جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Computer Graphics
• Machine Learning
• +۴ مورد دیگر

Prof. Matthias Nießner is a Professor at the Technical University of Munich , where he leads the Visual Computing Lab . Prior to this, he held a Visiting Assistant Professor position at Stanford University . His work bridges computer vision , graphics , and machine learning , focusing on 3D reconstruction , semantic scene understanding , and AI-driven video synthesis . Prof. Nießner has published over 150 works in top venues like SIGGRAPH , CVPR , and ECCV , with several receiving best paper awards (SIGCHI’14, HPG’15, SPG’18, SIGGRAPH’16 Emerging Tech). His research has garnered international media attention, including features in the New York Times , Wall Street Journal , and MIT Technological Review , as well as TV demonstrations (e.g., Jimmy Kimmel Live for Face2Face technology). Awards : TUM-IAS Rudolph Moessbauer Fellowship (2017–ongoing) Google Faculty Award (2017) Nvidia Professor Partnership Award (2018) ERC Starting Grant (2018, €1.5M) Eurographics Young Researcher Award (2019) Research Trends : 3D Gaussian Splatting for real-time rendering Neural Radiance Fields (NeRF) with mesh supervision Audio-driven facial animation via diffusion models Latent space diffusion for 3D scenes Self-supervised and zero-shot methods for 3D and image analysis As a co-founder and director of Synthesia Inc. , he drives democratization of synthetic media. His YouTube channel has over 5 million views, reflecting his impact beyond academia.

• Digital Signal Processing
• Speech Communication
• Acoustic Signal Processing
• +۳ مورد دیگر

Peter Vary is a Professor at the Faculty of Electrical Engineering and Information Technology of RWTH Aachen University, serving as Director of the Institute for Communication Systems. His work focuses on speech and audio signal processing for communication systems. Digital Signal Processing Speech Enhancement Acoustic Echo Control Microphone Array Beamforming Communication Systems Audio Compression His recent publications (2023–2024) emphasize speech coding, noise reduction, and bandwidth extension for hearing aids and mobile devices, with technical innovations in Kalman filters, hybrid digital-analog transmission, and wind noise detection. He holds a leadership role in the Institute for Communication Systems and serves as Ombudsperson for teaching in his faculty. Contact: vary@iks.rwth-aachen.de

Bo Bernhardssonمشاهده پروفایل

استاد

• Automatic Control
• Control Theory
• Modeling and Control of Uncertain Systems
• +۱۷ مورد دیگر

Bo Bernhardsson is a Professor in Automatic Control at the Department of Automatic Control, Faculty of Engineering (LTH), Lund University. He has been a full-time professor at Lund since 2010, following a decade (2001–2010) as an Expert in Mobile System Design and Optimization at Ericsson. He is affiliated with major research initiatives including ELLIIT (Excellence Center in Information Technology), LCCC (Lund Center for Control of Complex Engineering Systems), and WASP-AS (Wallenberg AI, Autonomous Systems and Software research school), where he has played a leadership role since 2016. His research focuses on modeling and control of uncertain and large-scale systems, with applications spanning industrial automation, mobile communications, particle accelerators, biomedical systems, and navigation technologies. He integrates theoretical control methods with practical implementations, particularly under constraints such as communication limitations, noise, and delays. His recent publications reveal a strong trend in networked control, communication-constrained estimation, and optimization-based control design. The works span theoretical advances in signal estimation under SNR constraints, event-based and stochastic control, and practical applications like IMU-radio fusion for navigation and RF field control in particle accelerators. Keywords include Control Theory, Communication Systems, Optimization, Signal Processing, and Networked Control , with subfields such as encoder-decoder co-design, virtual antenna arrays, and dynamic programming for time-delay systems. PhD in Control, Lund University, 1992 Professor in Automatic Control, Lund University, since 1999 Expert, Mobile Systems, Ericsson, 2001–2010 Bo Bernhardsson has supervised over 20 PhD and licentiate students, including Jacob Bergstedt (immune system modeling), Anders Mannesson (navigation and radio), and Erik Johannesson (control under communication constraints). His research has been funded by major entities such as the European Spallation Source and the Wallenberg Foundation. He teaches advanced courses in Linear Systems, Convex Optimization, and Robust Control, and has contributed significantly to both academic and industrial advancements in control engineering. He leads and collaborates on interdisciplinary projects involving real-time control, autonomous systems, and machine learning, often in partnership with industry and international research centers. His work in the RobotLab at LTH and on cloud-based control systems highlights his engagement with emerging technologies.

