaches to enhance computational efficiency in complex simulations and data-driven modeling. Teaching and Advising: Core instructor for Scientific Computing II and Algorithms for Uncertainty Quantification . Supervised multiple Master’s and Bachelor’s theses on topics like black-box optimization, surrogate modeling, and plasma instability analysis. Publications highlight contributions to multi-fidelity Gaussian processes, No-U-Turn sampling, and kinetic modeling. Her work appears in journals like Machine Learning: Science and Technology and conferences such as SIAM CSE and MCQMC.

Stefan Alexander SchneiderView profile

Professor

• Autonomous Driving
• Driver Assistance Systems
• Vehicle Simulation
• +3 more

Affiliations & Roles Prof. Dr. Stefan Alexander Schneider holds a professorship in Autonomous Driving and Driver Assistance Systems at the Faculty of Electrical Engineering of Kempten University of Applied Sciences. He also serves as Program Coordinator and Academic Advisor for the Master's program in Driver Assistance Systems. Additionally, he is a Visiting Professor at Shibaura Institute of Technology (Tokyo, Japan) . Education & Academic Background He completed his doctoral thesis "Adaptive Solution of Elliptic Partial Differential Equations by Hierarchical Tensor Product Finite Elements" in 2000, laying groundwork for his later research in computational methods. Research Focus His work centers on autonomous driving technologies , including: Simulation methodologies for vehicle systems Safety validation of driver assistance systems Human-machine interface design for elderly mobility solutions Standardization of testing frameworks (e.g., Open Simulation Interface) Key Contributions Recent projects include: ZuMoBe: Exploring autonomous electric vehicles in mountain valleys Development of Virtual Systems Prototyping frameworks for automotive innovation Cross-border collaboration via the VIVID German-Japanese initiative Teaching & Mentorship As a leader in one of the world's few Master's programs dedicated to ADAS/AV technologies, he mentors students in cutting-edge topics like monocular depth estimation, trajectory modeling, and interface design. His advisees have produced impactful works on autonomous scooter usability, localization algorithms, and motion planning validation.

• Glaciology
• Geophysics
• Climate Science
• +3 more

Daniela Jansen is a Geophysicist and Glaciologist at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research. Her research focuses on radar stratigraphy, ice rheology, and microdynamical processes in polar ice sheets and ice shelves, particularly in Antarctica and Greenland. She leads projects on ice flow dynamics, subglacial processes, and ice core analysis, contributing to understanding ice sheet stability and climate interactions. Affiliations: Alfred Wegener Institute (AWI), Helmholtz-Gemeinschaft Key Collaborations: EastGRIP Ice Core Project, NEGIS (Northeast Greenland Ice Stream) studies, Antarctic ice shelf monitoring Her work integrates field measurements, radar sounding, and numerical modeling to investigate ice deformation, basal conditions, and englacial structures. Notable contributions include studies on rift propagation in Larsen C Ice Shelf and the structural analysis of ice stream margins via airborne radar data. Publications span over 100 peer-reviewed articles, focusing on glaciological processes, remote sensing techniques, and ice core microstructure. She has advised numerous collaborative projects but no formal student list is available.