Find Academic Supervisors

• Planetary Science
• Space Instrumentation
• Asteroid Geology
• +3 more

Andreas Nathues is a Researcher at the Max Planck Institute for Solar System Research, leading projects for interplanetary instruments such as the VenSpec DHU on EnVision (ESA) and the Framing Camera on NASA's Dawn mission. His expertise spans instrument development for space probes and data analysis of asteroids, including NEAs, Vesta, Ceres, the Moon, Venus, and Ganymede. He has held roles such as Project Lead and Co-Investigator on missions like SMART-1 (ESA), Chandrayaan-1 (ISRO), and Dawn (NASA). Education: Dr. rer. nat. in Geophysics (Freie Universität Berlin, 2000), Diploma in Geophysics (WWU Münster, 1995) Awards: NASA Exceptional Scientific Achievement Medal (2019), NASA Group Achievement Award (2016) Research focuses on Ceres' cryovolcanism, brine residues, organics, and impact processes. His work includes analyzing data from the Dawn mission, identifying surface features like Occator Crater's cryovolcanic activity and Urvara basin organics. He collaborates on upcoming missions like EnVision's VenSpec instrument. Notable contributions include co-authoring studies on Ceres' surface composition (e.g., Nat Commun , Nature Astronomy ), and advancing spectral analysis techniques for planetary surfaces.

Peter DechentView profile

Researcher

• Neurology
• Neuroimaging
• Magnetic Resonance Imaging (MRI)
• +3 more

Peter Dechent is the Head of the Research Group 'MR-Research in Neurology and Psychiatry' at the Medical Faculty of the University of Göttingen. His work focuses on combining functional MRI with non-invasive brain stimulation techniques like TMS and tDCS to study brain networks in healthy and pathological conditions. He holds a diploma in Biology and a Dr.rer.nat. from the University of Göttingen, with postdoctoral research at the Max Planck Institute for Biophysical Chemistry. Education: 1991-2001: Studies in Biology at the University of Mainz 1998-2001: Doctoral thesis at the Max Planck Institute for Biophysical Chemistry 2001-2003: Postdoc at the Biomedical NMR Research Lab Research Interests: Functional MRI integration with brain stimulation Hemodynamic processes underlying BOLD signals Application of advanced MRI techniques (structural MRI, DTI, MRS) Publications focus on neuroimaging applications in neurology and psychiatry, with recent emphasis on stimulation-induced brain activity modulation and reward system dysfunction in depression. Collaborations include studies on primate neuroimaging and genetic disorders using MRS. Awards: None explicitly listed. Lab Affiliation: Department of Cognitive Neurology (University Medical Center Göttingen). Active in GGNB programs in Systems Neuroscience and Molecular Medicine.

• Media Studies
• Gender Studies
• Film Studies
• +3 more

Jun.-Prof. Dr. Julia Bee is an Assistant Professor for Image Theory at the Faculty of Media, Bauhaus University Weimar. Her research focuses on media studies, gender/queer theory, film studies, and media anthropology. She has held visiting professorships at the Free University of Berlin (2020) and the University of Vienna (2016), and was a Mercator Fellow at the University of Siegen (2020/21). Key roles include co-spokesperson of the Gender/Queer Studies working group (2018–2020) and recipient of the 2015 Best Dissertation Award from Heinrich Heine University. Her work bridges media theory with activism, addressing topics like climate crisis, feminist film analysis, and cycling culture. Notable publications include Differenzen und Affirmationen (2020) and Fahrradutopien (2022). She actively engages in collaborative research projects, such as Auf dem Weg zum Erinnerungsort , exploring the NS history of Bauhaus buildings.

