جستجوی استاد راهنمای دانشگاهی

Marius Pesaventoمشاهده پروفایل

استاد

• Sensor Array Processing
• MIMO Communication
• Adaptive Beamforming
• +۴ مورد دیگر

Prof. Marius Pesavento is a Full Professor at the Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, leading the Communication Systems Group. His research focuses on sensor array processing, MIMO communication systems, adaptive beamforming, and mathematical optimization in networks. He has held academic and industry roles since 2001, including positions at mimoOn GmbH and FAG Industrial Services. Education: PhD (Doktorate) in Electrical Engineering, Ruhr-Universität Bochum (2001–2005) Master of Engineering, McMaster University (1999–2000) Dipl.-Ing. in Electrical Engineering, Ruhr-Universität Bochum (1992–1999) His research interests span robust high-resolution sensor array processing, 4G/5G mobile networks, and network information theory. Notable projects include developing tensor models for ultrasonic sensor calibration and applying machine learning to anomaly detection in network flows. His work bridges theoretical optimization and practical applications in automotive radar, 6G networks, and medical imaging. Labs/Teams: Leads the Communication Systems Group at TU Darmstadt, focusing on interdisciplinary projects in signal processing and communication systems.

Volker Kühnمشاهده پروفایل

استاد

• Wireless Communication Systems
• Information Theory
• Error-Correcting Coding
• +۴ مورد دیگر

Prof. Dr.-Ing. habil. Volker Kühn serves as Professor and Head of the Institute of Communications Engineering at the University of Rostock within the Faculty of Computer Science and Electrical Engineering. He concurrently holds the position of Vice Dean of the Faculty while directing academic operations as Chairman of both the Electrical Engineering and Medical Information Technology Examination Boards. His research integrates wireless communications theory with biomedical applications, specializing in spatial modulation techniques, information bottleneck optimization, and electrical impedance tomography signal processing. Current work focuses on machine learning-enhanced physiological monitoring systems and distributed compression algorithms for sensor networks, with significant contributions to bias-free spectral estimation from irregularly sampled data. Analysis of his 2021-2025 publications reveals three dominant research thrusts: (1) neural network applications for medical signal processing, (2) information-theoretic optimization of communication protocols, and (3) advanced spectral estimation methods for biomedical and sensor data. This interdisciplinary approach bridges communications engineering with healthcare technology development. Prof. Kühn actively mentors students as Study Advisor for Electrical Engineering and Academic Advisor for Medical Information Technology. His leadership extends to the ITG Technical Committee 5.1 on Information and Systems Theory and IEEE societies. The Institute of Communications Engineering under his direction maintains specialized laboratories for wireless communications testing, medical signal acquisition, and MIMO system prototyping, supporting both fundamental research and industry collaboration projects in 5G/6G technologies and healthcare IoT.

Gerlind Plonkaمشاهده پروفایل

استاد

• Numerical Fourier Analysis
• Wavelet Theory
• Regularization Methods
• +۳ مورد دیگر

Gerlind Plonka is a Professor of Applied Mathematics at the University of Göttingen, specifically within the Institute for Numerical and Applied Mathematics (NAM). Her research focuses on Numerical Fourier Analysis Wavelet Theory Regularization and Nonlinear Diffusion Methods Fast Algorithms and Numerical Stability Signal and Image Processing Applications Her recent publications emphasize structured subsampling in Fourier domains, Prony-type methods for exponential sum recovery, and deep learning integration in medical imaging. She supervises active PhD candidates including Benjamin Kocurov, Anahita Riahi, Yannick Nicola Riebe, and Janina Schmidt, with a legacy of advising over 50 graduates across diverse topics like Sparse FFT Algorithms Phase Retrieval Constraints Wavelet-Based Image Compression Nonlinear Diffusion Filters High-Dimensional Data Approximation

Robert Fischerمشاهده پروفایل

استاد

• Precoding and Signal Shaping
• Equalization Algorithms
• OFDM Transmission Systems
• +۷ مورد دیگر

