جست‌وجوی استاد و پژوهشگر

Akram Alomainyمشاهده پروفایل

استاد

• Antenna Engineering
• Applied Electromagnetics
• Biomedical and Healthcare
• +۱۴ مورد دیگر

Professor Akram Alomainy is a distinguished academic at Queen Mary University of London, where he serves as Professor of Antennas and Applied Electromagnetics, Deputy Dean for Postgraduate Research in the Faculty of Science and Engineering, and Head of the Centre for Electronics within the School of Electronic Engineering and Computer Science. His research spans multiple interdisciplinary domains, with a strong emphasis on applied electromagnetics, wearable technology, and biomedical applications. His research interests include Antenna Engineering, Applied Electromagnetics, Biomedical and Healthcare, Communication Engineering, Radio Propagation, Wearable Technology, Terahertz Sensing, and Body-Centric Wireless Communication . His work integrates engineering principles with healthcare innovations, particularly in areas such as remote patient monitoring, smart sensors, and non-invasive diagnostics. The recent publications highlight a strong trend toward miniaturized and implantable antennas for medical devices, terahertz-based biosensing for cancer detection, millimeter-wave radar for health monitoring , and the application of machine learning and deep learning in signal and image processing for healthcare. These works demonstrate a consistent focus on solving real-world medical and communication challenges through advanced electromagnetic systems. Scientific recognitions include the 2011 British Science Festival Isambard Kingdom Brunel Award and the QMUL Education Excellence Award 2019 . He is also an Associate Editor for IEEE AWPL and IEEE J-ERM, reflecting his leadership in the research community. Prof. Alomainy leads several significant research grants funded by EPSRC, Huawei, and Innovate UK, covering areas such as smart sensors for virtual wards, antenna measurement metrology, and wearable-free health monitoring. He is actively involved in advising and leading research teams, contributing to advancements in nano-scale communication, cognitive radio, and wireless body area networks. His leadership in projects like the Wearable Technology and Creativity Research initiative underscores his commitment to innovation. He is affiliated with multiple research centres including the Centre for Electronics, Centre for Bioengineering, Centre for Brain and Behaviour, and Centre for Networks, Communications and Systems , indicating a collaborative and multidisciplinary research environment. His lab work focuses on developing wearable and implantable devices, RF sensing systems, and terahertz imaging tools for future healthcare applications.

• Machine Learning
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Louise Rixon Fuchs is an Industry doctoral student at KTH Royal Institute of Technology's Division of Robotics, Perception and Learning. She specializes in advanced sonar imaging techniques, machine learning applications, and signal processing for robotics. Her research focuses on enhancing sonar data analysis through deep learning methodologies, optimization algorithms, and compressive sensing models. Her work bridges robotics and computer vision, addressing challenges in low-resolution sonar image processing, object detection, and beamforming optimization. Key contributions include GAN-based sonar image simulations and transfer learning for object recognition in sonar environments. Louise has published extensively on sonar technology advancements, with recent articles emphasizing dense point correspondence algorithms and wide-beam sonar system improvements. She currently holds no recorded scientific awards but is actively contributing to robotics and marine sensing research.

Michalis Matthaiouمشاهده پروفایل

استاد

• Wireless Communications
• Signal Processing
• Massive MIMO
• +۳ مورد دیگر

Michalis Matthaiou is a Professor of Communications Engineering and Signal Processing at Queen’s University Belfast (QUB), UK, and Deputy Director of the Centre for Wireless Innovation (CWI). He holds a Diploma in Electrical and Computer Engineering from Aristotle University of Thessaloniki (2004), an M.Sc. (Distinction) from the University of Bristol (2005), and a Ph.D. from the University of Edinburgh (2008). Prior roles include Assistant Professor at Chalmers University of Technology (Sweden) and postdoctoral research at TU Munich (Germany). His research focuses on signal processing for wireless communications, massive MIMO, intelligent reflecting surfaces, and AI-driven systems. He leads a team of 20 researchers under multi-million-pound grants, including the ERC Consolidator Grant BEATRICE (2021–2026). **Affiliations**: Centre for Wireless Innovation, Institute of Electronics, Communications & Information Technology **Awards**: IEEE Fellow (2023), Leonard G. Abraham Prize (2017), EURASIP Early Career Award (2019) His work spans over 340 publications (140+ journal papers) and includes groundbreaking contributions to wireless systems. He supervises PhD students in areas like edge computing, reconfigurable intelligent surfaces, and millimeter-wave systems. He serves as Editor-in-Chief of Elsevier’s Physical Communication and Senior Editor for IEEE journals.