• Neuroscience
• Neuroimaging
• Brain Structure and Function
• +3 more

Dr. Gabriele Radnikow is a Researcher at the Institute of Neuroscience and Medicine (INM) within the Research Center Jülich , Germany. She is affiliated with the JARA Institute Brain structure-function relationships (INM-10) , located at Building 15.22, Room 2011 in Jülich. Her research focuses on understanding the structural and functional relationships in the brain, leveraging advanced neuroimaging and computational methods. Key areas of interest include brain organization, neuroanatomy, and the interplay between brain structure and cognitive processes. Her work contributes to interdisciplinary efforts in neuroscience, aiming to bridge gaps between anatomical insights and functional brain dynamics. While specific publications are not detailed here, her affiliation with JARA—a collaborative initiative between Forschungszentrum Jülich and RWTH Aachen University—suggests involvement in cutting-edge, team-based projects. No awards, grants, or advisees are explicitly listed in the provided information. Her role emphasizes research within the institute’s infrastructure, contributing to advancements in medical neuroscience and imaging technologies.

Susanne WenzelView profile

Research Fellow

• Artificial Intelligence
• Remote Sensing
• Machine Learning
• +3 more

Dr. Susanne Wenzel is a Research Fellow at the Structural and Functional Organisation of the Brain (INM-1) within the Forschungszentrum Jülich GmbH. She serves as the Scientific Coordinator for the Helmholtz AI Local Unit at INM-1 and the Jülich Supercomputing Centre (JSC), fostering collaboration with the Helmholtz-wide AI platform. Additionally, she leads the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL), a partnership with McGill University’s Montreal Neurological Institute and the Mila Quebec AI Institute. Her work focuses on advancing AI and high-performance computing for neuroimaging, particularly in developing 3D brain models through interdisciplinary research with CIFAR and MILA. Her research expertise spans artificial intelligence, remote sensing, and image analysis, with a strong emphasis on machine learning applications in environmental science, neuroscience, and urban scene understanding. She coordinates projects that integrate cutting-edge technologies to address complex challenges in climate modeling, neuroimaging, and spatial data analysis. Dr. Wenzel’s contributions include pioneering work in neural networks for ocean eddy tracking, sea level anomaly prediction, and facade image interpretation. Her projects emphasize interdisciplinary collaboration, combining computational methods with domain-specific knowledge across multiple institutions globally. Her current initiatives reinforce the utilization of AI and supercomputing resources to enhance scientific discovery, particularly in neuroscience through HIBALL. This collaboration aims to leverage advanced analytics for detailed brain model development, reflecting her commitment to bridging computational innovation with real-world applications.

Chang-Hoon ChoiView profile

Researcher

• Magnetic Resonance Imaging (MRI)
• Magnetic Resonance Spectroscopy (MRS)
• Ultra-High Field MRI
• +8 more

Dr. Chang-Hoon Choi is a researcher at the Institute of Neurosciences and Medicine (INM) , Forschungszentrum Jülich GmbH, Germany, with a focus on Medical Imaging Physics (INM-4) . His work bridges Magnetic Resonance Imaging (MRI) , PET-MRI hybrid systems , and RF coil instrumentation for neuroscience applications. Research Highlights: Development of double-tuned coils for 1H/X-nuclei imaging, ultra-high field MRI systems , and MR-PET hybrid technologies . Technical Expertise: Specializes in RF antenna arrays , signal optimization , and multinuclear MRI/MRS for brain studies. Key Article Trends : Over 15 recent publications emphasize coil design innovations (e.g., butterfly, dipole, and birdcage coils), hybrid MR-PET/SPECT systems , and neurochemical dynamics via tDCS-MRS integration and phosphorus/sodium imaging . Methodological advances include free water elimination in diffusion MRI , quantum filtering , and shielding techniques for UHF-MR systems . Applications : His work targets stroke , epilepsy , brain tumors , and neuroplasticity studies using preclinical animal models (rat, chick embryo) and translational hardware (e.g., 9.4T systems).

