جست‌وجوی استاد و پژوهشگر

Jean-Yves Guillemautمشاهده پروفایل

مدرس ارشد

• 3D Computer Vision
• 3D Reconstruction
• Computational Photography
• +۴ مورد دیگر

Dr. Jean-Yves Guillemaut is a Senior Lecturer in 3D Computer Vision and Postgraduate Research Director at the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering at the University of Surrey. His research focuses on 3D reconstruction of complex dynamic scenes, particularly addressing challenges with glossy materials and uncontrolled outdoor environments. He collaborates with industry to apply his work in AR/VR, immersive content production, and free-viewpoint video. Education: He holds an MEng (hons), PhD, and is a member of professional bodies including MIEEE, MBMVA, and FHEA. His technical expertise spans computational photography, lightfield imaging, and artificial intelligence. Current PhD students: Matthew Bailey, Yue Zhang, Gianmarco Addari Laboratory affiliations: CVSSP Research interests include scene modeling from multi-view video, dynamic scene reconstruction, and applications in creative industries. Recent projects involve developing algorithms for illumination estimation, pedestrian tracking, and neural rendering. Grants and collaborations are centered on industry partnerships enhancing creative technology sectors. Future work aims to expand outdoor scene modeling and AI-driven medical imaging applications.

Steve Cassidyمشاهده پروفایل

دانشیار

• Research Data Management
• Linguistic Annotation Standards
• Speech Analysis Technology
• +۳ مورد دیگر

Steve Cassidy is an Associate Professor in the School of Computing at Macquarie University. His research focuses on research data discovery, reproducibility, and accessible tools for language sciences. He specializes in linguistic annotation standards, speech analysis technologies, and interoperability infrastructure for human communication science. He holds a PhD in Computer Science from Victoria University of Wellington, an MSc in Knowledge-Based Systems from the University of Edinburgh, and a BSc in Physics with Astrophysics from the University of Leicester. He leads major projects such as FAIMS3 (Field Acquired Information Management Systems) and the Alveo Virtual Laboratory, which support digital data capture and management in diverse research contexts. His work emphasizes FAIR data principles and interdisciplinary collaboration, with applications in linguistics, archaeology, and speech technology. He has contributed to initiatives like the AusTalk corpus and Signbank, advancing open science practices and accessibility in research. Recent research highlights include machine learning models for military performance prediction and temporally rich deep learning approaches for magnetoencephalography analysis. His work has been recognized through awards including the 2024 Teaching Excellence Highly Commendation and the 2023 Inter-School Collaboration Award. Education: PhD in Computer Science (Victoria University of Wellington, 1993) MSc in Knowledge-Based Systems (University of Edinburgh, 1984) BSc in Physics with Astrophysics (University of Leicester, 1983) Postgraduate Certificate in Higher Education (Macquarie University, 2008) Key Projects: FAIMS3: Mobile field data collection platform Alveo Virtual Lab: Linguistic data repository AusTalk Corpus: Large-scale Australian English speech database His publications span machine learning applications, speech technology, and open research infrastructure. He actively collaborates across disciplines to develop tools that enhance reproducibility and accessibility in scientific research.

Miles Hansardمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Robotics
• 3D Displays
• +۷ مورد دیگر

Miles Hansard is a Senior Lecturer in the School of Electronic Engineering & Computer Science at Queen Mary, University of London. He is affiliated with the Vision Group and the ARQ Centre for Advanced Robotics. His research focuses on computer vision, robotics, and 3D imaging technologies, including depth cameras, binocular vision, and scene reconstruction. He has contributed to projects like the GRIP Software Framework for robotics integration and Gaussian Process models for UAV localization. His work spans medical imaging applications, such as analyzing eye movements after vision loss, and explores discomfort factors in stereoscopic displays. Hansard has published extensively in journals like IEEE Transactions and the Royal Society Proceedings, and his research often addresses sensor fusion, calibration, and computational photography. He holds a primary email address at miles.hansard@qmul.ac.uk.