Willi Menapaceمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• 3D Reconstruction
• +۳ مورد دیگر

Willi Menapace is a Researcher at the University of Trento , affiliated with the Multimedia and Human Understanding Group (MHUG). His work focuses on the intersection of Computer Vision , Deep Learning , and 3D Reconstruction , with applications in Medical Imaging and Interactive Video Generation .

• Wireless Communications
• Signal Processing
• MIMO Systems
• +۴ مورد دیگر

Cédric Le Ruyet is a prominent researcher at the Conservatoire National des Arts et Métiers (CNAM) , affiliated with the CEDRIC Laboratory. His work spans Wireless Communications , Signal Processing , and 5G/6G Network Design , with a focus on Filter Bank Multicarrier (FBMC) systems, MIMO , and Cognitive Radio . He collaborates extensively with institutions like CEA-LETI and CNAM . His research interests include Channel Modeling , Resource Allocation , Beamforming , and Machine Learning in wireless contexts. He has contributed to Reconfigurable Intelligent Surfaces (RIS) for beam optimization and SCMA/OFDM codebook design. Recent publications highlight his work on QAM-FBMC systems, Rayleigh Fading Models , and 5G Network Coexistence . His articles often explore Distributed MIMO , Low-Complexity Equalizers , and Indoor Positioning via Channel State Information (CSI) . He has authored numerous IEEE Transactions and conference papers, with technical reports for projects like ANR POSEIDON and NF-PERSEUS . His methodologies integrate Genetic Algorithms , Lattice-Reduction , and Deep Learning for next-gen wireless systems.

Frank R. Kschischangمشاهده پروفایل

استاد

• Digital Communications
• Coding Theory
• Information Theory
• +۴ مورد دیگر

Frank R. Kschischang is a Distinguished Professor of Digital Communication in the Department of Electrical and Computer Engineering at the University of Toronto. He holds the Canada Research Chair in Communications Algorithms and has been a faculty member since 1991. His research focuses on coding theory, digital communications, and information theory, with applications in optical, wireless, and fiber-optic systems. He is a Hans Fischer Senior Fellow at the Institute for Advanced Study (TUM-IAS), Technische Universität München, collaborating on advancing fiber-optic communication capabilities. Education: B.A.Sc. (University of British Columbia, 1985), M.A.Sc. and Ph.D. (University of Toronto, 1988 and 1991). He has held visiting roles at MIT (1997–1998) and ETH Zurich (2005). Academic roles include IEEE Information Theory Society President (2010) and editorial roles for the IEEE Transactions on Information Theory. Research emphasizes error-correcting codes, fiber-optic channel modeling, and nonlinear Fourier transform-based communication. Notable contributions include staircase codes for 100 Gb/s optical networks and rank-metric codes for network coding. His work has led to practical advancements in high-speed optical systems and improved channel capacity understanding. Awards include IEEE Fellow (2006), Killam Research Fellowship (2010), and multiple teaching and service awards. He advises numerous graduate students and collaborates internationally on projects like spectral modulation in fiber optics and machine learning for signal processing. Labs and teams: Leads the Coding and Information Theory group at UofT, focusing on cutting-edge research in communications algorithms and hardware implementation. Collaborates with industry partners like the Communications Research Centre Canada and Inphi Corp.

