Find Academic Supervisors

• Computational Mathematics
• Biomedical Imaging
• Optoacoustic Imaging
• +5 more

Dominik Jüstel holds a tenure-track position at the Institute for Biological and Medical Imaging and the Institute for Computational Biology at Helmholtz Munich, and is affiliated with the Chair for Biological Imaging at TUM. He leads the Jüstel Lab, focusing on computational methods for optoacoustic imaging and sensing, bridging computational mathematics with clinical applications in biomedical imaging. He earned a doctoral degree (Dr. rer. nat.) with highest distinction from TUM for work on mathematical models in molecular X-ray diffraction imaging. His research emphasizes clinical translation of optoacoustic technology through advanced computational tools, including machine learning and AI. Key awards include the ERC Starting Grant ‘EchoLux’ (2021) , Medical Valley Award (2020) , and VIP+ (2022) . His work collaborates with clinical institutions and industry partners like iThera Medical, targeting applications such as breast cancer detection, peripheral neuropathy imaging, and non-invasive blood glucose monitoring. Grants include the ERC Starting Grant and the Tenure Track ‘AI in Optoacoustics’ initiative. His lab develops cutting-edge methods for optoacoustic reconstruction, spectral analysis, and deep learning integration to enhance clinical imaging accuracy and real-time performance. He is a driving force in TranslaTUM’s interdisciplinary research, advancing optoacoustic technology’s clinical adoption through computational solutions for translational challenges.

• Learning and Memory
• Neuronal Circuits
• Neurological Disorders
• +2 more

Dr. Stefanie Poll is a Junior Professor at the University of Bonn , affiliated with the Institute for Experimental Epileptology and Cognition Research and part of the Transdisciplinary Research Area Life and Health . Her work focuses on deciphering neuronal circuits in spatial/social cognition and CNS tumor-microenvironment interactions. Affiliation: University Hospital Bonn (UKB), Institute for Experimental Epileptology and Cognition Research Research Tools: Two-photon in vivo microscopy, transgenic mice, neuronal activity manipulation Her research explores neurodegenerative impairments in memory networks and glioblastoma communication pathways using advanced imaging and behavioral assays. Recent publications highlight Alzheimer's memory trace disruptions and hippocampal synaptic plasticity mechanisms. Key project SecureNeuroAI (€2.5M BMFTR funding) develops AI-driven seizure detection systems with real-time cybersecurity safeguards.

• Machine Learning
• Computer Science
• Data Science
• +8 more

Haichao Wang is a researcher affiliated with multiple institutions, including Shanghai University of Electric Power, Henan Polytechnic University, Army Engineering University of PLA, and Huazhong University of Science and Technology . His work spans interdisciplinary applications in Machine Learning, Embedded Systems, Environmental Science, and Biomedical Engineering , with a focus on k nowledge distillation, rough set theory, and signal processing. Research interests include sleep staging using neural networks, defect detection in industrial settings, acoustic analysis of wind turbines , and environmental monitoring . Recent publications highlight trends in multi-modal learning, adaptive thresholding, and 5G-enabled geospatial modeling . The 15 most recent articles reflect a blend of deep learning architectures, remote sensing, and biomedical applications . His collaborations extend to institutions in China and international scholars, emphasizing interdisciplinary solutions for technical and environmental challenges.

• Computer Science
• Electrical Engineering
• Machine Learning
• +3 more

Xue Jiang is a faculty member at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering, China. Their research focuses on interdisciplinary areas including machine learning, signal processing, biomedical engineering, and cybersecurity. With a prolific publication record, they specialize in neural networks, fault diagnosis systems, federated learning, and brain-computer interfaces. Key research interests include developing intelligent algorithms for fault detection in mechanical systems, advancing medical imaging techniques through deep learning, and enhancing privacy in distributed data frameworks. Their work spans applications in robotics, cybersecurity, and bioinformatics. Notable contributions include adversarial robustness studies for EEG-based systems and PML-based computational methods for wave propagation analysis. Publications highlight innovations across engineering and computer science, with a focus on practical implementations in real-world systems. Collaborations with institutions like the Hong Kong University of Science and Technology and Harbin Institute of Technology reflect their extensive academic network.

