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Christian TetzlaffView profile

Professor

• Computational Neuroscience
• Synaptic Physiology
• Neuromorphic Engineering
• +4 more

Prof. Christian Tetzlaff holds a W2 Professorship at the Department for Neuro- and Sensory Physiology, University Medical Center Göttingen. He leads the Computational Synaptic Physiology group, focusing on integrating computational methods to understand neural systems' principles and translating these into technologies like robotics and neuromorphic engineering. His work spans scales from synaptic protein interactions to large neural networks, leveraging disciplines such as nonlinear dynamics and machine learning. Educational Background: 2009: Diploma in Physics, Georg-August University Göttingen 2013: PhD in Physics (Dr.rer.nat.), Georg-August University Göttingen Research Leadership & Grants: Principal Investigator in EU Horizon 2020 Projects (Plan4Act, ADOPD, Human Brain Project's FIPPA) Lead in DFG-funded SFB 1286 (Quantitative Synaptology) INRC member (Intel Neuromorphic Research Community) BMBF-funded KISSKI project (2022–present) Research Interests: His lab explores synaptic mechanisms underlying learning, memory consolidation via synaptic tagging-and-capture, and neuromorphic hardware implementations. Recent work emphasizes calcium signaling in synapses, memristive devices for synaptic working memory, and robust trajectory generation using neuromorphic chips like Loihi. Affiliations: Co-coordinator of Plan4Act (2017–2021) and active in the Göttingen Graduate Center for Neurosciences (GGNB). Collaborates globally with institutions like the Weizmann Institute and Columbia University. Laboratory: Visit tetzlab.com for lab activities and open-source tools like Plastic Arbor .

• Computer Architecture
• Hardware Design
• Signal Processing
• +7 more

Prof. Dr.-Ing. Holger Blume serves as Vice President for Research and Transfer at Leibniz University Hannover while maintaining his academic position as Professor in the Architectures and Systems Section within the Faculty of Electrical Engineering and Computer Science. He holds multiple leadership positions including Chairperson of the Research Commission and Central Ethics Committee, Executive Board member of eNIFE (Leibniz Research Initiative for Neurosciences), and membership in both the Laboratory of Nano and Quantum Engineering and L3S Research Centre. His research interests span computer architecture, hardware design, signal processing, AI accelerators, hearing aid technology, and biomedical engineering. His work bridges theoretical computer science with practical applications in automotive systems, medical devices, and quantum engineering. Professor Blume's research demonstrates strong interdisciplinary connections between electrical engineering, computer science, and biomedical applications, with particular emphasis on hardware-oriented solutions for real-world problems. Analysis of his recent publications (2023-2025) reveals a strong focus on hardware acceleration for AI and signal processing applications, particularly in automotive radar/LiDAR systems and hearing aid technology. His work shows consistent innovation in RISC-V processor design, specialized hardware for mathematical functions, and biomedical applications of engineering principles. The research demonstrates a clear trajectory toward energy-efficient, specialized computing architectures for specific application domains. As Vice President for Research and Transfer, Professor Blume oversees significant research initiatives at Leibniz University Hannover, which hosts multiple Clusters of Excellence including PhoenixD (Photonics, Optics, and Engineering), QuantumFrontiers, and Hearing4all. The university participates in numerous collaborative research centers and junior research groups funded by DFG, BMBF, and EU programs. Professor Blume is actively involved in multiple research facilities including the Laboratory of Nano and Quantum Engineering and the L3S Research Centre. His work connects with Leibniz University's research focuses on optical technologies, quantum optics and gravitational physics, and biomedical research and technology. His leadership positions indicate strong involvement in shaping the research strategy and ethical framework of the university's scientific endeavors.

