جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Signal Processing
• Computer Vision
• +۴ مورد دیگر

Yuan Liao is an active researcher with a focus on interdisciplinary areas spanning computer science, electrical engineering, and applied mathematics. His work emphasizes innovative solutions in signal processing, machine learning, robotics, and human mobility studies. Notably, he has contributed to advancements in wearable biomedical devices, remote sensing image analysis, and next-generation wireless communication systems like 6G. Collaborations with institutions like Academia Sinica and co-authors such as Vasilis Friderikos highlight his global academic network. Liao's research often bridges theoretical frameworks with practical applications, addressing challenges in healthcare, transportation, and urban planning. Key contributions include developing novel neural network architectures for tasks like image inpainting and emotion recognition from EEG signals. His work on robotic aerial base stations for mmWave backhauling demonstrates expertise in cutting-edge wireless systems. While specific institutional affiliations are not explicitly stated in the provided data, his prolific publication record across top-tier journals and conferences indicates an established academic role, likely as a researcher or faculty member in a technical discipline.

• Computer Science
• Artificial Intelligence
• Remote Sensing
• +۳ مورد دیگر

Jie Yang is a prominent academic affiliated with Carnegie Mellon University in the Department of Machine Learning under the School of Computer Science . With a research focus on Computer Science Artificial Intelligence Remote Sensing Machine Learning Data Analysis Pattern Recognition , Yang's work bridges theoretical advancements with practical applications across diverse fields. Recent publications highlight trends in object detection , trajectory prediction , 5G network security , and agricultural automation . These contributions leverage cutting-edge techniques such as ConvGRU networks , YOLO-seg , and transfer learning to address complex challenges. Despite extensive research output, no formal scientific awards or student advisement details were identified in the provided materials. Yang's work remains interdisciplinary, impacting both engineering and healthcare domains through innovative methodologies.

• Wireless Communication
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Yoan Shin is a researcher active in wireless communication, machine learning, and remote sensing. His work spans indoor localization, SAR ship detection, UAV object detection, and IoT resource allocation. Key research areas: Wireless Sensor Networks, Deep Learning, SAR Imagery, UAV Systems, IoT Recent publications focus on hybrid Wi-Fi/PDR indoor tracking (2023), enhanced RT-DETR architectures for UAV imagery (2024), and SAR ship detection optimization (2024). Trends show expertise in sensor fusion, attention mechanisms, and domain-specific neural networks. Collaborates extensively with researchers like Chushi Yu, Thu L. N. Nguyen, and Oh-Soon Shin. No explicit awards or teaching roles listed.

Eva Dorschkyمشاهده پروفایل

پژوهشگر

• Physics-Informed Machine Learning
• Optimal Control
• Artificial Intelligence
• +۴ مورد دیگر

Eva Dorschky is a PostDoc and Group Leader at the Machine Learning and Data Analytics Lab of Friedrich-Alexander-University Erlangen-Nuremberg (FAU). She leads the Sports Analytics group and contributes to the BioMAC group and EmpkinS Collaborative Research Center (CRC). Her research focuses on physics-informed machine learning, optimal control, and AI applications for climate change mitigation. She teaches the Green AI seminar, exploring sustainable algorithms and climate solutions through AI. Education: Dr.-Ing. in Computer Science (FAU, 2022), M.Sc. and B.Sc. in Electrical Engineering (FAU, 2012–2017). Previous roles include Research Associate at FAU's Pattern Recognition Lab (2014–2017) and internships at Industrial Research Limited (New Zealand). Research interests span biomechanical simulations, sensor-based gait analysis, and musculoskeletal modeling. Recent projects include developing AI-driven solutions for sports performance optimization and sustainable energy systems. Her work bridges machine learning, biomedical engineering, and environmental science. Publications highlight advancements in inertial sensor setups, radar-based pose estimation, and gamification in sports. Collaborations include EmpkinS CRC and the BioMAC group, emphasizing interdisciplinary approaches to human movement analysis and technology-driven solutions.