Regina Pohle-FröhlichView profile

Professor

• Image Processing
• Pattern Recognition
• Computer Vision
• +5 more

Regina Pohle-Fröhlich is a Professor of Computer Science and Graphical Data Processing at the Department of Electrical Engineering and Computer Science, Niederrhein University of Applied Sciences. Her research focuses on image processing, pattern recognition, computer vision, and medical imaging applications. Education: Diplom-Ingenieurin in Materials Engineering (1989), PhD in Image Analysis of Welding Defects (1995), Habilitation in Medical Image Analysis (2004) Her work spans applications in neuromorphic event cameras for outdoor monitoring, 3D city modeling, and biomedical image analysis. She collaborates extensively with institutions like the University of Cologne, German Aerospace Center, and Charité Berlin. Recent research trends include deep learning for event stream segmentation, semantic scene filtering, and advanced 3D reconstruction techniques. She actively participates in the iPattern Institute for Pattern Recognition and the Promotionskolleg NRW graduate institute. Key Projects: MLPCO (Machine Learning for Crop Output), KIRaPol.5G (AI for Radar Surveillance), BeeVision (Bee Population Monitoring), and EU/DFG-funded agricultural sustainability initiatives.

• Phonetics
• Speech Processing
• Linguistics
• +3 more

Dr. Esther Kunay is a scientific researcher at the Institute of Phonetics and Speech Processing (IPS) at Ludwig Maximilian University of Munich. Her current role involves leading the DFG-funded project on nasal coarticulation and sound change using real-time MRI technology. She holds a PhD (Dr. phil.) and specializes in experimental phonetics, articulatory dynamics, and the interplay between physiological speech production and linguistic evolution. Her research focuses on real-time visualization of articulatory movements via magnetic resonance imaging to study how nasal coarticulation patterns influence sound change processes. This interdisciplinary work bridges speech physiology, acoustic phonetics, and historical linguistics. Dr. Kunay's affiliation with LMU's IPS positions her within a renowned institution dedicated to speech processing research. Her contact details include the Schellingstr. 3/II campus location and institutional email address.

Guillaume LandryView profile

Professor

• Radiation Oncology
• Medical Physics
• Artificial Intelligence in Healthcare
• +3 more

Prof. Guillaume Landry is a Professor in the Department of Radiation Oncology at the Medical Center of Ludwig-Maximilians-Universität München. His research focuses on advancing image-guided radiation therapy (IGRT), particularly through AI-driven innovations in MRI-guided radiotherapy (MRgRT), proton imaging, and adaptive treatment planning. Key areas include deep learning applications for autosegmentation, real-time tumor tracking, and reducing radiation exposure via advanced imaging techniques like photon-counting CT. His work spans clinical trials such as the PUMA trial for MRI-guided adaptive radiotherapy in lung cancer and the SAVE-HEART study evaluating surface-based radiotherapy for breast cancer. He also explores AI frameworks like foundation models for motion management and open-source solutions for multi-institutional collaboration. Prof. Landry’s contributions address challenges in dosimetry, toxicity prediction (e.g., radiation pneumonitis), and improving treatment precision through real-time imaging and data analytics. Notable projects include developing lung phantoms for proton CT, optimizing gating systems for motion management, and validating AI models for clinical implementation. His research bridges cutting-edge technology with clinical needs, emphasizing translational applications in radiation oncology.

• Deep Learning
• Computer Vision
• Acoustic Signal Analysis
• +5 more

Prof. Dr.-Ing. Christian Bergler is a Professor at the Department of Electrical Engineering, Media and Computer Science at Ostbayerische Technische Hochschule Amberg-Weiden. His academic focus lies in Deep Learning , with applications spanning domains like Computer Vision, Acoustic Analysis, and Natural Language Processing. He holds a summa cum laude doctoral degree from Friedrich-Alexander-University Erlangen-Nürnberg (FAU), specializing in Deep Learning Applied to Animal Linguistics. Research & Teaching Leading research on Deep Learning paradigms including Transfer Learning, Federated Learning, and Representation Learning. Develops tools for animal communication analysis (e.g., ORCA-SPOT, ANIMAL-CLEAN) and medical signal processing. Teaches advanced modules in Artificial Intelligence, Data Engineering, and Natural Language Processing. Awards & Contributions Recipient of highest academic honor: summa cum laude doctoral thesis on Animal Linguistics. Co-founded OrasTEC GmbH , a startup applying AI to industrial and service sectors. Member of the University Council and Senate at OTH Amberg-Weiden. Key Projects Pioneered frameworks for automated killer whale sound detection/classification (ORCA-SPOT/ORCA-SLANG) and contributed to open-source tools like ANIMAL-CLEAN for signal enhancement. His work has been featured on German television (3Sat/TerraX) and published in journals like Scientific Reports and Ecological Informatics .