Prof. Dr.-Ing. Robert Fischer has been a full professor for Communications and Signal Theory at the University of Ulm, Germany, since 2011. He specializes in fast digital transmission techniques, including single- and multicarrier modulation, with a focus on precoding and shaping methods for high-rate systems. Research Interests: His work spans Precoding and Signal Shaping , Equalization Algorithms , OFDM Transmission Systems , Low-Complexity Receivers , MIMO and Multiuser Communications , Ultra-Wideband , Information Theory , Channel Coding , Physical-Layer Security , and Compressed Sensing . Recent publications emphasize applications in THz communication, MIMO systems, and compressed sensing. Scientific Recognition: He has received multiple awards, including the Promotionspreis der Technischen Fakultät (1997) Literaturpreis der ITG (2000) Wolfgang-Finkelnburg-Habilitationspreis (2002) Philipp-Reis-Preis (2005) Christian Doppler Laboratory Industrial Award (2007) Best Paper Award at International OFDM Workshop (2012) Teaching: At Ulm University, he teaches Signals and Systems , Communications Engineering , and MIMO Systems . Previously, he taught courses like Kanalcodierung (Channel Coding) at University Erlangen (1998–2010) and short courses at ETH Zürich and TU Berlin.

Martin Schäferمشاهده پروفایل

پژوهشگر

• Applied Mathematics
• Harmonic Analysis
• Approximation Theory
• +۳ مورد دیگر

Dr. Martin Schäfer is a Researcher at the Chair of Applied Analysis within the Faculty of Mathematics at Chemnitz University of Technology. His research focuses on advanced mathematical frameworks such as α-molecules, coorbit space theory, and approximation methods in harmonic analysis. He earned his Diploma in Mathematics from the University of Bonn (2012) and completed his PhD at the Technical University of Berlin (2018) with a thesis on α-molecules. His work bridges pure and applied mathematics, addressing challenges in signal processing, image analysis, and high-dimensional approximation. Key research interests include approximation theory, Fourier analysis, function spaces, and the development of adaptive representation systems like shearlets and curvelets. Schäfer has contributed to conferences such as the International Conference on Multivariate Approximation (2016) and the GAMM Annual Meeting (2014), presenting on topics like phase retrieval and sparse recovery. His publications explore theoretical foundations of wavelet systems and their applications in optimal function recovery and sampling problems. Notable achievements include establishing new upper bounds for sampling numbers and developing subsampling techniques for finite frames. While no specific awards are listed, his prolific publication record in top journals like Applied and Computational Harmonic Analysis and Journal of Fourier Analysis and Applications highlights his academic impact. Schäfer collaborates internationally, evidenced by his participation in workshops in Germany, Canada, and the USA.

Götz Pfanderمشاهده پروفایل

استاد

• Numerical Harmonic Analysis
• Time-Frequency Analysis
• Gabor Systems
• +۷ مورد دیگر

Götz Pfander is a Professor of Mathematics at the Catholic University of Eichstätt-Ingolstadt , holding the Chair of Mathematics - Scientific Computing . He has held previous academic roles at Philipps-Universität Marburg (W2 Numerical Analysis), Jacobs University Bremen (Associate/Assistant Professor), and visiting positions at institutions including MIT, NYU Courant, and TU München. His leadership roles include Dean and Vice Dean of the Faculty of Mathematics and Geography (2019-2023) and Speaker of the Mathematical Institute of Machine Learning and Data Science (MIDS) since 2022. PhD in Mathematics (University of Maryland, 1999), advised by John J. Benedetto Master of Arts in Mathematics (University of Maryland, 1998) Studies in Mathematics and Psychology (Johannes Gutenberg University Mainz, Freie Universität Berlin) Pfander's research focuses on numerical harmonic analysis, operator sampling theory, and time-frequency analysis, with applications in digital communications and signal processing. His work bridges Gabor frames, wavelet transforms, and uncertainty principles to solve problems in OFDM channel modeling, sparse signal recovery, and quantum information theory. Notable contributions include: Sampling theory for pseudodifferential operators with bandlimited Kohn-Nirenberg symbols Uncertainty principles for joint time-frequency representations on finite Abelian groups Design of robust Gabor systems for wireless communication channels Wavelet-based periodicity detection for biomedical signals His recent publications (2022-2024) emphasize exponential bases for interval partitions, cube tiling constraints, and complex-valued neural network approximation. Scientific awards include the Max Kade Fellowship and John von Neumann Visiting Professor title. He serves as Editor in Chief of Sampling Theory, Signal Processing, and Data Analysis and chairs the International Conference on Sampling Theory and Applications .