• Medical Imaging
• Neuroscience
• Radiology
• +3 more

Dr. Christoph Lerche is a researcher at the Institute of Neuroscience and Medicine (INM) , specifically in the Physics of Medical Imaging (INM-4) department at Forschungszentrum Jülich GmbH . His research focuses on advanced medical imaging technologies, particularly positron emission tomography (PET) and hybrid imaging systems like PET-MRI. Key areas include improving PET detector design, developing novel reconstruction algorithms, and applying these techniques to neurodegenerative diseases (e.g., Alzheimer’s) and oncology (e.g., glioma diagnosis). His work emphasizes interdisciplinary collaborations, such as optimizing BrainPET insert compatibility with ultra-high field (UHF) MRI systems and analyzing sleep-related effects on synaptic density. He contributes to clinical applications, such as distinguishing glioma relapse from treatment effects using FET PET radiomics, and advancing multimodal imaging (e.g., simultaneous PET/MR/EEG). Notable projects include enhancing dead-time correction for accurate BrainPET quantification, designing dipole antenna arrays for hybrid systems, and developing photon-counting CT-based PET attenuation maps. His innovations aim to bridge gaps between imaging hardware, data processing, and clinical neuroscience.

• Nanoscale Science
• Surface Chemistry
• Molecular Engineering
• +3 more

Wilhelm H. Auwärter is a Carl von Linde Junior Fellow at the Technical University of Munich (TUM) since 2010. He holds a Researcher position in the Department of Molecular Nanoscience and Chemical Physics of Interfaces. Previously, he completed his Diploma and PhD in Physics at the University of Zurich (1993-2003) and served as a postdoctoral fellow at the University of British Columbia and the École Polytechnique Fédérale de Lausanne. His research focuses on nanoscale science, particularly the geometric and electronic structure of molecular modules and their self-assembly into supramolecular architectures. He explores functional materials such as transition metal oxides and graphene to engineer low-dimensional organic nanostructures. Key areas include surface engineering, molecular design, and quantum materials. As a TUM-IAS Fellow, his publications highlight advancements in nanoporous networks, graphene interfaces, and lanthanide-directed molecular assembly. His work emphasizes surface chemistry and materials science applications in quantum systems. Selected Awards: 2004-2005 Swiss National Science Foundation Fellowship for prospective researchers. Advising and grants include contributions to the Focus Group ‘Nanoscale Control of Quantum Materials’ under Prof. Johannes V. Barth. Collaborations extend to interdisciplinary projects involving quantum technologies and molecular imaging.

Leonidas GuibasView profile

Professor

• Computer Vision
• Computer Graphics
• Machine Learning
• +5 more

Leonidas Guibas is the Paul Pigott Professor of Computer Science at Stanford University, leading the Geometric Computation group. He is a Hans Fischer Senior Fellow at the Technical University of Munich (TUM), associated with the Visual Computing Focus Group under Prof. Matthias Nießner. His research bridges computer vision, graphics, and machine learning, focusing on 3D data processing, geometric modeling, and sensor networks. Guibas holds prestigious awards including the Vannevar Bush Fellowship and ACM Allen Newell Award, and is a member of the US National Academy of Engineering and American Academy of Arts and Sciences. Education: Ph.D. from Stanford University under Donald Knuth, with prior appointments at Xerox PARC, MIT, and international institutions. His work emphasizes algorithms for sensing, reasoning, and acting in physical environments, with contributions to robotics, computational geometry, and discrete algorithms. Research interests span 3D vision (e.g., generative models for shape synthesis), deep learning architectures for spatiotemporal data, and multimodal sensor integration. Recent projects include object pose estimation, functional relationship learning in scenes, and deformation-aware 3D model retrieval. Publications highlight advancements in 3D reconstruction, point cloud analysis, and geometric deep learning. His TUM fellowship supported work on digital twins and visual computing, emphasizing high-fidelity environmental modeling. Awards also include IEEE and ACM Fellowships, underscoring his impact on theoretical and applied computer science.