Bodo BernsdorfView profile

Lecturer

• Geoinformatics
• Risk Management
• Remote Sensing
• +3 more

Bodo Bernsdorf is a Lecturer in Geoinformatics and Spatial Analysis at Technische Hochschule Georg Agricola (THGA) and Karlsruhe University of Applied Sciences, specializing in Geomonitoring in mining, risk management, and geospatial technologies. He also serves as a Senior Consultant at the Post-Mining Research Center and holds leadership roles in fire department and emergency response initiatives. His career spans over three decades, with expertise in geoinformatics, remote sensing, and disaster management. Education: PhD in Physical Geography from the University of Trier (2000), with research on snowmelt erosion in Canada. Earlier studies at the University of Cologne and Trier. Research Interests: Integration of UAVs, thermal imaging, and sensor networks for environmental monitoring, risk assessment in gas infrastructure, and spatial data applications in firefighting and disaster response. Key areas include geospatial data infrastructure, emergency routing systems, and interdisciplinary approaches to safety and environmental management. Recent Research Trends: Focus on unmanned aerial vehicles (UAVs) for gas leak detection, satellite-based flood monitoring, and geospatial tools for emergency services. His work bridges academic research with practical applications in public safety and environmental policy. Professional Memberships: President of the German Geoinformation Association (until 2009), leader of the Research Committee in the VdF NRW e.V., and key roles in the vfdb e.V. communications committee. Advisory Roles: Project leader in gas safety (KaMonSys), routing optimization for volunteer fire departments, and geospatial infrastructure development for municipalities. Labs/Teams: Active in the Post-Mining Research Group and collaborates with institutions like the Institute of the Fire Department NRW and the EFTAS Remote Sensing company.

Mihail BogojeskiView profile

Researcher

• Geometric Deep Learning
• Quantum Chemistry
• Computational Biology
• +3 more

Dr. Mihail Bogojeski is a postdoctoral researcher at the Technical University of Berlin , affiliated with the Machine Learning Group and the Berlin Institute for the Foundations of Learning and Data (BIFOLD) . His research focuses on advancing machine learning techniques for applications in the physical sciences and medical sciences , particularly in quantum chemistry , computational biology , brain-computer interfaces , and sequential data analysis . B.Sc. in Software and Information Engineering (2014), Vienna University of Technology M.Sc. in Computer Science (2017), TU Berlin Ph.D. in Computer Science (2023), TU Berlin His research integrates geometric deep learning with quantum chemistry to develop models for predicting electronic structure of molecular systems and electron densities . Recent work includes equivariant neural networks for molecular wavefunction prediction, generative time-series models for industrial aging processes, and explainable AI frameworks for catalyst discovery. Publications highlight his expertise in machine learning , quantum chemical accuracy , and multimodal data augmentation . His 15 most recent articles focus on machine learning applications in chemistry , neuroscience , and industrial process modeling , with subfields such as generative models , equivariant networks , density functional theory , and neural signal processing .

• Deep Learning
• Computer Vision
• Machine Learning
• +3 more

Dr. Ulf Krumnack is a researcher at the Institute of Cognitive Science, University of Osnabrück, affiliated with the LE Cognitive Science faculty. His research focuses on Deep Learning, Computer Vision, Machine Learning, and the theoretical foundations of analogical reasoning. He has contributed to projects on neural network architecture optimization, malaria detection via computer vision, and cognitive models for analogy-making. Krumnack teaches courses such as Advanced Topics in Deep Learning and Computer Vision, emphasizing practical applications in AI. His work integrates interdisciplinary approaches, bridging cognitive science and computational methods. Research interests include developing algorithms for structure from neural networks, visual analytics of gaze data, and ethical implications of AI. He collaborates on international projects like the I-Cog framework for integrated cognition. His publications span conferences like IEEE WACV and journals like the Journal of Computers. Teaching highlights include machine learning and computer vision seminars, alongside study projects on AI applications in animal communication. Krumnack’s contributions to cognitive systems emphasize human-like rationality and spatial cognition modeling.

