Find Academic Supervisors

• Radar Image Analysis
• Out-of-distribution Detection
• One-Class Deep Neural Networks
• +5 more

Mustafa Kahya is a Scientific Staff member and Ph.D. candidate at the Chair of Media Technology within the Munich Institute of Robotics and Machine Intelligence (MIRMI) at the Technical University of Munich (TUM). He works under the supervision of Prof. Dr.-Ing. Eckehard Steinbach and is actively involved in research related to radar systems and machine learning. His academic background includes a B.Sc. in Computer Engineering from Istanbul Technical University (2017) and an M.Sc. in Informatics from TUM (2021). During his master's studies, he conducted research on 3D Reconstruction and Multi-view Shape from Shading at the TUM Computer Vision Group. Kahya's research focuses on Radar Image Analysis , Out-of-distribution Detection , One-Class Deep Neural Networks , Anomaly Detection , and Generative Models . His work primarily centers on applying deep learning techniques to short-range FMCW radar systems for various applications including human presence detection, facial authentication, and activity recognition. His publications demonstrate a strong trend toward real-time radar-based systems with emphasis on out-of-distribution detection capabilities. Kahya has been actively publishing in top-tier conferences and journals from 2023 through 2025, with multiple first-author publications in IEEE venues including ICASSP, ICIP, and IEEE Sensors. His research has been part of several significant projects including the Centre for Tactile Internet with Human-in-the-Loop (CeTI) and DFG-funded research on Teleoperation over 5G. As a Ph.D. candidate at the Chair of Media Technology, Kahya contributes to the research group's work in computer vision, machine learning, and radar systems. His work bridges the gap between traditional computer vision techniques and novel radar-based sensing modalities, creating opportunities for applications in environments where optical systems face limitations.

Verena Hüttl-MaackView profile

Professor

• Consumer Behavior
• Brand Management
• Advertising Effectiveness
• +3 more

Prof. Dr. Verena Hüttl-Maack holds the Chair of Marketing and Consumer Behavior at the University of Hohenheim's Faculty of Economics and Social Sciences. Her roles include academic leadership, research, and teaching in consumer behavior, brand management, and advertising effectiveness. She has been affiliated with the university since 2012, following her habilitation in 2013. Her research explores areas like curiosity-driven marketing, sustainability perceptions, and multilingual packaging effects. Education: Studied Business Administration at the University of Augsburg and University of Wales (UK). Doctorate (2010): Thesis on brand extensions. Habilitation (2013): University of Hohenheim. Research Interests: Her work focuses on the psychological and behavioral aspects of consumer decision-making, particularly how curiosity, language use, and brand messaging influence evaluations. Key themes include the role of art in advertising, competitive advertising effects, and sustainable consumption behaviors. Publications: Over 50 peer-reviewed articles in journals like Journal of Consumer Psychology , Psychology & Marketing , and International Journal of Advertising . Recent work addresses sustainability perceptions in food marketing and the impact of augmented reality on consumer experiences. Grants & Collaborations: Led projects on short food supply chains, consumer responses to multilingual packaging, and transformative consumer journeys. Collaborates internationally with institutions in Germany, Spain, Greece, and Hungary. Labs/Teams: Active in the Marketing and Consumer Behavior research group at Hohenheim, focusing on experimental design and applied consumer research.