• Surface Metrology
• Additive Manufacturing
• Precision Engineering
• +4 more

Professor Jane Jiang is a distinguished academic at the University of Huddersfield , affiliated with the School of Computing and Engineering and the Department of Engineering . She specializes in surface metrology, precision engineering, and advanced manufacturing technologies. Her work focuses on optimizing measurement techniques for additive manufacturing, X-ray computed tomography, and optical systems. Research Interests Her research spans surface texture analysis , 3D vision systems , and metrology for smart manufacturing . She develops novel methods for characterizing complex surfaces using techniques like phase measuring deflectometry and chromatic confocal sensors. Her contributions bridge engineering and materials science, emphasizing practical applications in aerospace, biomedical, and industrial sectors. Recent Contributions Her 2025 work includes advancements in freeform optics design, vibration-resistant microscopy, and neural network-based surface characterization. Her studies on XCT measurement for additive manufacturing parts address critical challenges in precision and data analysis. Affiliations & Projects She leads the Centre for Precision Technologies and actively collaborates on EU-funded projects. She organized the 1st International Conference on Metrology and Standard (2024) and contributes to interdisciplinary initiatives in bio-engineering and advanced materials. Grants & Teams Her team includes researchers like Shan Lou and Paul Scott , focusing on metrology for medical implants and aerospace components. She supervises PhD students exploring AI-driven surface specification and manufacturing process optimization.

Seung-Goo KimView profile

Researcher

• Neuroscience
• Cognitive Science
• Music Psychology
• +4 more

Dr. Seung-Goo Kim is a Postdoctoral Researcher at the Max Planck Institute for Empirical Aesthetics (since 2021), working under Prof. Daniela Sammler. His career spans postdoctoral roles at Duke University, University of Cambridge, and MPI for Human Cognitive and Brain Sciences, with a PhD in Psychology from University of Leipzig (2017, summa cum laude). He holds graduate degrees in Cognitive Science and dual BA in Economics/Psychology, with early training in Musical Composition. PhD: University of Leipzig & MPI for Human Cognitive and Brain Sciences Postdoc: Duke University (USA), University of Cambridge (UK) Current affiliation: Max Planck Institute for Empirical Aesthetics, Frankfurt/Main His research focuses on the neural encoding of music and speech , particularly: shared mechanisms of musical emotion and prosody; intersubject variability in musical reward sensitivity; temporal structure processing; pitch perception correlates; and applications in clinical neuroscience. Key methodologies include MEG, fMRI, and computational modeling of auditory systems. Major scientific awards include: Johanna Quandt Young Academy Fellowship (2023) Postdoc Research Grant Award (Duke University, 2021) Summa cum laude PhD (2017) Conference Scholarship (Mariani Foundation, Italy, 2014) Best Poster Presentation Award (Korean Society of Human Brain Mapping, 2010) His work includes collaborations with leading labs in neuroaesthetics , music cognition , and clinical neuroscience , with recent publications analyzing tonal hierarchy, dissonance encoding, and myeloarchitecture of impulsivity. Current projects emphasize computational models of auditory processing and empirical aesthetics.

Claudia KuenzerView profile

Researcher

• Remote Sensing
• Environmental Science
• Geoscience
• +5 more

Claudia Kuenzer is a Senior Researcher and Head of the 'Land Surface Dynamics' Research Group at the German Earth Observation Center (EOC) of the German Aerospace Center (DLR). She serves as the Overall Scientific Project Coordinator for initiatives such as WISDOM, DELIGHT, and DRAGON-3. Her expertise spans multispectral, thermal, and radar remote sensing, with a focus on environmental monitoring, land surface dynamics, and capacity-building in geosciences and resource management. She has held leadership roles in projects addressing coal fires, forest fires, and integrated watershed management in Asia. Dr. Kuenzer holds a PhD in Earth Sciences and has conducted research at institutions including the University of Trier (Germany), Western Washington University (USA), and Beijing Normal University (China). Her work integrates remote sensing with GIS to address global challenges like climate change impacts, disaster response, and sustainable resource management. She has coordinated large-scale international collaborations, notably in Southeast Asia and Europe. Key areas of focus include satellite-based monitoring of vegetation, surface water dynamics, and coastal changes. She has pioneered methodologies for analyzing long-term environmental trends using multi-decadal satellite datasets. Her contributions extend to policy-oriented applications, such as vulnerability assessments and knowledge systems for adaptive management in regions like the Mekong Delta. Her research outputs include over 150 peer-reviewed articles, focusing on remote sensing applications in forestry, hydrology, and climate science. She leads DLR’s efforts in developing innovative tools for Earth observation data analysis, including deep learning approaches for glacier monitoring and supraglacial lake dynamics. Her team also explores offshore wind energy infrastructure tracking and permafrost erosion monitoring in Arctic regions.