• Federated Learning
• Cybersecurity
• Edge Computing
• +4 more

Dr. Meikang Qiu is a faculty member at Texas A&M University Commerce (Department of Computer Science) and Columbia University (Department of Electrical Engineering). He received his PhD in Computer Science from the University of Texas at Dallas in 2007. His research spans distributed intelligence systems with current focus areas including federated learning security, efficient AI for edge devices, and multimodal learning applications in healthcare and finance. Qiu's research integrates machine learning with systems engineering, emphasizing privacy-preserving AI , robust distributed systems , and resource-constrained optimization . His recent work explores: (1) Defenses against adversarial attacks in federated environments, (2) Quantum-classical hybrid computing frameworks, and (3) Efficient model compression techniques for large language models. Publication analysis (2024-2026) reveals three dominant themes: (1) Security enhancements for distributed learning systems, (2) Efficiency optimizations for edge AI deployment, and (3) Cross-modal applications in medical and financial domains. His work consistently addresses scalability challenges in real-world AI systems.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Jia Liu is a Professor at Tsinghua University's School of Information Science and Technology, Department of Computer Science, with a distinguished research career spanning multiple disciplines in computer science and engineering. Her extensive publication record demonstrates leadership in artificial intelligence, machine learning, and their applications across diverse domains. Dr. Liu's research interests encompass the development and application of advanced computational techniques to solve complex real-world problems. She has pioneered innovative approaches in neural network architectures, medical imaging analysis, remote sensing applications, and multi-agent systems. Her work on Asym-UNet and ThreeF-Net has significantly advanced breast lesion segmentation techniques, while her contributions to digital twin technology have enhanced nuclear reactor operations and Martian exploration systems. Analysis of Dr. Liu's recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications in healthcare, environmental monitoring, defense technology, and education. Her work demonstrates consistent innovation in algorithm development, particularly in adapting machine learning techniques to domain-specific challenges. Dr. Liu maintains an active research program with numerous collaborations across academia and industry. Her work on federated learning for dual carbon initiatives reflects her commitment to addressing global sustainability challenges through technological innovation. As an educator, Dr. Liu has developed innovative teaching methodologies that integrate virtual reality and game-based learning, particularly in language education. Her research on interactive e-learning environments demonstrates her dedication to improving educational outcomes through technology.

Raymond O. WellsView profile

Professor

• Complex Analysis
• Wavelet Analysis
• Differential Geometry
• +3 more

Raymond O. Wells is a Distinguished Professor of Mathematics at the School of Computer Science & Engineering, Constructor University. Previously, he held roles as Professor of Mathematics and Vice President for External Affairs at Jacobs University Bremen, and served as Professor Emeritus at Rice University. His academic journey includes a BA in Mathematics from Rice University (1962) and a PhD from New York University (1965). His research focuses on complex analysis, wavelet theory, differential geometry, and the history of mathematics. Notable contributions include foundational work on complex manifolds, cohomology in theoretical physics, and applications of wavelet analysis in signal processing. He has authored influential books such as *Differential Analysis on Complex Manifolds* and *Wavelet Analysis: The Scalable Structure of Information*. Wells has received prestigious awards including the Guggenheim Fellowship (1974–1975) and Humboldt Senior Scientist Award. Beyond academia, he has served as President of the Boulder Philharmonic Orchestra and the Boulder Ensemble Theatre Company. His career reflects a blend of mathematical rigor and interdisciplinary engagement, spanning academic leadership, research innovation, and cultural contributions.

• Geoinformatics
• Remote Sensing
• Urban Planning
• +6 more

Prof. Dr. rer. nat. habil. Matthias Möller is a Professor at BHT Berlin University of Applied Sciences, Department III. His work focuses on geoinformatics, remote sensing, and urban development. He leads projects such as real-time monitoring of public building roof loads using meteorological data, development of land information systems for the Untersberg natural area, and creating a digital geomedia browser for historical Berlin maps. He collaborates with institutions like the Berlin State Archives, DLR, and European Space Imaging. Research interests include environmental GIS, spatial data science, and applications of satellite imagery (e.g., Copernicus Sentinel data). He advises numerous BA/MA theses on topics ranging from geocaching in education to webGIS development for disaster management. Current projects involve snow load monitoring algorithms and augmented reality for landscape assessment. Consultations are available via Zoom during his 2023 sabbatical.