Ulrich Bauerمشاهده پروفایل

استاد

• Applied Topology
• Computational Topology
• Topological Data Analysis
• +۳ مورد دیگر

Prof. Dr. Ulrich Bauer is a Professor at the Technische Universität München (TUM) , leading the Applied and Computational Topology research group within the TUM School of Computation, Information and Technology . His academic career includes positions at Freie Universität Berlin, Georg-August-Universität Göttingen (where he earned a doctoral degree in Mathematics), and the Institute of Science and Technology Austria. Bauer specializes in applied and computational topology, focusing on multi-scale data connectivity and developing computational methods for large datasets. He is a key member of the Collaborative Research Center Discretization in Geometry and Dynamics and the Centre for Topological Data Analysis . Research Interests: Bauer’s work bridges theoretical foundations and practical applications in topology. He explores methods like persistent homology and discrete Morse theory to uncover global data structures. His contributions include advancing algorithms for topological data analysis, with applications in medical imaging, computational biology, and geometric modeling. Bauer’s software tool Ripser is widely recognized for efficient computation of persistence barcodes. Awards: ATMCS Best New Software Award (2016) Best Paper Award TopoInVis (2013) Apple Design Award (2003) O’Reilly Mac OS X Innovators Award (2003) Grants & Leadership: Bauer’s leadership roles include the executive board of the CRC Discretization in Geometry and Dynamics. His research has been supported by grants focusing on topological methods in data science and geometry. He actively contributes to advancing interdisciplinary collaborations between mathematics, computer science, and applied fields. Labs & Teams: As founder of the Applied and Computational Topology group at TUM, Bauer fosters innovation in computational topology, mentoring researchers and students in developing cutting-edge methodologies. His work intersects with the TUM School’s broader mission in computational and data-driven science.

• Signal Processing
• Machine Learning
• Wireless Communications
• +۹ مورد دیگر

Michael Joham is a Senior Researcher at the Chair of Signal Processing Methods at the Technical University of Munich (TUM). He teaches courses in Circuit Theory, Systems Theory, MIMO Systems, Numerical Linear Algebra for Signal Processing, and Introduction to Signal Processing. Circuit Theory (Winter Term) Systems Theory (Summer Term) Signal Processing Systems (Winter Term) MIMO Systems (Winter Term) Numerical Linear Algebra for Signal Processing (Summer Term) Introduction to Signal Processing (Summer Term) His research spans Signal Processing for Communications, Machine Learning in Physical Layer Wireless Communications, Intelligent Reflecting Surface Systems, Multi-User MIMO Communications, Automotive Safety and Autonomous Driving, Quantum Radar, Generative Modeling for Biomedical Applications, and methodologies like Applied Information Theory, Array Processing, and Sparse Signal Processing. Contact: joham@tum.de , Phone: +49-89-289-28510, Fax: +49-89-289-28522, Postal Address: 80290 Munich, Germany, Visitor Address: Theresienstrasse 90, Room N1125A.

Denis Belomestnyمشاهده پروفایل

استاد

• Statistics of Stochastic Processes
• Financial Mathematics
• Econometrics
• +۵ مورد دیگر

Denis Belomestny is a Professor of Applied Stochastics at the Department of Mathematics, University of Duisburg-Essen. His academic journey includes a PhD from Lomonosov Moscow State University (2002), postdoctoral work at the University of Bonn, and research positions at WIAS Berlin and Humboldt University Berlin. He currently leads research at the intersection of stochastic processes and financial mathematics. PhD in Mathematics, Lomonosov Moscow State University (2002) W3 Professorship in Applied Stochastics, University of Duisburg-Essen (2011–present) His research focuses on statistics of stochastic processes , optimal stopping/control , and Monte Carlo methods , with applications in financial mathematics and machine learning. Key collaborations include work with John Schoenmakers on multilevel approximation algorithms and Alexey Naumov on variance reduction techniques. Recent publications (2025–2023) explore deep neural networks for SDEs , generative adversarial networks , and nonparametric estimation in complex stochastic models. His work spans stochastic differential equations , financial derivatives pricing , and machine learning-driven statistical inference . He supervises doctoral students, including Sascha Nolte (research: robust optimal stopping without reference models). Current projects involve McKean-Vlasov SDEs , gamma-driven processes , and reinforced optimal control .