Maximilian Starkمشاهده پروفایل

مدرس

• Communication Systems
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Dr. Maximilian Stark is a Lecturer at the Institute of Communications, TU Hamburg. His research focuses on machine learning-driven advancements in communication systems, particularly applying the information bottleneck method to decoding algorithms, signal processing, and quantization techniques. His work bridges theoretical information theory with practical implementation challenges in coding and channel design. Key research areas include LDPC and polar codes optimization, low-bitwidth decoding architectures, and distributed signal processing frameworks. Recent publications emphasize adaptive learning systems for error resilience under quantization constraints and hardware-efficient receiver design. Dr. Stark's academic contributions span over 20 peer-reviewed articles since 2016, with a strong emphasis on integrating machine learning principles into traditional communication engineering problems. Notable themes include neural decoding paradigms, resource-constrained quantization strategies, and distributed information compression methods. No scientific awards explicitly listed. Academic advising details and lab affiliations are not provided in source materials.

• Digital Communications
• Error-Correcting Coding
• Iterative Decoding
• +۵ مورد دیگر

Catherine Douillard is a Professor in the Department of Mathematical and Electrical Engineering at IMT Atlantique. She holds an engineering degree from École Nationale Supérieure des Télécommunications de Bretagne (1988), a doctorate in electronics from the University of Western Brittany (1992), and accreditation to supervise research from the University of Southern Brittany (2004). Her research focuses on digital communications, error-correcting coding, iterative decoding, and coded modulations for multi-antenna and NOMA systems. She led the 'Coding, Constellations and Interleaving' group in DVB-NGH standardization (2010–2012) and received the IEEE/SEE Glavieux Award (2009) for industrial impact in coding. She chairs major coding symposiums like ISTC and leads projects such as NF-PERSEUS (5G networks) and AI4CODE (AI-driven FEC design). Her work emphasizes resource allocation in IoT, NOMA, and full-duplex systems. Key contributions include optimizing non-binary turbo codes for IoT, applying machine learning to interleaver design, and exploring full-duplex relay architectures. She supervises interdisciplinary teams across IMT Atlantique, Lebanese University, and international partners. Current projects address 6G challenges like low-latency decoding and coexistence of eMBB/URLLC services. Research awards include the Glavieux Prize and leadership in European initiatives (EPIC, FANTASTIC-5G). She contributes to standardization bodies (DVB) and chairs conferences, bridging academia and industry through projects like Beyond5G (sovereign 5G networks) and QCSP (NB-code for IoT).

• Communications Engineering
• Information Theory
• Coding Theory
• +۳ مورد دیگر

Gianluigi Liva is a Lecturer at the Chair of Communications Engineering within the Technical University of Munich (TUM). His affiliation is under the TUM School of Computation, Information and Technology. He contributes to teaching and research in communications engineering, with a focus on iterative decoding and coding theory. His contact information includes the postal address at Theresienstr. 90, 80333 München, and the email gianluigi.liva@tum.de . Office hours are 09:00–17:30. Teaching responsibilities include the course 'Channel Codes for Iterative Decoding'. His research interests align with the department's focus on advanced communication systems and signal processing.

• Large Language Models
• Multilingual Machine Translation
• Vision-Language Models
• +۳ مورد دیگر

Xiaocheng Feng is a prominent researcher in computational linguistics and large language models (LLMs), with extensive contributions to multilingual systems, vision-language integration, and knowledge alignment. His work spans from 2016 to 2025, focusing on advanced topics like Mitigating hallucinations in multimodal models Efficient knowledge editing across multiple models Length-controlled text generation for black-box LLMs Improving contextual faithfulness via retrieval heads Cross-lingual connection mechanisms for fine-tuning His recent publications address critical challenges in LLMs, including knowledge misalignment, entity-level unlearning, and Pareto optimization in multilingual translation. He has explored probability density estimation for text control, adaptive context modeling in visual storytelling, and cross-lingual annotation projection for low-resource languages. Collaborations with researchers like Bing Qin, Lei Huang, and Weitao Ma highlight his central role in advancing NLP methodologies.