• Sparse optimization
• Machine learning
• Convex and nonconvex optimization
• +۷ مورد دیگر

Sophie Fosson is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, Italy. She is a member of the System Identification & Control (SIC) research group and teaches courses such as Automatic Control and Modeling and Control of Cyberphysical Systems at both the master's and doctoral levels. Education: PhD in Mathematics for Industrial Technologies, Scuola Normale Superiore di Pisa Master’s in Mathematical Engineering, Politecnico di Torino Her research focuses on sparse optimization, machine learning, system identification, and control theory . She integrates mathematical modeling with practical applications in cyber-physical systems and neural networks. Her work emphasizes efficient and robust optimization techniques for deep learning and control systems. The recent publications highlight a strong trend in optimization of neural networks , particularly through sparse training, binary and ternary quantization, self-supervised learning, and fault tolerance . Her research bridges control theory with machine learning, applying mathematical rigor to modern AI challenges in safety-critical and embedded environments. Scientific Awards: No awards explicitly mentioned. She supervises several PhD students including Alice Re, Rosario Milazzo, and Simone Pirrera , and has been involved in research projects such as CRISP and collaborations with Centre Tecnològic de Telecomunicacions de Catalunya . She has held postdoctoral positions at DISMA and DET (Politecnico di Torino) and worked as a researcher at Istituto Superiore Mario Boella. Laboratories and Research Groups: System Identification & Control (SIC), DAUIN, Politecnico di Torin

Greg Hamerlyمشاهده پروفایل

استاد

• Machine Learning
• Unsupervised Learning
• Data Clustering
• +۱۰ مورد دیگر

Greg Hamerly is a Professor and former Interim Chair (2022–2025) in the Department of Computer Science at Baylor University’s School of Engineering and Computer Science. He holds a PhD from the University of California, San Diego (2003) and specializes in machine learning, particularly unsupervised learning and data clustering, with real-world applications in medical imaging and ecological monitoring. PhD, University of California San Diego (2003) MS, University of California San Diego (2001) BS, California Polytechnic State University (1999) His research centers on the development and application of efficient machine learning algorithms. Key areas include improving k-means clustering techniques, federated learning, and applying AI to early detection of retinoblastoma through leukocoria in photographs and monitoring invasive aquatic species using video data. He has also contributed to computational thinking curriculum development. His recent publications reflect strong trends in unsupervised and self-supervised learning, with a focus on algorithmic efficiency, robustness, and real-world deployment in healthcare and environmental science. His work often combines clustering, deep learning, and computer vision for practical applications. Dubious awards (as humorously noted on personal website) Chief Judge for ICPC South Central USA regional competition (2018, 2019) Site Director for ICPC South Central USA regional competition (2010–2013, 2015–2019) Member of ICPC Live Analytics team at ICPC World Finals (2011–2015) Participant in ICPC World Finals as contestant (2000, 2001) Greg Hamerly has advised numerous graduate students, including PhD candidates now at Texas A&M Kingsville and Meta. He has secured research funding from the NSF, NIH, Department of the Interior, and Texas Parks and Wildlife Department. He is also deeply involved in competitive programming, having coached students and developed the Competitive Learning course at Baylor with over 300 original problems. He leads research projects on eye disease detection and invasive species monitoring, often in collaboration with interdisciplinary teams. His lab integrates algorithmic innovation with practical deployment, emphasizing accessibility and real-world impact.

• Internet of Things (IoT)
• Smart Grids
• Renewable Energy Systems
• +۳ مورد دیگر

Mohamed A. Ahmed is a researcher affiliated with Central Michigan University, School of Engineering & Technology , with additional collaborations across institutions like Ain Shams University, Memorial University of Newfoundland, and Prince Sultan University. His work spans interdisciplinary domains including Internet of Things (IoT) Smart Grids Renewable Energy Systems Machine Learning Recent research focuses on IoT platforms for smart energy management , including electric vehicle charging infrastructure, remote elderly care systems, and large-scale wind turbine monitoring. He has contributed to wireless network architectures for cyber-physical energy systems and developed fuzzy logic algorithms for EV charging prioritization. Publications highlight expertise in time-series forecasting for energy demand, clustering algorithms for smart grid planning, and data aggregation techniques in wind energy applications. Collaborations frequently involve researchers such as Young-Chon Kim, Ali Mohamed Eltamaly, and José Luis Gallardo.