Josef Kittlerمشاهده پروفایل

استاد

• Pattern Recognition
• Computer Vision
• Image Analysis
• +۵ مورد دیگر

Josef Kittler is a Professor at the University of Surrey, affiliated with the School of Computer Science and Electronic Engineering and the Centre for Vision, Speech and Signal Processing (CVSSP). He holds editorial roles in journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and serves on the IAPR's KS Fu Prize Committee. His research focuses on pattern recognition, computer vision, and applications in system identification, medical diagnosis (ECG), and automatic inspection. He has received prestigious awards, including the Honorary Medal from the Czech Technical University. His work spans theoretical advancements and practical implementations, with notable contributions to multimodal tracking, deep learning, and fusion techniques. Education & Roles: Research Assistant, Engineering Department, University of Cambridge (1973–75) SERC Research Fellow, University of Southampton (1975–77) Royal Society European Research Fellow, Paris (1977–78) IBM Research Fellow, Balliol College, Oxford (1978–80) Principal Research Associate/Scientific Officer, Rutherford Appleton Laboratory (1980–85) Research Interests: Kittler's work emphasizes theoretical and applied aspects of pattern recognition, including computer vision applications in security, healthcare, and robotics. His recent publications address challenges in multimodal tracking, self-supervised learning, and fusion algorithms. Awards: Best Paper Awards from Pattern Recognition Society, British Machine Vision Association, and IEE Honorary Medal (Czech Technical University, 1995) Advising & Grants: While specific student/advisor details aren’t listed, his leadership in CVSSP and editorial roles indicates extensive mentorship and collaborative projects. His research has been supported by grants enabling advancements in AI and computer vision. Labs & Teams: He leads the CVSSP, a world-renowned center for vision, speech, and signal processing research, fostering interdisciplinary projects in AI and multimedia systems.

Chinmay Hegdeمشاهده پروفایل

دانشیار

• Machine Learning
• Algorithms
• Big Data
• +۶ مورد دیگر

Chinmay Hegde is an Associate Professor at the Tandon School of Engineering, NYU, with a joint appointment in Computer Science and Electrical Engineering. His research focuses on machine learning, signal processing, and their applications in cybersecurity, imaging, and materials science. He leads the DICE Lab, exploring data-driven solutions for complex engineering problems. Previously, he held positions at Iowa State University and MIT. Education: Ph.D. in Electrical and Computer Engineering, Rice University (2012) M.S. in Electrical and Computer Engineering, Rice University (2010) B.Tech. in Electrical Engineering, IIT Madras (2006) Research Interests: Machine Learning, Algorithms, Big Data, Signal Processing, Generative Models, Scientific Computing. Awards: NSF CAREER Award (2018-2023) NeurIPS Top Reviewer Award (2019) MRS Open Data Challenge Award (2019) Ralph Budd Award for Best Thesis (2013) Labs & Teams: Director of the DICE Lab, collaborating with industry partners on projects like real-time deepfake detection (GOTCHA), generative models for microstructure design, and AI-driven materials science.

Unnikrishna Pillaiمشاهده پروفایل

استاد

• Signal Processing
• Radar Systems
• Machine Learning
• +۳ مورد دیگر

Unnikrishna Pillai is a Professor of Electrical Engineering at the Tandon School of Engineering, New York University since 1995. He previously held roles at Polytechnic Institute of New York from 1985. His research focuses on radar signal processing, synthetic aperture radar (SAR), autonomous systems, machine learning, and portfolio risk management. He has co-authored five textbooks and developed over 250 educational YouTube videos. Education: Ph.D. in Systems Engineering (University of Pennsylvania, 1985), M.S. in Electrical Engineering (IIT Kanpur, 1982), B.Tech. in Electronics Engineering (Banaras Hindu University, 1980). Research emphasizes signal decomposition for clutter mitigation, waveform design for radar systems, and dynamic swarming for vehicle coordination. His work bridges theoretical contributions with practical applications in defense and transportation. Teaching contributions include supplementary video lectures on electrical engineering topics. Non-academic interests include cultural projects like translating the Bhagavad Gita and producing classical Indian music recordings.