Magdalena Wysockiمشاهده پروفایل

پژوهشگر

• Medical Ultrasound Imaging
• 3D Computer Vision
• Neural Radiance Fields
• +۳ مورد دیگر

Magdalena Wysocki is a researcher at Technical University of Munich's Department of Computer Science within the School of Computation, Information and Technology. She works under Prof. Nassir Navab's Chair of Computer Aided Medical Procedures at the Garching campus, focusing on medical ultrasound imaging and neural radiance fields for surgical applications. Her research integrates deep learning with medical physics to solve reconstruction challenges in ultrasound imaging. Her primary research interests include: Medical Ultrasound Imaging with neural field applications Physics-consistent 3D reconstruction from sparse views Geometry-aware deep learning for medical imaging Real-time surgical data science applications Confidence mapping in neural implicit representations Her recent publication trends show concentrated work on neural radiance fields for ultrasound (Ultra-NeRF), with expanding applications to tissue property estimation (UltRAP-Net) and surgical activity understanding (EgoExOR). Key methodological threads include physics-based constraints in deep learning and confidence quantification for clinical deployment. She actively supervises multiple Master's students on projects including: 4D Ultrasound SLAM (Sebastian Herz) Cardiac Shape Reconstruction with Motion Correction (Abdulkader Ghandoura) Confidence Maps research leading to MIUA 2024 publication (Bugra Yesilkaynak) Her GitHub repositories (ultra-nerf, ultron) demonstrate strong software engineering contributions to the medical imaging community. She contributes to the NARVIS and RobUSt research groups focusing on surgical vision systems and robotics.

Nadiia Dereviankoمشاهده پروفایل

پژوهشگر

• Physics-enhanced Machine Learning
• Applied Mathematics
• Signal Processing
• +۵ مورد دیگر

Nadiia Derevianko is a researcher at the Technical University of Munich (TUM) within the Department of Computer Science under the TUM School of Computation, Information and Technology (CIT). Her work bridges physics-enhanced machine learning, signal processing, and applied mathematics. Research Focus: Exponential sums and rational approximation Sparse signal recovery Fourier analysis Matrix pencil methods High-precision algorithms for frequency estimation Recent Publication Trends emphasize robust methods for reconstructing exponential structures using rational approximations (AAA/ESPIRA), with applications to partial differential equations and signal processing. Her work includes sensitivity analysis of rational function poles and differential approximation techniques for Gaussian functions. Teaching Activities include supervising master's theses on topics like adaptive activation neural networks and protein-ligand docking algorithms. She also teaches "Algorithms for Scientific Computing" in the summer semester.

• Machine Learning
• Cloud Computing
• Fault Diagnosis
• +۳ مورد دیگر

Chao Yin is a researcher at Shanghai University , Department of Computer Engineering and Science. His work spans multiple domains including Machine Learning, Cloud Computing, Fault Diagnosis, and Supply Chain Optimization. Key research areas: Machine Learning , Cloud Computing , Quantum Computing , Supply Chain Systems , Network Security Scientific Contributions (2024-2025): Developed heterogeneous graph neural networks for automotive supply chain analysis Created MSDF-VAE cloud-edge fault diagnosis framework using transfer learning Proposed quantum metrology methods with Heisenberg-limited precision Designed LARP pseudonym protocol for V2X communication Optimized fog computing resource scheduling with hybrid metaheuristics Prior Work (2012-2023): Contributed to fluid animation feature preservation from single images Developed label distribution learning for facial age estimation Designed erasure coding storage systems for big data Created multi-agent manufacturing networks in cloud environments