Felix KrahmerView profile

Assistant Professor

• Compressed Sensing
• Signal Processing
• Optimization
• +4 more

Prof. Felix Krahmer is a TUM Tenure Track Assistant Professor of Optimization and Data Analysis at the Department of Mathematics, Technische Universität München (TUM), part of the School of Computation, Information and Technology. Previously, he held a Junior Professorship (W1) for Mathematical Data Analysis at the University of Göttingen (2012–2015). His research focuses on mathematical foundations of data science, including compressed sensing, signal quantization, uncertainty quantification, and optimization algorithms. He has led an Emmy Noether research group and contributed to the ZeMat interdisciplinary project. Education : PhD in Mathematics (2009), New York University, advisors: Percy Deift & Sinan Güntürk MSc in Mathematics (2007), New York University BSc in Mathematics (2004), Jacobs University Bremen Research Interests : Krahmer’s work bridges theoretical mathematics and applied data science. Key areas include compressed sensing algorithms, high-dimensional signal reconstruction, quantization theory for analog-to-digital conversion, and optimization methods for inverse problems. He also explores applications in imaging (e.g., MRI reconstruction) and stochastic processes. Key Contributions : His research on phase retrieval, sigma-delta quantization, and compressed sensing recovery has advanced signal processing techniques. Recent efforts focus on uncertainty quantification for high-dimensional inverse problems and the mathematical analysis of consensus-based optimization. Grants & Awards : Emmy Noether Research Group Grant (DFG) Funding from BMBF (ZeMat project) Teaching & Outreach : Krahmer has taught advanced courses on compressed sensing, functional analysis, and random matrix theory. He co-organized workshops on probabilistic techniques and clinical risk prediction, emphasizing interdisciplinary collaboration. Labs/Teams : Member of the TUM Data Science Research Group, focusing on optimization, imaging, and uncertainty quantification. Active in the Munich Center for Quantum Science and Technology (MCQST).

• Quantum Photonics
• Computational Electromagnetics
• Quantum Amplifier Design
• +3 more

Yongjie Yuan is a Researcher at the Technical University of Munich under the Associate Professorship of Computational Photonics within the TUM School of Computation, Information and Technology. His research focuses on quantum cascade devices, Josephson traveling-wave parametric amplifiers, and noise modeling in superconducting circuits. Institution: Technical University of Munich School: TUM School of Computation, Information and Technology Academic Rank: Researcher Email: yongjie.yuan@tum.de Yuan's work integrates quantum mechanics with computational photonics , emphasizing the modeling of superconducting circuits and microwave parametric amplifiers . He investigates dissipative-dispersive systems , flux-driven amplifiers , and nonlinear junction topologies to enhance quantum device performance. Recent publications highlight his contributions to quantum amplifier design , Josephson junction modeling , and noise dissipation analysis in microwave photonics. His research is associated with the EU Project QOMBS, focusing on quantum cascade devices and parametric amplification. Quantum Cascade Devices Josephson Traveling-Wave Parametric Amplifiers Quantum Noise Modeling Nonlinear Circuit Analysis Microwave Photonics Yuan actively participates in teaching and supervising activities, contributing to courses on computational photonics , quantum engineering , and partial differential equations for electrical engineering. He collaborates with researchers like Christian Jirauschek and Michael Haider on quantum device simulations.

Ana SerranoView profile

Associate Professor

• Visual Computing
• Computational Imaging
• Material Appearance Perception
• +7 more

Ana Serrano is an Associate Professor at Universidad de Zaragoza, Spain, where she is affiliated with the Graphics & Imaging Lab in the EINA (Edificio Ada Byron) school. She earned her PhD at the same institution under the supervision of Prof. Diego Gutierrez and Prof. Belen Masia, and completed a postdoctoral fellowship at the Max-Planck-Institute for Informatics under Prof. Karol Myszkowski. Her research focuses on visual computing , particularly in computational imaging , material appearance perception and editing , and virtual reality . She is especially interested in developing perceptually-driven methods that leverage knowledge of the human perceptual system to enhance user experiences and assist content creation in immersive environments. Her recent publications (2023–2025) span top-tier venues such as SIGGRAPH, CVPR, IEEE TVCG, and Eurographics. These works explore topics like saliency prediction in 3D and 360° video, crossmodal perception in VR, gloss modeling, radiance fields, and perceptual evaluation of immersive content. The research demonstrates a strong integration of machine learning, human perception, and computer graphics to solve real-world problems in visual computing. She has received several prestigious awards, including: Eurographics 2023 Young Researcher Award VGTC VR 2024 Significant New Researcher Award Eurographics 2020 PhD Award Adobe Research Fellowship (honorable mention, 2017) NVIDIA Graduate Fellowship (2018) Ana Serrano actively supervises PhD and Master’s students and has taught courses such as Virtual Reality, Computational Imaging, and Deep Learning applications. She serves as an Associate Editor for Computer Graphics Forum , ACM Transactions on Applied Perception , and Computers and Graphics , and has held leadership roles in major conferences including Eurographics (Tutorials co-chair, 2023), ACM SAP (Program co-chair, 2022), and CEIG (Program co-chair, 2022). Her professional service includes extensive program committee and reviewer roles for SIGGRAPH, IEEE VR, ISMAR, and others. She leads a vibrant research group focused on human perception in virtual environments, with current projects on computational models of attention and perception, integrated with physiological signals. Her lab, the Graphics & Imaging Lab, fosters interdisciplinary collaboration and innovation in visual computing.