Jürgen HesserView profile

Professor

• Medical Imaging
• Computational Oncology
• Inverse Problems in Science
• +7 more

Jürgen Hesser is a Professor at the Mannheim Medical Faculty , Heidelberg University, specializing in Experimental Radiotherapy and Medical Imaging . His research focuses on solving inverse problems in imaging, particularly for CT reconstruction , brachytherapy planning , and low-dose imaging . Current affiliations: Clinic for Radiotherapy and Radiooncology, Mannheim University Hospital Collaborative ties: Interdisciplinary Center for Scientific Computing (IWR) and Center for Bioinformatics (ZITI) at Heidelberg University Research interests center on anisotropic total variation techniques for medical and industrial applications, including MR-guided interventions and real-time radiation therapy . His work has led to a 1000x speed improvement in brachytherapy planning algorithms. Recent publications highlight expertise in image reconstruction (CT/X-ray), noise optimization , machine learning for cancer classification, and big data management solutions. His methods are applied to both clinical and industrial imaging challenges. Additional contributions include scientific data infrastructure development and variance stabilization techniques for medical sensors. The research group maintains strong interdisciplinary links with Physics, Mathematics, and Computer Science faculties.

Rishabh DabralView profile

Researcher

• 3D Computer Vision
• Computer Graphics
• Multi-Modal Interactions
• +6 more

Rishabh Dabral is a Research Group Leader at the Max Planck Institute for Informatics since August 2024, leading the "3D Visual Intelligence" group. He is also affiliated with the Research Training Group on Neuro-Explicit Models of Language, Vision, and Action at Saarland University. Expertise: 3D computer vision, computer graphics, human-object interaction modeling, and motion synthesis. Leadership: Conducts cutting-edge research on 3D human performance capture and physical plausibility in motion. His research focuses on: 3D human pose estimation under gravity constraints Multi-modal gesture synthesis using neural architectures Quantum auto-encoding for 3D representations Wearable robotics informed by human behavior Temporal dynamics in human-object interaction Recent publications at top venues like SIGGRAPH , CVPR , and ICCV demonstrate his work on: Music-driven motion synthesis Egocentric motion capture systems Reactive two-person interaction models Diffusion-based gesture generation Object-aware motion prediction Wearable robotic limb design

• Signal Processing
• Machine Learning
• Neural Networks
• +3 more

Professor Danilo P. Mandic, affiliated with Imperial College London, UK, is a leading researcher in signal processing, machine learning, and biomedical signal analysis. His work spans quaternion algebra, tensor networks, and neural networks for real-world applications. 2025: Published 11+ works on EEG/PPG analysis, quantum learning, and tensor-based LLM compression 2024: Active in interpretable transformers, graph learning for financial data, and hearable devices Research focuses on hypercomplex signal processing, graph neural networks, and medical AI applications. Recent work explores quaternion calculus for signal processing, tensor network structures for LLMs, and hearable device optimization. Key publication trends include: 2025 emphasis on quantum-aware learning, 2024 graph-based time series clustering, and 2023 foundational work on graph CNNs and matched filtering approaches. Collaborates extensively with Dongpo Xu, Sayed Pouria Talebi, Clive Cheong Took, and Tobias Reichenbach on projects involving ear-EEG, ECG enhancement, and financial sentiment analysis.

• Computer Vision
• Artificial Intelligence
• Multimodal Learning
• +4 more

Ke Yan is an Associate Professor in the Department of Computer Science at the National University of Singapore's College of Design and Engineering. With extensive research output from 2021-2026, their work spans computer vision, artificial intelligence, and multimodal learning systems. National University of Singapore (Primary Affiliation) Collaborations with University of Electronic Science and Technology of China Research ties with Tencent Youtu Lab in Shanghai Research focuses on advancing computer vision techniques, particularly in medical image analysis, vision-language integration, and fault diagnosis systems. Their work bridges theoretical AI development with practical applications in healthcare, industrial systems, and environmental monitoring. Notable contributions include novel approaches to multimodal learning, medical image segmentation with sparse annotations, and improving reliability of large vision-language models. Recent publication trends (2024-2026) demonstrate increasing focus on multimodal systems, with significant contributions to vision-language model reliability, medical AI applications, and efficient transfer learning techniques. Their work frequently addresses critical challenges like hallucination mitigation in large models and sparse data scenarios in medical imaging. As an advisor, they mentor multiple researchers including Junlong Du, Shouhong Ding, and Zhiwen Lin, with whom they frequently collaborate on cutting-edge computer vision projects. Their research group maintains strong industry connections, particularly with Tencent's AI research division. The research is conducted within NUS's computer vision and AI research ecosystem, collaborating with multiple laboratories focused on multimodal intelligence and practical AI deployment in real-world systems.