• Robotics
• Machine Learning
• Electrical Engineering
• +۳ مورد دیگر

Xinyu Qin is a Professor at the Department of Electrical and Computer Engineering within the School of Information Engineering at Guangdong University of Technology. His research focuses on advanced robotics, signal processing, and integrated circuit design, contributing to fields like multi-manipulator systems and Delta-Sigma modulators. Education: Affiliated with prestigious institutions through collaborative research Research Interests: Robotics, Machine Learning, Electrical Engineering His recent publications (2023-2025) demonstrate expertise in robotic task allocation, high-speed circuit design, and explainable AI for healthcare. Award-winning work includes Interactive Explainable Deep Survival Analysis (2024) and SVP: Safe and Efficient Speculative Execution Mechanism through Value Prediction (2023). Key collaborations involve Guoxing Wang and Liang Qi across 16 records. Current projects involve optimizing convolutional neural network accelerators, analyzing atmospheric river impacts on Greenland's crustal deformation, and advancing MASH Delta-Sigma modulator architectures. His work bridges theoretical innovation with practical applications in smart energy systems and autonomous robotics.

Yaniv Planمشاهده پروفایل

دانشیار

• Applied Probability
• Compressive Sensing
• Matrix Completion
• +۳ مورد دیگر

Yaniv Plan is an Associate Professor of Mathematics at the University of British Columbia. His research focuses on applied probability, compressive sensing, matrix completion, and mathematical foundations of machine learning. He co-organizes the interdisciplinary group Mathematics of Information, Learning, and Data (MILD). His work bridges high-dimensional probability with applications in signal processing and data science. He teaches advanced courses such as Probability in High Dimensions and Compressed Sensing , emphasizing theoretical foundations like concentration inequalities, random matrix theory, and optimization. His research has led to advancements in 1-bit compressed sensing, matrix completion, and robust recovery algorithms. Notably, he received the NIPS 2018 Best Paper Award for contributions to learning Gaussian mixtures via sample compression. Plan’s publications address topics like sub-Gaussian matrices, weighted matrix completion, and non-smooth stochastic gradient descent. His work often combines rigorous mathematical analysis with practical applications in compressed sensing and sparse recovery. He collaborates widely, with co-authors including Roman Vershynin, Emmanuel Candès, and Mary Wootters. His research has been supported by grants exploring compressed sensing, high-dimensional data, and algorithmic robustness.

Guillaume Tochonمشاهده پروفایل

استادیار

• Mathematical Morphology
• Statistical Signal Processing
• Machine Learning
• +۴ مورد دیگر

Guillaume Tochon is an Assistant Professor at EPITA since 2016 and an Invited Researcher at the Institut de Mécanique Céleste et de Calcul des Éphémérides (IMCCE, Observatoire de Paris) since 2022. He holds a M.Sc. and Ph.D. in electrical engineering and signal/image processing from Grenoble Institute of Technology and Université Grenoble Alpes, with postdoctoral experience at Carnegie Institution for Science and UCLA. His research focuses on mathematical morphology, statistical signal processing, and machine/deep learning, applied to space imagery (e.g., astrometry) and satellite data analysis. He leads projects on morphological neural networks, Sentinel-2 time series dynamics, and meteor/satellite detection using 1-second exposure imagery. Education: M.Sc. in Electrical Engineering, Grenoble Institute of Technology (2012) Ph.D. in Signal and Image Processing, Université Grenoble Alpes (2015) Visiting Scholar, UCLA Department of Mathematics (2013) Research Interests: Combines mathematical morphology with machine learning to address challenges in remote sensing and space imagery. Key areas include hierarchical representations, noise analysis, and applications in astrometry and hyperspectral unmixing. Current projects involve neural Koopman operators for forecasting, Sentinel-2 time-series assimilation, and meteor detection in night-sky imagery. Teaching: At EPITA, he teaches Mathematics for Signal Processing, Data Compression, Image Processing, Convex Optimization, Machine Learning, and Constrained Optimization across undergraduate and graduate levels. Collaborations: Works with researchers at IMCCE (e.g., V. Lainey, J. Vaubaillon), IMT-Atlantique, and Grenoble-INP. Supervises PhD students in topics such as noise estimation, spectral dynamics, and Cassini image analysis. Labs/Teams: Active in EPITA Research Laboratory's Image Processing and Pattern Recognition team and IMCCE's PEGASE team.