• Gravitational Wave Data Analysis
• Cosmological Parameter Estimation
• LISA Data Analysis
• +3 more

Dr. Jonathan Gair is a Group Leader in the Astrophysical and Cosmological Relativity Division at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Potsdam, Germany. Previously, he served as Professor of Astrostatistics at the University of Edinburgh (2018-2019) and as Reader (Associate Professor) in Statistics at the same institution (2015-2018). Dr. Gair's research focuses on gravitational wave data analysis and its applications to cosmology and fundamental physics. His work spans multiple areas of gravitational wave astronomy, with particular emphasis on: Developing and applying new methodologies for gravitational wave data analysis Using gravitational wave observations to derive cosmological parameters, particularly the Hubble constant Developing data analysis tools for the LISA space-based gravitational wave detector Exploring the scientific potential of gravitational wave observations for testing general relativity Creating computationally efficient techniques for parameter inference in gravitational wave astronomy Dr. Gair plays a leading role within the LIGO/Virgo collaboration in deriving cosmological constraints from gravitational wave observations. He currently chairs the LISA Science Group, overseeing the development of data analysis tools for the planned ESA-led LISA mission. His research has significantly contributed to our understanding of how gravitational wave observations can serve as "standard sirens" for measuring cosmic distances and probing the expansion history of the universe. Dr. Gair's work involves both theoretical development and practical application of data analysis techniques. He has developed methods for handling selection effects in rate estimation of gravitational wave events, techniques for mapping gravitational wave backgrounds using methods adapted from cosmic microwave background analysis, and approaches for incorporating model uncertainties into gravitational wave parameter estimation.

Ralf BeckView profile

Professor

• Control Engineering
• Automation Technology
• Regulation Systems
• +5 more

Prof. Dr.-Ing. Ralf Beck serves as Professor for Control and Regulation Technology and Automation Technology at Hochschule Düsseldorf University of Applied Sciences within the Faculty of Electrical Engineering & Information Technology. His academic responsibilities span multiple degree programs including BEng Electrical Engineering, BEng Industrial Engineering, and MSc Electrical Engineering and Information Technology. His educational background includes Mechanical Engineering studies at TU Braunschweig (1998-2004), followed by doctoral research at RWTH Aachen's Institute of Control Engineering where he earned his Dr.-Ing. in 2010 with a dissertation on predictive energy management for hybrid vehicles. Prior to his current professorship, he held progressive roles at FEV Europe GmbH from 2009-2018, culminating as Senior Project Manager for Vehicle and Powertrain Electronics. Beck's research focuses on control engineering systems with particular emphasis on automation technology, regulation systems, and model-based development approaches. His work bridges theoretical control methodologies with practical automotive applications, especially in hybrid vehicle energy management, multi-robot systems, and intelligent air path control. The Modellfabrik Fab21 serves as his primary experimental platform for model-based development applications. His publication record since 2005 demonstrates consistent contributions to control engineering, particularly in hybrid vehicle systems, emission control optimization, and calibration methodologies. Recent work shows increasing focus on distributed robotics and intelligent transportation systems, reflecting evolving research directions while maintaining core expertise in control theory applications. As an educator, Beck teaches foundational and advanced courses including Electrical Engineering III, Control and Regulation Technology, Model-Based Development, Technical Mechanics, and Advanced Control Engineering at the Master's level. His teaching integrates theoretical concepts with practical laboratory applications through the university's Moodle platform, emphasizing hands-on implementation of control algorithms and system modeling techniques.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +12 more

Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols

Sandra KeiperView profile

Lecturer

• Compressed Sensing
• Sparse Signal Recovery
• Numerical Linear Algebra
• +5 more

Sandra Keiper is a Lecturer at the Institute of Mathematics within Faculty II - Mathematics and Natural Sciences at Technical University of Berlin. She has held academic positions since at least 2011, including roles as Tutor, Assistant, and Lecturer, with teaching responsibilities in Analysis, Linear Algebra, and Partial Differential Equations for both mathematicians and engineers. Research interests include: Compressed Sensing and Sparse Signal Recovery Numerical Linear Algebra with applications to high-dimensional data Wavelet and curvelet transforms for geometric multiscale analysis Approximation theory for finite-valued and cartoon-like functions Deep learning and graph approximation techniques Professional activities : Active in teaching since 2011 (Analysis I-III, Functional Analysis, Integral Transforms) Supervising theses since 2015 on topics like Compressed Sensing and Deep Learning Invited lectures at Caltech, ETH Zurich, and Alan Turing Institute Research stays at Hausdorff Institute, ETH Zurich, and Duke University

• Machine Learning
• Educational Data Mining
• Time Series Forecasting
• +5 more

Irena Koprinska is a prominent researcher at the University of Sydney with over 150 publications from 1996 to 2025. Her work spans multiple interdisciplinary domains with significant contributions to machine learning applications in educational technology, time series forecasting, and health informatics. She maintains strong research collaborations, particularly with Kalina Yacef (38 joint publications), Mashud Rana (26 papers), and Bryn Jeffries (22 papers), indicating leadership in her research group. Her research interests focus on practical applications of machine learning across diverse domains. In educational data mining, she has pioneered methods for predicting student performance in programming courses, analyzing syntax errors, and developing automated hint generation systems. Her work in time series forecasting has made significant contributions to solar power prediction using advanced neural network architectures. Additionally, she has applied machine learning techniques to medical domains, particularly in sleep disorder detection and analysis. The analysis of her 15 most recent publications (2022-2025) reveals a continued focus on educational technology and time series analysis, with increasing attention to interpretable methods and health applications. Her work demonstrates a consistent trajectory of applying sophisticated machine learning techniques to solve real-world problems across multiple domains, with particular emphasis on creating practical tools for education and renewable energy management. Notable Research Contributions: Development of the HINTS framework for automated programming hint generation Innovative approaches to multistep-ahead time series forecasting Applications of deep learning to sleep disorder detection Methods for predicting student performance in programming education Her publication record in top venues including Machine Learning journal, AIED, EDM, and IJCNN demonstrates significant impact in both machine learning and educational technology communities. The consistent output of high-quality research over nearly three decades indicates sustained scholarly productivity and leadership in her fields of expertise.

• Global Navigation Satellite Systems (GNSS)
• Autonomous Systems
• Sensor Fusion
• +4 more