• Image processing
• Inverse problems
• Radio interferometric imaging
• +7 more

Audrey Repetti is an Associate Professor in the Department of Actuarial Mathematics and Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University in Edinburgh, UK. She also holds a dual affiliation with the Institute of Sensors, Signals, and Systems in the School of Engineering and Physical Sciences, and is part of the Maxwell Institute for Mathematical Sciences - Edinburgh. Her research spans mathematical imaging, optimization, and computational methods with applications across astronomy, medical imaging, and optical engineering. Dr. Repetti's research focuses on developing advanced mathematical frameworks for solving imaging inverse problems. Her work centers on optimization algorithms, Bayesian uncertainty quantification, and the integration of machine learning with traditional mathematical approaches. She has made significant contributions to radio interferometric imaging, computational optical imaging with photonic lanterns, and uncertainty quantification in medical imaging. Her research bridges theoretical mathematics with practical applications in astronomy, healthcare, and engineering. Analysis of her recent publications reveals a clear trajectory toward integrating traditional mathematical imaging approaches with modern machine learning techniques. Her work increasingly focuses on 'hybrid' methodologies that combine data-driven models with optimization frameworks. Key themes include plug-and-play algorithms, uncertainty quantification in imaging, and the development of efficient computational methods for high-dimensional inverse problems. Her research demonstrates strong interdisciplinary connections between mathematics, signal processing, astronomy, and medical imaging. Dr. Repetti is actively involved in academic service, including co-organizing the 2026 ICMS Workshop on Imaging inverse problems and generating models. She has received research funding supporting her work in computational imaging and inverse problems, though specific grant details aren't listed in the provided materials. Her teaching portfolio includes advanced courses in scalable inference, deep learning, and statistics for sciences. She leads several research projects with associated software toolboxes including BUQO (Bayesian Uncertainty Quantification by Optimization), SARA-COIL (Compressive optical imaging with a photonic lantern), and CALIM (Self direction-dependent effect calibration and imaging in radio-interferometry). These projects demonstrate her commitment to developing practical computational tools that advance both theoretical understanding and real-world applications in imaging science.

• Computer Science
• Machine Learning
• Image Processing
• +4 more

Yang Cao is a Professor at the University of Science and Technology of China , Department of Automation, Hefei, China. He holds a PhD from Northeastern University (2004, Shenyang, China) and has active affiliations with institutions like Virginia Tech and Huazhong University of Science and Technology. Research Focus: Spatiotemporal modeling, event-based vision, 3D human-object interaction, and industrial defect detection. Publications: 15 recent articles highlight his work in diffusion models, transformers, and state-space networks for tasks like traffic emission imputation, eye tracking, and PCB defect detection. Collaborative Work: Co-authored with Zheng-Jun Zha, Wei Zhai, Yu Kang, and others in journals like IEEE Transactions on Neural Networks and CVPR Workshops. Scientific Contributions: His research bridges computer vision, machine learning, and industrial applications, emphasizing real-world challenges such as low-light enhancement and sensor fusion.

Ulrike Pestel-SchillerView profile

Researcher

• Hyperspectral Imaging
• Image Processing
• Video Coding
• +2 more

Dr. Ulrike Pestel-Schiller is a researcher at the Institute for Information Processing, Leibniz University Hannover, Germany, where she has been employed since 1996. Her work focuses on hyperspectral and Synthetic Aperture Radar (SAR) image processing, coding, and evaluation, with significant contributions to remote sensing applications. She actively supervises bachelor's and master's theses in these fields. Her academic background includes: Electrical Engineering and Communications Engineering studies at University of Hannover Dipl.-Ing. (Master's equivalent) awarded in 1989 Dr.-Ing. (Doctorate) completed in 1997 with dissertation on filter bank optimization for subband coding Her research centers on hyperspectral image data processing, coding efficiency, and usability evaluation for human interpreters. She investigates how compression techniques (HEVC, JPEG) impact SAR image usability, often finding counterintuitive results where compression improves interpretability. Recent work integrates deep learning, particularly CNNs, for spectral-spatial analysis in hyperspectral data and fruit classification. Her early career focused on HDTV video coding standards development. Analysis of her publication trends reveals a clear evolution from foundational HDTV subband coding research (1990s) to contemporary hyperspectral/SAR applications. A dominant theme is human-centered evaluation of compressed imagery, with 70% of her 2018-2023 publications examining interpreter performance. She increasingly employs deep learning for band selection and semantic segmentation, while maintaining core expertise in image compression algorithms. No scientific awards were documented in the source material. Dr. Pestel-Schiller supervises undergraduate and graduate theses in hyperspectral/SAR processing but no specific grant funding or formal advising records were provided. Her research appears institutionally supported through the Institute for Information Processing. The Institute for Information Processing serves as her primary research base, collaborating on projects involving drone remote sensing, VideoSAR stabilization, and hyperspectral band optimization. Current work emphasizes practical applications where image compression directly impacts interpreter effectiveness in remote sensing scenarios.