Patrick E. Manteyمشاهده پروفایل

استاد

• Signals and Systems
• Image and Signal Processing
• Geographic Information Systems
• +۱۵ مورد دیگر

Professor Patrick E. Mantey is a distinguished faculty member at the University of California, Santa Cruz (UCSC), where he serves in the Jack Baskin School of Engineering. He is the founding dean of the school (1997–2001) and continues to contribute as a professor in Computer Engineering. He also holds leadership roles as Director of the Institute for Networking, Information Systems and Technologies and as the UCSC affiliate director of CITRIS. His career bridges academia and industry, with prior experience at IBM and teaching at Stanford University. Ph.D., Electrical Engineering, Stanford University (NSF Fellow) M.S., Electrical Engineering, University of Wisconsin-Madison B.S., Electrical Engineering, University of Notre Dame (magna cum laude) His research spans signals and systems, with early contributions in digital signal processing, geographic information systems, and database applications. His current focus is on resilient and renewable energy systems through the RREES Lab, where he investigates microgrid energy management, anomaly detection, and federated microgrid systems. His work integrates machine learning, real-time data analysis, and decision support systems. The majority of his recent publications center on real-time environmental monitoring, multimedia systems, and educational technology. Trends show a strong emphasis on distributed systems, data visualization, and networked applications in environmental and educational contexts. His earlier work laid foundations in digital filtering, power systems, and interactive geographic data systems. Fellow of the IEEE (2001) NSF Fellowship during doctoral studies Microsoft, NOAA, NSF, and U.S. Army Corps of Engineers research awards Professor Mantey has advised numerous students through capstone projects and research initiatives, particularly in networked systems and environmental informatics. His research has been supported by major grants from the National Science Foundation, Office of Naval Research, NOAA, and industry partners including IBM, Cisco, Sun, Microsoft, and Hewlett-Packard. He has led large-scale projects such as REINAS and contributed to the development of the UCSC Scientific Visualization Laboratory. He is actively involved in multiple research groups, including the Storage Systems Research Center (SSRC), the Telecollaboration Lab, the Computer Communication Research Group (CCRG), and the CARNIVORE Project. These labs focus on storage systems, educational technology, wireless networks, and environmental sensor networks, reflecting the interdisciplinary nature of his work.

• Edge Computing
• Mobile Computing
• Wireless Networking
• +۹ مورد دیگر

Sanglu Lu is a Professor in the Department of Computer Science at Nanjing University's School of Computer Science and Engineering. With over 400 publications spanning from 1998 to 2025, Professor Lu has established a significant research presence in mobile computing, edge computing, and wireless sensing technologies. Their work frequently appears in top-tier conferences including INFOCOM, IEEE Transactions journals, and AAAI. Professor Lu's research focuses on cutting-edge areas in computer science, particularly in edge AI systems, millimeter-wave and RFID-based sensing, graph neural networks, and time series analysis. Their work bridges theoretical advances with practical applications, often addressing resource-constrained environments and real-world deployment challenges. Recent publications show a strong emphasis on privacy-preserving sensing techniques, efficient model deployment at the edge, and multimodal approaches to human activity understanding. Analysis of publication trends reveals Professor Lu's work has evolved from traditional mobile networking topics toward more AI-centric research, particularly in the last five years. The research demonstrates strong interdisciplinary connections between networking, sensing, and machine learning, with increasing focus on practical applications in healthcare, accessibility technology, and industrial IoT systems. Publications consistently show collaboration with researchers across China and internationally, indicating an active research group and extensive professional network. Professor Lu has mentored numerous researchers as evidenced by the extensive publication record with various co-authors, though specific student names aren't detailed in the provided information. Their work has received significant attention in the research community, as reflected by the substantial number of publications in high-impact venues. Current research directions include innovative approaches to edge AI, advanced wireless sensing techniques using commercial hardware, and novel neural network architectures for time series and graph data. Professor Lu's laboratory appears to be actively working on real-world applications of these technologies, particularly in health monitoring, accessibility solutions, and industrial automation contexts.

Gary Schajerمشاهده پروفایل

استاد

• Industrial Residual Stress Measurements
• Full-Field Optical Metrology
• X-Ray CT Log Scanning
• +۳ مورد دیگر

Professor Gary Schajer is a faculty member at the University of British Columbia , affiliated with the Faculty of Applied Science and the Department of Mechanical Engineering . He holds multiple professional designations including P.Eng., C.Eng., and fellowships with IMechE and ASME. Research Interests : Industrial Residual Stress Measurements Full-Field Optical Metrology (ESPI, DIC) X-Ray CT Log Scanning for wood quality assessment Scientific Awards : Lazan Award (2013) Most Outstanding Mech2 Professor (2005) Teaching Award for Excellence in Engineering and Geoscience Education (2005) Marquardt Wood Engineering Research Award (2004) Faculty Recognition Award (2004)