Patrick Henkel is a Professor at the Technical University of Munich (TUM) affiliated with the TUM School of Engineering and Design and the Chair of Communication and Navigation. He holds a professorship in Satellite Geodesy under Prof. Hugentobler. His research focuses on advanced positioning technologies, including Global Navigation Satellite Systems (GNSS), autonomous systems, and sensor fusion. He develops algorithms for precise positioning in challenging environments such as urban areas, alpine regions, and indoor spaces. His work also extends to environmental applications, such as snow hydrology and climate monitoring using GNSS signals. Henkel’s contributions include innovations in real-time kinematic (RTK) positioning, UAV navigation, and multi-sensor integration for robotics and autonomous vehicles. His research is supported by collaborations with industry and academic partners, addressing both theoretical and applied challenges in geodesy and navigation. Henkel leads projects on GNSS signal processing, satellite-based environmental monitoring, and autonomous driving technologies. He has contributed to the Galileo HAS service and developed methodologies for snow water equivalent estimation using multi-frequency GNSS signals. His expertise spans hardware-software co-design for navigation systems and algorithm optimization for high-precision positioning in dynamic environments. He actively publishes in top-tier journals and conferences, with a focus on advancing the reliability and accuracy of navigation systems across various domains. His advising and grants include funding for projects on sensor fusion, UAV-based measurements, and satellite receiver development. He collaborates with teams at TUM’s Navigation Lab and the Professur für Satellitengeodäsie, contributing to both academic and industrial applications. His work on low-bandwidth RTK dissemination and laser-tracker verified UAV positioning highlights his commitment to bridging theoretical advancements with real-world implementation.

Serge A. ShapiroView profile

Professor

• Seismology
• Geophysics
• Rock Physics
• +5 more

Prof. Serge A. Shapiro is a Full Professor of Geophysics at Freie Universität Berlin since 1999 and Director of the PHASE consortium since 2004. He holds a Diploma in Applied Geophysics from Lomonosov Moscow State University (1982), a PhD from the Moscow Research Institute of Geosystems (1987), and a Habilitation from Karlsruhe University (1995). His research focuses on seismogenic processes, induced seismicity, rock physics, and subduction zone dynamics, with applications to geothermal energy, CO2 storage, and hydraulic fracturing. Education: Diploma in Applied Geophysics, Lomonosov Moscow State University (1982) PhD in Geophysics, Moscow Research Institute of Geosystems (1987) Habilitation, Karlsruhe University (1995) Research Interests: Induced seismicity from fluid operations CO2 storage and hydraulic fracturing risks Seismic hazard assessment Rock physics under stress Key Contributions: Developed the Seismogenic Index Model for induced earthquakes Pioneered DAS-based seismic monitoring techniques Advanced understanding of fault stability and pressure diffusion effects Awards: Virgil Kauffman Gold Medal (2013) for work in microseismic monitoring and rock physics Grants & Projects: PHASE consortium leader (2004–present) Utah FORGE EGS project advisor Labs/Teams: Seismology Group, Freie Universität Berlin PHASE university consortium

Stefano NoventaView profile

Research Fellow

• Mathematical Psychology
• Psychometrics
• Psychophysics
• +7 more

Stefano Noventa is a Research Fellow at the Methods Center, Department of Social Sciences, Faculty of Economics and Social Sciences, University of Tübingen. He has held multiple postdoctoral positions at the University of Tübingen and previously at the University of Verona and the University of Padova. Education: Ph.D. in Cognitive Psychology, University of Padova (2011) M.Sc. in Physics, University of Padova (2006) Studies in Physics, University of Padova (1999–2006) International Visiting Graduate Student, University of Toronto (2009, 2010) Dr. Noventa's research lies at the intersection of mathematical psychology, psychometrics, and psychophysics, with a focus on developing and unifying quantitative models of human cognition and assessment. His work integrates Item Response Theory (IRT) and Knowledge Space Theory (KST) to create more robust frameworks for educational and psychological measurement. He investigates latent variable models, probabilistic knowledge structures, and the identifiability of complex psychometric models, often applying these to domains such as education, organizational psychology, and entrepreneurship. His recent publications (2020–2024) demonstrate a strong trend toward theoretical integration, particularly in bridging cognitive diagnosis models with traditional psychometric frameworks. The articles emphasize mathematical rigor, model generalization, and empirical validation, with applications in both cognitive science and applied psychology. Topics include the unification of assessment models, parameter estimation under local dependence, and the modeling of intuitive physical reasoning. Scientific Awards: No awards or honors listed in the provided text. Dr. Noventa has not been explicitly mentioned as an advisor to students, but he has served as a corresponding author and collaborator on multiple research projects, indicating a leadership role in research teams. He has been involved in a DFG-funded project (GLI NON-NORM) since 2019, suggesting active grant participation. His work is highly collaborative, involving researchers from Germany, Italy, Austria, and Canada. Labs and Research Groups: Methods Center, University of Tübingen Hector Institute of Education Science and Psychology, University of Tübingen Center of Assessment, University of Verona Department of General Psychology, University of Padova