Felix SchneiderView profile

Researcher

• Structural Dynamics
• Uncertainty Quantification
• Bayesian Methods
• +3 more

Felix Schneider is a Researcher at the Chair of Structural Mechanics at Technical University of Munich since 2018. He holds an M.Sc. in Civil Engineering from TU Munich (2018) and completed a semester abroad at the Norwegian University of Science and Technology. His research focuses on uncertainty analysis in structural and acoustic models, Bayesian updating techniques, and stochastic reduction methods in the frequency domain. He has contributed to advancements in rational polynomial chaos expansions and sparse Bayesian learning for structural dynamics applications. Education: 2011-2018: Study of Civil Engineering at Technical University of Munich 2018: Master of Science (M.Sc.) in Civil Engineering 2016: Semester abroad at Norwegian University of Science and Technology 2009: Abitur at Humboldtgymnasium Solingen Research emphasizes computational methods for structural dynamics, including: Bayesian parameter estimation for linear systems Rational surrogate models for uncertainty quantification Frequency-domain stochastic reduction techniques Applications in seismic risk assessment and vibroacoustic simulations Key contributions include a Maurer Söhne Prize for his Master's thesis and a published MATLAB toolbox for rational polynomial chaos expansions on the LRZ Git repository. Teaching responsibilities include courses on random vibrations, structural dynamics, and continuum mechanics. He has participated in research stays at ETH Zurich (2023) and collaborates with international institutions on uncertainty quantification projects.

• Computer Vision
• Photometric Stereo
• 3D Reconstruction
• +3 more

Fumio Okura is a professor at Osaka University , specializing in Computer Vision and 3D Reconstruction . His work bridges Photometric Stereo , Neural Rendering , and Medical Imaging , with a focus on cognitive decline prediction and plant modeling . He collaborates extensively with researchers like Hiroaki Santo and Yasuyuki Matsushita . Education: Ph.D. in Computer Science (Osaka University) Research Interests span Computer Vision , Photometric Stereo , 3D Reconstruction , and Biomedical Applications . His recent work includes HoGS for object reconstruction and TreeFormer for botanical structure estimation. Publications trend toward neural rendering , reflectance modeling , and augmented reality . Notable contributions include PPGCN for cognitive detection and MVCPS-NeuS for multi-view photometric stereo. Labs & Collaborations include the Osaka University Computer Vision Lab , working with teams on photometric analysis and medical imaging .

Peter SollichView profile

Professor

• Non-equilibrium statistical physics
• Soft matter rheology
• Active matter
• +7 more

Prof. Dr. Peter Sollich is a Professor of Theoretical Physics at Georg-August-Universität Göttingen, affiliated with the Institute for Theoretical Physics. His research spans non-equilibrium statistical physics with applications to soft matter, active systems, and complex networks. He maintains a small part-time appointment at King's College London. His primary research interests focus on non-equilibrium statistical physics , particularly soft and active matter rheology, jamming transitions, glassy dynamics, dynamical phase transitions, and inference from dynamical data. His work bridges theoretical physics with applications in materials science and network theory, emphasizing both fundamental mechanisms and quantitative modeling approaches. Analysis of his recent publications reveals strong thematic consistency in studying glassy dynamics and active matter systems , with increasing integration of machine learning techniques for network analysis. Key methodological threads include coarse-grained modeling, spectral analysis of complex systems, and non-equilibrium thermodynamics frameworks. His 2023-2025 work shows growing emphasis on nonreciprocal interactions in active mixtures and physics-inspired machine learning applications. Prof. Sollich actively supervises Bachelor's, Master's, and PhD students, welcoming thesis inquiries in theoretical physics. His group develops analytical and computational approaches to complex dynamical systems, with recent grants likely supporting work on network dynamics and active matter modeling (specific grants not detailed in source text). His research group operates within the Institute for Theoretical Physics at Göttingen, focusing on computational and analytical modeling of disordered systems. Current projects involve elastoplastic modeling of amorphous solids, spectral analysis of heterogeneous networks, and theoretical frameworks for active matter phase separation.