• Artificial Intelligence
• Multimedia Systems
• Robotic Interaction
• +۵ مورد دیگر

Wang Yufei is affiliated with Xiamen University, China, holding positions across the School of Informatics, Institute of Artificial Intelligence, Center for Digital Media Computing, and School of Film. The research spans interdisciplinary domains, integrating artificial intelligence, robotics, and multimedia systems for real-world applications. Wang Yufei's research primarily focuses on artificial intelligence , computer vision , robotic interaction , and multimedia systems . Key areas include 3D scene modeling using Gaussian splatting, diffusion models for storytelling and image generation, reinforcement learning for interactive instruction, and AI applications in healthcare, agriculture, and urban planning. The work emphasizes robustness, real-time feedback, and human-centered design. The recent publications demonstrate a strong trend in deep learning applied to vision and multimodal systems , with increasing focus on interactive AI , embodied environments , and domain adaptation . Topics such as differentiable trajectory optimization, backdoor attacks in compression models, and context-aware assistance systems reflect both technical depth and societal relevance. Wang Yufei has not been mentioned in relation to any scientific awards in the provided text. No information is available regarding student advising or research grants. However, the breadth and volume of publications suggest active research leadership and likely involvement in funded projects. Research is conducted in collaboration with teams at Xiamen University, particularly within the Institute of Artificial Intelligence and the Center for Digital Media Computing, focusing on intelligent multimedia and robotic systems. The integration of AI with cultural and educational applications, such as piano instruction and heritage preservation, highlights a unique interdisciplinary lab environment.

• Chaos theory
• Compressed sensing
• Electromagnetic interference
• +۱۴ مورد دیگر

Fabio Pareschi is an Associate Professor at the Department of Electronics and Telecommunications (DET), Politecnico di Torino, where he conducts research in circuit architectures, embedded systems, and signal processing with applications in security, AI, and power electronics. He is affiliated with the VLSILAB research group and leads multiple high-impact research projects. Research Interests: Chaos theory and true random number generation for cryptographic applications Compressed sensing for secure and efficient signal acquisition EMI reduction techniques in DC-DC power converters Tiny machine learning and low-power embedded systems Circuit design for IoT and biomedical applications The recent articles highlight a strong trend in integrating compressed sensing with encryption, leveraging chaos-based randomness for security, and optimizing power electronics for EMI reduction. His work bridges theoretical foundations with practical hardware implementations in microelectronics and embedded systems. Scientific Awards: Best Student Paper Award (IEEE, 2005) Best Paper Award (IEEE, 2005) IEEE PRIME Gold Leaf Certificate (2019) BioCAS Transactions Best Paper Award (2019) Best Student Paper Award at EMCCompo (IEEE, 2019) Advising and Grants: He supervises multiple PhD students in the Electrical, Electronics, and Communications Engineering program. He is the Scientific Director of the CESOIA project (Non-EU International Research, 2025–2028) on low-complexity AI models, and leads the ECS4DRES project (EU-funded, 2024–2027) on resilient energy systems. He also heads a commercial research project on high-performance DC-DC converters (2022–2025). His editorial roles include Associate Editor for IEEE Transactions on Circuits and Systems and guest editorships in multiple IEEE journals. Labs and Teams: He is a key member of the VLSILAB Group (DET), which focuses on VLSI systems, embedded signal processing, and secure hardware design.

Eric Millerمشاهده پروفایل

استاد

• Signal and Image Processing
• Tomographic Image Formation
• Inverse Problems
• +۴ مورد دیگر

Eric Miller is a Professor of Electrical and Computer Engineering at Tufts University's School of Engineering. He also holds adjunct professorships in Computer Science, Biomedical Engineering, and Mathematics. His academic roles include serving as Chair of the Electrical and Computer Engineering department and leading the Lab for Imaging Science Research (LaISR). Miller earned his SB, SM, and PhD in Electrical Engineering from MIT (1990–1994). His research focuses on signal and image processing, particularly inverse problems, tomographic imaging, and applications in medical imaging, environmental monitoring, and security screening. He has pioneered methods like the parametric level-sets (PaLEnTIR) for reconstruction and shape-based inversion algorithms. His work integrates physics-based modeling with computational techniques, addressing challenges in subsurface sensing, biomedical diagnostics, and materials science. Miller is a Fellow of IEEE and a member of honor societies like Tau Beta Pi and Phi Beta Kappa. Over 489 publications reflect his contributions to imaging science, with recent advancements in AI-driven pedestrian behavior analysis and X-ray anomaly detection.