Katja RöslerView profile

Professor

• Automotive Engineering
• Modeling and Simulation
• Driver Modeling
• +12 more

Prof. Dr. Katja Rösler is a Professor of Automotive Engineering at the Institute of Mechanical Engineering, Ruhr West University of Applied Sciences since March 2012. Her career spans academic research and industrial development with key positions at TU Braunschweig, Volkswagen AG, and Fraunhofer Institute. Education: Industrial Mathematics degree completed under standard period Doctorate: Engineering (Driver Modeling) from TU Braunschweig, 2008 Her research focuses on automotive engineering with special emphasis on modeling/simulation, vehicle dynamics, driver assistance systems, accident research, alternative drives, and mobility concepts. She actively combines simulation with experimental verification and has significant involvement in Formula Student projects. Recent publications highlight her work in intelligent mobility systems (2018-2020), with particular attention to electromobility, accessibility solutions for elderly/disabled populations, and micromobility analysis. Earlier works established her expertise in driver modeling, vehicle measurement technology, and simulation-experiment correlation. Labs: Automotive Engineering Lab Teaching: Mechanics (Statics, Strength of Materials, Dynamics), Vehicle Dynamics, Driver Assistance Systems

Jochen WolfView profile

Professor

• Evolutionary Biology
• Speciation Genomics
• Genome Evolution
• +7 more

Jochen Wolf is Chair of the Evolutionary Biology Division at Ludwig-Maximilians-Universität München (LMU) and a Max Planck Fellow of the Max Planck Institute for Biological Intelligence since 2022. His research integrates evolutionary biology, genomics, and ecology to address fundamental questions about speciation, adaptation, and biodiversity across multiple biological systems. Dr. Wolf's research program applies an integrative approach to understand microevolutionary processes and genetic mechanisms underlying species divergence. His work combines large-scale genomic analyses with laboratory and field experiments to characterize genomic divergence across populations and species. Key empirical systems include natural populations of birds (particularly corvids, swallows, and cuckoos), marine mammals (pinnipeds and killer whales), plant communities, and experimental evolution in fission yeast. His research spans multiple scales from immediate microevolutionary processes to broader evolutionary patterns across time. His recent publications reveal a sophisticated integration of genomic, epigenetic, and ecological perspectives. A notable trend shows increasing focus on structural genomic variation, chromosomal rearrangements, and epigenetic mechanisms as drivers of evolutionary processes. His work demonstrates how these molecular mechanisms interact with ecological factors to shape patterns of biodiversity and adaptation. Dr. Wolf's research has gained significant recognition through publications in top-tier journals including Nature, Science, and Nature Ecology & Evolution. His groundbreaking studies on crow hybrid zones, killer whale ecotypes, and experimental evolution of speciation have been featured in prominent media outlets such as The New Yorker, The Guardian, Scientific American, and Der Spiegel, demonstrating the broad impact of his work. As Principal Investigator, Dr. Wolf actively mentors doctoral students and postdoctoral researchers, fostering the next generation of evolutionary biologists. His lab maintains strong international collaborations, particularly through affiliations with SciLifeLab in Uppsala. Research in his group is supported by multiple funding sources including German Research Foundation grants and European Union programs, enabling both fundamental research and applications to conservation biology. The Wolf lab operates within LMU's Division of Evolutionary Biology, which provides access to state-of-the-art facilities including the Leibniz Supercomputing Centre. The lab maintains strong connections with the Max Planck Institute for Biological Intelligence and SciLifeLab in Uppsala, creating a rich collaborative environment for interdisciplinary research in evolutionary genomics. This network enables comprehensive studies spanning from molecular mechanisms to ecological and evolutionary consequences across diverse biological systems.