• Biomedical Engineering
• Machine Learning
• Wearable Technology
• +3 more

Muhammad Awais Bin Altaf is a researcher specializing in biomedical engineering, machine learning, and wearable technology. His work focuses on low-power embedded systems for neurological and cardiovascular monitoring, including EEG processors for seizure detection and PPG-based blood pressure classification. He has collaborated extensively with co-authors like Wala Saadeh and Jerald Yoo on IEEE journals and conferences. His research interests include Biomedical signal processing Wearable health devices Machine learning for medical diagnostics Energy-efficient hardware design Neurological disorder detection Embedded systems for clinical applications Recent publications highlight trends in shallow neural networks, autoencoders, and hardware acceleration for real-time health monitoring. Key subfields span seizure prediction, stress detection, and impedance-adaptive sensors. Collaborations include institutions in Germany, Finland, and Pakistan. His work often integrates open-source toolflows and industry-standard chip design techniques, emphasizing practical implementations for wearable environments. Contributions to HDR imaging algorithms and biomedical SoCs demonstrate interdisciplinary expertise in signal processing and healthcare technology.

• Antenna Engineering
• Radar Technology
• Electromagnetic Fields
• +3 more

Prof. Dirk Heberling is a Universitätsprofessor at RWTH Aachen University, leading the Institute for High Frequency Technology. His research focuses on advanced antenna systems, radar technology, and electromagnetic field exposure assessment in 5G/6G networks. He specializes in high-frequency components, including leaky-wave antennas, reconfigurable intelligent surfaces, and robot-based antenna measurement systems. His work addresses challenges in antenna design, signal processing, and environmental compliance for next-generation communication infrastructure. Key research areas include: Development of wideband antennas for IoT and smart building applications Numerical analysis of near-field antenna measurements and phase recovery techniques Assessment of radio frequency exposure from massive MIMO base stations using digital twins Integration of radar systems with automotive technologies for self-localization and object detection Optimization of antenna arrays to mitigate grating lobes and phase synchronization issues His lab operates a state-of-the-art robot-based mm-wave test system capable of spherical near-field measurements and real-time auralization of aircraft noise. This infrastructure supports both fundamental research and industry collaborations in telecommunications, automotive radar, and environmental monitoring. Current projects emphasize: Exposure modeling for 6G networks considering increased base station utilization Generative adversarial networks for radar data synthesis Calibration techniques for polarimetric radar systems

A. Lee SwindlehurstView profile

Professor

• Signal Processing
• Wireless Communications
• Biomedical Applications
• +3 more

A. Lee Swindlehurst is a Professor and Associate Dean for Research and Graduate Studies at the Henry Samueli School of Engineering of the University of California, Irvine . He is also a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study , where he collaborates on next-generation wireless communications research. Ph.D., Stanford University (Electrical Engineering, 1991) M.S., Brigham Young University (Electrical Engineering, 1986) B.S., Brigham Young University (Electrical Engineering, 1985) Swindlehurst's research focuses on sensor array signal processing for radar, wireless communications, and biomedical applications. His work addresses detection and estimation theory , system identification, and channel modeling, with recent emphasis on massive MIMO systems , low-resolution hardware, and physical layer security . His 2018-2017 publications demonstrate technical advances in precoding algorithms , channel estimation , and ADC/DAC optimization for emerging wireless architectures. IEEE Fellow (2004) IEEE Signal Processing Society Best Paper Awards (2010, 2006) Stephen O. Rice Prize in Communications Theory (2006) IEEE W.R.G. Baker Prize Paper Award (2000) Karl G. Maeser Research Award (2004) His research has produced foundational work on space-time block codes , vector-perturbation techniques , and blind equalization methods that continue influencing modern wireless system design.