Oleh Melnykمشاهده پروفایل

استاد مهمان

• Mathematical Imaging
• Phase Retrieval
• Numerical Analysis
• +۴ مورد دیگر

Oleh Melnyk is a Substitute Professor at the Bavarian AI Chair for Mathematical Foundation of Artificial Intelligence at Ludwig-Maximilians-Universität München (LMU Munich), currently on leave from TU Berlin. His academic journey includes a Ph.D. in Mathematics (2023) from Technical University of Munich/Helmholtz Center Munich, an M.Sc. in Mathematics in Data Science (2018) from TU Munich, and a B.Sc. in Statistics (2016) from Taras Shevchenko National University of Kyiv. His research focuses on Mathematical Imaging , Phase Retrieval , Numerical Analysis , Optimization , and Compressed Sensing , with applications spanning ptychography, optical flow, sparse regression, and inverse problems. Recent work emphasizes algorithm convergence, noise-robust recovery, and high-dimensional data decomposition. Melnyk's publications demonstrate a strong focus on inverse problems and computational mathematics , with recurring themes in ptychographic imaging, phase retrieval algorithms, and sparse modeling. His articles frequently address theoretical convergence guarantees and practical applications in medical imaging and electron microscopy. He collaborates with research groups at LMU Munich, TU Berlin, and Helmholtz Munich, focusing on mathematical foundations of imaging and AI. No students, awards, or grants are mentioned in the source materials.

• Mathematical Data Science
• Machine Learning Theory
• Compressed Sensing
• +۵ مورد دیگر

Johannes Maly is an Assistant Professor at Ludwig Maximilian University of Munich (LMU) since October 2022, holding the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence. Previously, he held PostDoc positions at Catholic University of Eichstaett-Ingolstadt (2020-2022) and RWTH Aachen University (2019-2020). His educational background includes: PhD in Mathematics from Technical University of Munich (TUM), 2019 (supervised by Prof. Massimo Fornasier) M.Sc. in Mathematics from TUM, 2015 B.Sc. in Mathematics from TUM, 2013 Prof. Maly's research centers on mathematical data science with core focus areas in covariance estimation , neural network approximation properties , implicit bias of gradient descent , and multi-structured signal recovery . A unifying theme across his work is the theoretical investigation of coarse quantization effects , building on his foundational contributions to compressed sensing and extending into modern AI architectures. Analysis of his recent publications reveals consistent exploration of quantization constraints in high-dimensional statistics and neural network training. Key trends include development of tuning-free covariance estimators using dithering techniques, characterization of implicit regularization in overparameterized models, and novel quantization approaches for neural networks that balance hardware efficiency with theoretical guarantees. His scientific recognition includes: relAI Fellow (Zuse School for Reliable AI) MCML Associate (Munich Center for Machine Learning) Prof. Maly's research is supported through his Bavarian AI Chair appointment and fellowships. He teaches advanced courses including Convex Optimization, High-dimensional Probability, and Mathematical Introduction to Data Science at LMU Munich, with prior teaching roles at Catholic University of Eichstaett-Ingolstadt and RWTH Aachen University. While specific student names are not listed in the source material, his position involves supervision of graduate researchers. He leads the Mathematical Data Science and Artificial Intelligence research group at LMU Munich under the Bavarian AI Chair, collaborating with MCML and relAI networks. The group investigates theoretical challenges in quantization, low-rank matrix recovery, and optimization dynamics for next-generation AI systems.

Marion Menzelمشاهده پروفایل

استاد

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Quantitative MRI
• +۴ مورد دیگر

Marion Menzel is a Professor and Vice Dean at the Faculty of Electrical Engineering and Information Technology (Technische Hochschule Ingolstadt). She specializes in Biomechatronics and Sensor Data Analysis, with a focus on Medical Imaging and Magnetic Resonance Imaging (MRI). Her research emphasizes quantitative MRI techniques, including MR fingerprinting, deep learning applications in medical imaging, and hyperpolarized MRI for metabolic analysis. Education highlights include a Ph.D. (1999–2002) and Diplom-Chemiker (1994–1999) from RWTH Aachen. She has held roles at GE Healthcare, Siemens AG, and Forschungszentrum Jülich before joining THI in 2021. Her work bridges engineering and clinical practice, addressing challenges in imaging reconstruction, parameter mapping, and AI-driven diagnostics. Research interests span MRI acceleration, uncertainty quantification, and multimodal data fusion. She has pioneered methods like StoDIP and MRI2Qmap, advancing 3D imaging and compressive sensing. Her contributions to hyperpolarized 13C MRI enable non-invasive metabolic profiling in oncology and diabetes research. Key awards include recognition as an Alumna of the German Academic Scholarship Foundation. Her lab collaborates on projects like CHAIMELEON, a Pan-European initiative for AI-driven cancer management tools. Recent grants focus on improving diagnostic accuracy through AI and spatiotemporal reconstruction algorithms. Menzel’s team develops open-source frameworks for MRI analysis and actively participates in standardizing medical imaging protocols. Her work has direct clinical impact in neuro-oncology, radiology, and precision medicine.