• Computer Science
• Electrical Engineering
• Machine Learning
• +۵ مورد دیگر

Haobo Wang is a researcher affiliated with Zhejiang University , specifically within the School of Software Technology under the College of Computer Science and Technology . His work bridges Computer Science and Electrical Engineering , focusing on Machine Learning , Signal Processing , and Remote Sensing .

• Machine Learning
• Compressed Sensing
• Privacy-Preserving Techniques
• +۴ مورد دیگر

Jia Liang is a researcher at Henan Polytechnic University's School of Electrical Engineering and Automation, with a focus on Machine Learning , Compressed Sensing , and Privacy-Preserving Techniques . His work bridges Computer Science and Signal Processing , particularly in Radar Imaging and Medical Image Analysis . Key Collaborations: Di Xiao, Ying Luo, Qun Zhang, Hui Huang Technical Expertise: Federated Learning, SAR Imaging, Compressive Sensing, Adversarial Learning His research emphasizes secure data processing in IoT and cloud environments, with recent innovations in cross-disciplinary applications like biosignal analysis for cysticercosis diagnosis . Publications span top venues including IEEE Transactions on Aerospace Systems and Remote Sensing . Notable trends include privacy-preserving machine learning for federated systems and 3D radar imaging of rotating targets, alongside medical imaging solutions for chest radiographs and optical coherence tomography .

• Machine Learning
• Biomedical Engineering
• Signal Processing
• +۳ مورد دیگر

Yanbo Zhang is a researcher affiliated with Nanyang Technological University , holding a PhD in Wireless Communication and Sensing (2022). His work spans machine learning , biomedical engineering , and signal processing with applications in healthcare, communication systems, and computer vision. Education: PhD from Nanyang Technological University, Singapore (2022) Research areas include: Machine learning for medical diagnosis (predicting infections, cardiovascular events, and cancer) Advanced analog-digital converter designs for precision electronics Diffusion models and evolutionary algorithms in AI Federated learning for imbalanced data and cybersecurity Recent publications focus on multi-label learning for disease prediction, secure communication architectures , and deep learning applications in medical imaging. Collaborations include institutions in Singapore, China, and international teams.

Wolfgang Utschickمشاهده پروفایل

استاد

• Signal Processing
• Wireless Communications
• Radar Technology
• +۳ مورد دیگر

Prof. Wolfgang Utschick is a full Professor at the Technische Universität München (TUM), holding the professorship for Methods of Signal Processing since 2002. He is affiliated with the TUM School of Computation, Information and Technology and the Department of Electrical Engineering and Information Technology (EI). Since 2017, he has served as Dean of the EI Department, overseeing academic and research activities. His research integrates applied mathematics into signal processing applications across wireless communications, radar technology, vehicle safety, and machine learning. Prof. Utschick earned his doctorate after industry experience and completed postdoctoral studies at TUM and ETH Zurich. He has led numerous industrial and DFG-funded projects, resulting in patents in signal processing and over 150 peer-reviewed publications. Notable contributions include advancements in channel estimation, MIMO systems, and quantum radar technology. His research interests emphasize the intersection of mathematical theory and practical engineering, with a focus on optimizing communication systems and safety-critical applications. Awards include IEEE Fellow (2021), TUM Sabbatical for Teaching Excellence (2014), and multiple IEEE publication awards. Prof. Utschick’s work bridges academia and industry, addressing challenges in 5G/6G networks, radar innovation, and machine learning-driven signal processing. He remains active in curriculum development and departmental leadership at TUM.