Fangning Huمشاهده پروفایل

مدرس ارشد

• Belief Propagation Applications
• Channel Coding
• Intelligent System Design
• +۷ مورد دیگر

Fangning Hu is a Senior Lecturer in Electrical Engineering at the School of Computer Science & Engineering at Constructor University Bremen gGmbH, Germany. She holds a Ph.D. in Electrical Engineering from Jacobs University Bremen (now Constructor University) and has held academic positions at multiple institutions, including Jacobs University, Hangzhou Dianzi University, and roles as a Guest Lecturer at Hochschule Bremen and German University in Cairo. Education: Ph.D. in Electrical Engineering (2004–2008), Jacobs University Bremen M.Sc. in Computer Science (2000–2002), DAAD Scholarship recipient at Duisburg-Essen University M.Sc. in Electrical Engineering (1999–2000), Zhejiang University (top 5% entry-exam exemption) B.Sc. in Electrical Engineering (1995–1999), Zhejiang University (full merit scholarships) Research Interests: Belief Propagation Applications in Channel Coding and Sudoku Solvers Intelligent System Design, including Hardware Development, Sensor Data Acquisition, and Object Detection LDPC Codes, OFDM Systems, and Impulse Noise Correction Key Contributions: Her work spans over 15 publications on topics like LDPC codes, analog signal processing, and iterative decoding algorithms. Notable projects include leadership on the National Natural Science Foundation of China (NNSFC) project on Belief Propagation in Channel Coding (2012–2014) and DFG-funded research on GEO-IDEC (2008–2010). Awards: 2020 Teacher of the Year, Jacobs University Bremen IEEE Fellowship (2005–2008) DAAD Scholarships (Master's programs in Germany) Labs and Teaching: Leads labs in Digital Design, Embedded Systems, Robotics, and Sensor Networks. Active in curricula for Advanced Digital Design and Digital Signal Processing courses. Her research teams collaborate on projects involving robotics, intelligent systems, and sustainable data acquisition technologies.

Vera Kempeمشاهده پروفایل

استاد

• Language Development
• Second Language Acquisition
• Cognitive Psychology
• +۷ مورد دیگر

Vera Kempe is a Professor of Psychology and Language Learning in the Department of Sociological and Psychological Sciences at Abertay University . She is a leading researcher in language acquisition, cognitive development, and bilingualism, with a strong publication record in top-tier journals such as Cognition , Journal of Experimental Psychology , and Frontiers in Psychology . Her research interests focus on language development , child-directed speech , second language acquisition , dialect processing , and the cognitive mechanisms underlying language learning. She investigates how linguistic input, cognitive abilities, and social factors shape language acquisition across the lifespan. Her work often employs experimental and computational methods to understand the emergence of structure in language. Recent publications highlight trends in iterated language learning , artificial language paradigms , literacy acquisition , and the role of teaching and social interaction in language evolution. Her research bridges developmental psychology, cognitive science, and linguistics, contributing to both theoretical and applied understanding of language. Vera Kempe has co-edited the Encyclopedia of Language Development and contributed to major handbooks. She has supervised numerous research projects and collaborated widely with scholars across Europe and North America. Editorial Roles: Guest editor for special issues on emergentist approaches and self-domestication in language evolution. Research Leadership: Pioneered studies on dialect exposure and literacy, iterated teaching, and the cognitive effects of music training on language learning. Open Science Advocacy: Contributed to the CHILDES project and promotes preprint peer review in undergraduate education. Key Labs/Teams: Collaborates with researchers at Abertay University and international institutions on projects involving artificial language learning, dialect variation, and child language development.

• Error-Correction Coding
• FPGA/ASIC Design
• Multicore Architectures
• +۳ مورد دیگر

Christophe Jégo is a researcher specializing in hardware/software co-design for communication systems, with affiliations suggesting connections to Université de Bordeaux's College of Engineering. His core expertise spans error-correction coding (LDPC, polar, turbo codes), FPGA/ASIC implementations, and high-performance computing architectures for telecommunications. Research interests focus on: Hardware acceleration of communication algorithms (LDPC/polar decoders, FFT processors) Multicore/GPU optimization for real-time signal processing RTL design automation tools and high-level synthesis frameworks 5G physical layer implementations including MIMO detection Resource-constrained embedded systems for IoT and space applications Publication analysis reveals strong emphasis on: FPGA implementations of communication blocks (67% of recent works) Parallel processing techniques for LDPC/polar decoding (2014-2025) Open-source tool development for hardware design (AFF3CT, Odatix) Cross-layer optimization from algorithms to silicon implementation