Jeffrey Boydمشاهده پروفایل

دانشیار

• Computer Vision
• Image Processing
• Optical Flow
• +۳ مورد دیگر

Jeffrey Boyd is an Associate Professor in the Department of Computer Science at the University of Calgary's Faculty of Science, serving as Director of the Computational Media Design (CMD) initiative. His research focuses on computer vision, video analysis, optical flow, and motion tracking with applications in audio-locomotion synchronization and wearable technologies. He holds a B.S. (1983) and M.S. (1986) in Electrical Engineering from the University of Calgary, followed by a Ph.D. in Computer Science from the University of British Columbia (1994). Notable achievements include recognition for graduate supervision (2015 nominee) and innovation in interactive media (2014 Armour39 Challenge). His work bridges computational methods with creative applications, evident in publications like Emotional Musification (2018) and particle filter research (2016). Academic contributions span special topics courses on wearable applications and interdisciplinary collaborations in auditory display systems. Grants and advising activities are central to his role, though specific details are not elaborated in the provided texts. The CMD lab under his direction likely focuses on cutting-edge media design innovations, though explicit lab descriptions are absent.

• Machine Learning
• Signal Processing
• Pattern Recognition
• +۳ مورد دیگر

Mário A. T. Figueiredo is an IST Distinguished Professor and Feedzai Professor of Machine Learning at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST) . He leads the Lisbon Unit for Learning and Intelligent Systems (LUMLIS) within ELLIS and coordinates research clusters at Instituto de Telecomunicações. His research focuses on machine learning , signal processing , and image restoration , particularly using sparsity-based methods , wavelet transforms , and Bayesian algorithms . Recent work includes 3D shape correspondence , hyperspectral sharpening , and adaptive optimization techniques . Key article themes: Biclustering , ADMM algorithms , sparsity regularization , and generative embeddings Scientific recognition: EURASIP Fellow , Clarivate Highly Cited Researcher , IEEE/IAPR Fellow , and multiple best paper awards He has mentored numerous PhD/MSc students and contributed to information-theoretic kernels , feature discretization , and multimodal data analysis in biomedical and computer vision applications.

Giuliano Grossiمشاهده پروفایل

دانشیار

• Sparse representation models
• Signal processing
• Face recognition
• +۵ مورد دیگر

Giuliano Grossi is an Associate Professor at the Department of Computer Science, University of Milan. His research focuses on sparse representation models, signal processing, and GPU algorithms with applications in biomedical engineering and affective computing. He leads projects such as 'Stairway to Elders' for elderly wellbeing and contributes to tools like pyVHR for remote health monitoring. Grossi has supervised PhD students in areas including high-performance computing and stochastic models. Notable awards include Best Paper recognitions in 2017 and 2020. His work spans interdisciplinary fields from bioinformatics networks to facial recognition systems. Education: PhD in Computer Science (1999), University of Milan Positions: Assistant Professor (2001-2022), Associate Professor (2022–present) Research Interests: Sparse recovery methods, parallel algorithms, emotion recognition via multimodal analysis, and graph-based models for learning. Projects include EU T-NOVA for network virtualization and emotion interpretation frameworks funded by MIUR. Scientific Contributions: Over 100 publications in journals like IEEE Transactions and conference proceedings. Key contributions include GPU-accelerated algorithms, network biology analysis tools (UNIPred-Web), and frameworks for photoplethysmography (pyVHR).

Naser El-Sheimyمشاهده پروفایل

استاد

• Satellite Technologies
• Multi-sensor Systems
• Navigation and Positioning
• +۷ مورد دیگر

Dr. Naser El-Sheimy is a Professor at the University of Calgary's Schulich School of Engineering, Department of Geomatics Engineering, and holds the Tier I Canada Research Chair in Geomatics Multi-Sensor Systems. He is a Fellow of the Canadian Academy of Engineering (CAE) and the US Institute of Navigation (ION). His research focuses on multi-sensor systems, GNSS/INS integration, mobile mapping, and autonomous vehicle navigation. He has published over 500 papers and supervised 100+ students. Education: B.S. (Ain Shams University, 1984), M.S. (Ain Shams University, 1990), Ph.D. (University of Calgary, 1996). Entrepreneurial ventures: Founded Micro Engineering Group and Profound Positioning Inc., instrumental in TECTERRA's establishment. Research expertise includes inertial navigation algorithms, MEMS sensors, and applications in transportation and GIS. Awards include the ASTech Leadership Award and Killam Professorship. His work has advanced navigation technologies for self-driving cars and unmanned systems. Editorial roles: Editor-in-chief of Geomatics Journal, associate editor of ION Navigation Journal, among others. Leadership: Former ISPRS Commission I President (2008-2012).