Armeen Taebمشاهده پروفایل

استادیار

• Selective Inference
• Multiple Testing
• Graphical Models
• +۷ مورد دیگر

Armeen Taeb is an Assistant Professor in the Department of Statistics at the University of Washington . Previously, he was a postdoctoral fellow at ETH Zürich under the ETH Foundations of Data Science, mentored by Peter Bühlmann. He earned his PhD in Electrical Engineering at Caltech under Venkat Chandrasekaran's supervision. Research Interests : His work bridges optimization and statistics , focusing on Graphical and latent-variable modeling Provably optimal causal model learning False positive error control in non-traditional settings Domain adaptation Applications in physical sciences Article Trends : His publications span causal inference (2022-2025), graphical models (2017-2025), convex optimization (2018-2025), and statistical robustness (2020). Recent work (2025) addresses extremal graphical modeling and selective inference challenges. Scientific Awards : ETH Zürich Foundations of Data Science Postdoctoral Fellowship (2019-2021) Caltech Resnick Institute Fellowship (2016-2018) W. P. Carey & Co. Prize for Applied Mathematics (2020) Caltech Graduate Fellowship (2013-2014) Grants : National Science Foundation DMS-2413074 (PI), University of Washington Royalty Research Fund (PI). Service : President of the Institute of Mathematical Statistics New Researcher Group; co-organized IMS New Researchers Conferences (2024-2025).

Olga Mulaمشاهده پروفایل

پژوهشگر

• Optimal Transport
• Wasserstein Spaces
• Model Reduction
• +۴ مورد دیگر

Olga Mula is a researcher at Eindhoven University of Technology (TU Eindhoven) in the Netherlands specializing in optimal transport theory, Wasserstein spaces, and model reduction techniques for partial differential equations. Her work bridges theoretical mathematics with practical applications in state estimation and inverse problems. Her research interests focus on Optimal Transport , Wasserstein Spaces , Model Reduction , and Numerical Analysis of PDEs . She develops algorithms for state estimation in metric spaces, particularly focusing on the Wasserstein space of probability measures. Her work includes developing reduced models using barycentric approximation, analyzing convergence properties, and addressing challenges in sensor placement for optimal data acquisition. Her recent publications demonstrate significant contributions to understanding how to build efficient reduced models in Wasserstein spaces for both forward and inverse problems. She has developed theoretical frameworks for state estimation algorithms, analyzed their performance in terms of Kolmogorov widths, and created practical implementations using sparse Wasserstein barycenters. Her work spans pure mathematical theory to applications in image processing and PDE-constrained optimization. Her scientific contributions include: Development of piecewise-affine algorithms for state estimation Nonlinear model reduction on metric spaces for conservative PDEs Sparse approximation using Wasserstein barycenters Applications to shape reconstruction and line completion Theoretical analysis of approximation rates in Wasserstein spaces She collaborates extensively with leading researchers in the field including Cohen, Dahmen, Feydy, and Rai. Her work demonstrates both theoretical depth and practical relevance, connecting abstract mathematical concepts to real-world problems in data assimilation and inverse modeling.

Lukas Luftمشاهده پروفایل

پژوهشگر

• Robot Localization and Mapping
• Causality and Foundations of Quantum Physics
• Probabilistic Methods
• +۳ مورد دیگر

Lukas Luft is a postdoctoral researcher at the Autonomous Intelligent Systems group within the Department of Computer Science at the University of Freiburg. His work spans robotics and quantum physics, focusing on probabilistic methods for robot localization, multi-robot systems, and causal inference. Post Doc (2020–present) PhD in Computer Science, University of Freiburg (2020) Master and Bachelor in Physics, RWTH Aachen and University of Freiburg His research in Robot Localization and Mapping includes advanced probabilistic techniques like Bayes filters, decentralized algorithms for multi-robot systems, and change detection in environments using full posterior distributions. He also explores Causality and Foundations of Quantum Physics , applying entropic inequalities and information theory to causal discovery and non-locality. The articles highlight his contributions to robotics, particularly in sensor modeling for Lidar, simultaneous localization and mapping (SLAM), and efficient probabilistic methods. In quantum physics, his work addresses causal structures and entropic information, bridging AI with foundational physics. Luft has collaborated with leading researchers, including Prof. Wolfram Burgard and Bernhard Schölkopf, and contributed to key conferences like Robotics: Science and Systems (RSS) and IEEE IROS.

Michael Mumaمشاهده پروفایل

استاد

• Robust Data Science
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.