Hanna Veselovskaمشاهده پروفایل

پژوهشگر

• Phase Retrieval
• Super-Resolution
• Sigma-Delta Modulation
• +۴ مورد دیگر

Hanna Veselovska is a researcher in the Data Science Group at the Technical University of Munich (TUM), part of the Faculty of Mathematics and the TUM School of Computation, Information and Technology. Her research focuses on Quantization Theory, Mathematical Perspectives of Machine Learning, Image Processing, and Graph Signal Processing. She holds a PhD from the Institute of Mathematics of the National Academy of Sciences of Ukraine (NASU) under Anatoly Holub and has held postdoctoral positions at TU Braunschweig (Germany) and the University of Lübeck (Germany as a DAAD fellow). Current Position: Researcher at TUM Data Science Group PhD: Institute of Mathematics of NASU (2017) Postdoc: TU Braunschweig (2018–2020), University of Lübeck (DAAD fellowship) Her research interests span Phase Retrieval, Super-Resolution, and Sigma-Delta Modulation, with contributions to numerical analysis and signal processing. She has published extensively on topics such as regularized sampling formulas, graph signal quantization, and non-intrusive surrogate modeling. Her work bridges theoretical mathematics with applied data science, with applications in image processing and crashworthiness analysis. Teaching includes courses on Modelling and Simulation with Differential Equations, Random Matrix Theory, and Foundations in Data Analysis at TUM, as well as engineering mathematics courses at TU Braunschweig. Key contributions include advancements in digital halftoning via sigma-delta modulation and recovery of atomic measures on spheres. Her research emphasizes interdisciplinary approaches to solving complex signal processing challenges.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Jian Li is a Professor at Tsinghua University's Institute for Interdisciplinary Information Sciences in Beijing, China. With an extensive publication record spanning multiple disciplines including Artificial Intelligence, Machine Learning, Computer Vision, and Medical Imaging, Dr. Li leads a highly productive research program that bridges theoretical computer science with practical applications across various domains. Dr. Li's research interests focus on developing novel AI methodologies with applications in healthcare, engineering, and scientific computing. Their work demonstrates particular expertise in neural network architectures, image processing techniques, and interdisciplinary approaches that combine domain knowledge with machine learning. The research spans both theoretical foundations and practical implementations, with numerous publications in top-tier journals and conferences. Analysis of recent publications reveals a strong trend toward interdisciplinary research, particularly in medical AI applications, scientific computing, and robotics. Dr. Li's work frequently involves developing novel neural network architectures and optimization techniques that address specific challenges in target domains. The publications show consistent collaboration with researchers across China and internationally, indicating an active research network. Dr. Li has contributed significantly to advancements in knowledge distillation, medical image analysis, fluid dynamics modeling, and reinforcement learning applications. The research demonstrates both theoretical rigor and practical relevance across multiple application domains.

Elena Sterkمشاهده پروفایل

پژوهشگر

• Compressed Sensing
• Iterative Algorithms
• Radar Signal Processing
• +۲ مورد دیگر

Elena Sterk is a Research Assistant at the Institute of Communications Engineering, Ulm University, specializing in iterative algorithms for compressed sensing and their applications to radar systems. Her work bridges theoretical advancements with practical implementations in signal processing. Rising from her research in compressed sensing and radar data, Sterk has contributed to key developments in signal recovery algorithms, particularly in damping approaches for AMP and distributed sensing frameworks. Her recent publications highlight interdisciplinary applications in microwave technology and climate change detection. In addition to her research, she provides teaching assistance in Advanced Channel Coding and mentors students on thesis topics related to compressed sensing algorithm optimization. Contact details: elena.sterk@uni-ulm.de , office space 43.2.229, phone +49(0)731/50-31536.