Antonio Orvietoمشاهده پروفایل

استاد

• Deep Learning
• Optimization Theory
• Sequence Modeling
• +۷ مورد دیگر

Antonio Orvieto is a Professor and Principal Researcher at the Max Planck Institute for Intelligent Systems and ELLIS Institute Tübingen, where he leads the Deep Models and Optimization research group. He is also a lecturer at the University of Tübingen and faculty for the CLS, ELLIS, and IMPRS-IS PhD Programs. His research focuses on improving the efficiency of deep learning technologies through theoretical understanding of optimization dynamics and innovative neural network architectures. Dr. Orvieto earned his PhD from ETH Zürich under the supervision of Prof. Dr. Thomas Hofmann and Dr. Aurelien Lucchi. Prior to his PhD, he obtained his master's degree in Robotics, Systems, and Control from ETH. His educational background also includes undergraduate studies at Universita' degli studi di Padova in Italy. During his academic journey, he gained research experience at DeepMind (UK), Meta (US), MILA (CA), INRIA (FR), and HILTI (LI). Dr. Orvieto's research spans two main areas: understanding the intricacies of large-scale optimization dynamics and designing innovative architectures and powerful optimizers capable of handling complex data. His work particularly focuses on decoding patterns in sequential data, with applications in biology, neuroscience, natural language processing, and music generation. His theoretical approach to deep learning has led to significant contributions in understanding recurrent neural networks, transformers, and optimization methods. His recent publications reveal a strong focus on sequence modeling, optimization theory, and the theoretical foundations of deep learning, with particular emphasis on improving training efficiency and model performance. Schmidt Sciences AI2050 Early Career Fellow Dr. Orvieto actively mentors PhD students and leads a vibrant research group at the Max Planck Institute for Intelligent Systems. His Deep Models and Optimization group includes PhD students Destiny Okpekpe, Felix Sarnthein, Diganta Misra, Sajad Movahedi, and Wenjie Fan, among others. He is deeply involved in doctoral education as faculty for multiple PhD programs including CLS, ELLIS, and IMPRS-IS. His teaching includes the course "Nonconvex Optimization for Deep Learning" at the University of Tübingen. The Deep Models and Optimization research group investigates the interplay between optimizers and architectures in deep learning, with a focus on developing new networks for long-range reasoning. The group's mission is to design new optimizers and neural networks to accelerate technology and scientific discovery, with a strong theoretical foundation in optimization theory. They strongly believe that deep learning will revolutionize science and technology, and they aim to make powerful deep learning solutions accessible to scientists and engineers regardless of resource limitations.

Marc Adratمشاهده پروفایل

استاد

• Channel Coding
• Modulation Techniques
• Iterative Source-Channel Decoding
• +۷ مورد دیگر

Marc Adrat is an Honorary Professor at RWTH Aachen University and Head of the Software Defined Radio research group at Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE). His dual role combines academic teaching with cutting-edge industrial research in communications engineering. Education: Diplom-Ingenieur in Electrical Engineering (1997), RWTH Aachen University Dr.-Ing. (PhD) in 2003 from Institute of Communication Systems and Data Processing (IND), RWTH Aachen Research Focus: Prof. Adrat specializes in channel coding , modulation techniques , and iterative decoding with particular emphasis on polar codes , BICM-ID systems , and EXIT chart analysis . His work bridges theoretical foundations with practical implementations in software-defined radio systems. His recent research directions include applying machine learning techniques (particularly genetic algorithms) to optimize communication systems, developing autoencoder-based signal enhancement methods, and advancing spectrum monitoring technologies for cognitive radio applications. Awards & Recognition: Best Paper Award at ICMCIS 2022 for work on spectrum monitoring techniques Appointed Honorary Professor by RWTH Aachen University in June 2024 Teaching & Supervision: Since 2009, he has taught courses on Modern Channel Coding for Wireless Communications and Advanced Coding and Modulation at RWTH Aachen. His teaching covers both theoretical foundations and practical implementations of modern communication systems. Laboratories & Teams: At Fraunhofer FKIE, he leads the Software Defined Radio research group, focusing on developing flexible, reconfigurable radio systems for military and civilian applications. The group works extensively on real-time implementations of advanced coding and modulation schemes.