• Optical Communication Systems
• Medical Image Segmentation
• Blockchain Technology
• +3 more

Xueguang Yuan is a researcher with expertise in optical communication systems, medical image segmentation, blockchain technology, and IoT networks. Their work spans 2009–2024, focusing on advanced sensor design, federated learning algorithms, and secure communication protocols. Key Research Areas : Optical Time-Domain Reflectometry, Graphene Metasurfaces, Thyroid Nodule Segmentation, and Federated Learning for Multi-Institutional Collaboration Recent Publications (2024): Wearable strain sensors using MXene composites, SNR optimization in Φ-OTDR systems, and polarization conversion metasurfaces for radar cross-section reduction Notable collaborations include Yangan Zhang (optical systems), Xiaohong Huang (medical AI), and Zhifang Deng (federated learning). Trends in their 2021–2024 articles highlight applications of transformers , compressed sensing , and blockchain in healthcare, 5G networks, and security. They have contributed to 5G broadcast services, satellite routing, and edge computing platforms, though no formal awards or student mentorship details are publicly listed in the provided sources.

• Wireless Communications
• Signal Processing
• MIMO Systems
• +6 more

Claude D'Amours is a Professor in the Department of Electrical and Computer Engineering at the University of Ottawa, Faculty of Engineering. With a research career spanning over two decades, his work has significantly contributed to the fields of wireless communications, signal processing, and network security. His current research focuses on cutting-edge technologies including 5G/6G networks, UAV communications, reconfigurable intelligent surfaces, and physical layer security. Dr. D'Amours' research interests center on advanced wireless communication systems, with particular emphasis on MIMO technologies, CDMA systems, spectrum sensing techniques, and channel modeling for emerging applications. His work bridges theoretical foundations with practical implementations, addressing critical challenges in modern wireless networks including interference management, security vulnerabilities, and efficient resource allocation. Recent research has expanded into UAV communications, where he investigates channel characteristics and reliability issues in drone-based networks. Analysis of his recent publications reveals a strong focus on security aspects of wireless communications, particularly physical layer security mechanisms to counter jamming attacks and eavesdropping. His work on RF fingerprinting for device authentication in industrial IoT settings demonstrates practical applications of his theoretical research. The trend in his publications shows increasing attention to 5G/6G technologies, with particular emphasis on millimeter wave communications, hybrid precoding architectures, and novel approaches to spectrum sharing. Multiple publications on UAV-to-ground channel modeling and performance analysis Research on physical layer security mechanisms against jamming attacks Work on hybrid precoding architectures for mmWave MIMO systems Studies on RF fingerprinting for secure industrial IoT applications Investigations into spectrum sensing techniques for cognitive radio Dr. D'Amours maintains active research collaborations with numerous colleagues, particularly Michel Kulhandjian, Hovannes Kulhandjian, and François Chan, resulting in a consistent stream of high-impact publications across top-tier IEEE journals and conferences. His research group appears to focus on both theoretical analysis and practical implementation of advanced wireless communication techniques, with applications spanning from critical mission communications to industrial IoT security.

• Machine Learning
• Computer Science
• Recommender Systems
• +4 more

Xiangyang Li is a Professor at the University of Science and Technology of China, School of Computer Science and Technology. His work spans interdisciplinary domains including computer science, machine learning, and geoscience. Research Focus: Machine Learning, Recommender Systems, Blockchain, and Computer Vision. Key Contributions: Development of novel algorithms for UWB positioning, code information retrieval benchmarks, and vision-language models. Recent publications highlight trends in large language model (LLM) integration for recommendation systems, quantum-inspired optimization, and cross-chain consensus models. His 2025 work includes collaborations on semantic-driven inference, prompt tuning, and hybrid BFT consensus for blockchain scalability.