• Image processing
• Remote sensing
• Estimation of model parameters
• +۷ مورد دیگر

Max Mignotte is a Full Professor at the Department of Computer Science and Operations Research , Faculty of Arts and Sciences, Université de Montréal. He leads research in Image Processing, Remote Sensing, and Bayesian Inference , focusing on applications in medical imaging, computer vision, and data fusion. His work includes developing advanced algorithms for saliency estimation, multimodal change detection, and 3D reconstruction. Research Interests: Image processing techniques for medical diagnostics, remote sensing analysis, and machine learning-driven solutions for visual attention and segmentation. His methodologies often integrate Bayesian models and fusion strategies to address complex problems in computer vision and biomedical applications. Key Projects: Leading the Multi-Dimensional Scaling Maps for Saliency Estimation initiative. Developing Bayesian fusion models for remote sensing applications. Advancing 3D biplanar reconstruction of human limbs using statistical models. Grants & Teams: Principal investigator on CRSNG-funded projects, including New unsupervised Bayesian and energy-based models (2022–2028) and Bayesian fusion models (2016–2023). Collaborates with the Laboratoire de traitement d’images and interdisciplinary teams in biomedical research.

Emanuele Maioranaمشاهده پروفایل

استادیار

• Biometric Recognition
• Biometric Template Protection
• Digital Signal Processing
• +۷ مورد دیگر

Emanuele Maiorana is an Assistant Professor (tenure track since 2023) at the Department of Engineering, Roma Tre University, Italy. He received his Laurea Degree cum laude in Electronics Engineering from Roma Tre University in 2004 and a Ph.D. with European Doctorate Label in 2009. His academic journey includes research engineering roles before transitioning to faculty. Research Focus: Specializes in digital signal/image processing with applications to: Biometric systems security and template protection Multimedia communications and telecommunication security HDR imaging, video watermarking, and stereo image enhancement Video textures synthesis and analysis Awards & Honors: Premio Galluzzi 2004 IEEE Lockheed Martin Best Paper Award (2007) IEEE Honeywell Student Best Paper Award (2008) Professional Service: General Chair for IEEE WIFS 2024, IEEE IWBF 2021, IAPR WMWB 2021 Associate Editor for IEEE Transactions on Information Forensics and Security Former Chair of EURASIP BForSec Technical Area Committee Teaching: Courses include Signal Processing for Big Data Analytics and Telecommunication Services and Networks Planning and Management at the Laurea Magistrale level. Research Projects: EU MSCA-ITN-2015 AMBER (Biometrics) EU MSCA-RISE-2015 ENCASE (Security/Privacy) FP7 projects SANDRA, JEOPARD, MINERVAA (Avionic Systems)

• Sensor Technologies
• Nanotechnology
• Nanomaterials
• +۹ مورد دیگر

Dr. Ata Khalid is a Senior Lecturer in Sensors and Sensor Systems at Cranfield Defence and Security, part of Cranfield University and the Defence Academy of the UK. He leads the Electro-Optic and Autonomy Group and teaches the Electro-Optic & Infrared Systems module in the MSc Military Electronic Systems Engineering program. Research Interests: His expertise spans Sensor Technologies, Nanotechnology, Nanomaterials, Energy and the Environment, and advanced semiconductor devices for THz, mm-wave, and mid-IR applications. His work focuses on compound semiconductors, nanofabrication, and photonic systems with defense and security applications. Publication Trends: His recent publications (2015–2024) show a strong focus on planar Gunn diodes, resonant tunneling diodes, InSb-based infrared detectors, and monolithic integration for THz and mid-IR systems. Key themes include device modeling, thermal management, antenna integration, and miniaturized sensor development for imaging and detection. Scientific Leadership: He has been a Co-I on major projects including the EPSRC EP/H011862/1 and the €4M Horizon 2020 TeraApps project, demonstrating significant research funding and collaboration. Advising and Grants: He has supervised numerous MSc projects and currently mentors research students Reece Dillon and Noor Mohammadd. His leadership in the TeraApps and EPSRC projects highlights his role in securing and managing research grants. Labs and Teams: He leads the Electro-Optic and Autonomy Group at Cranfield Defence and Security, focusing on advanced electronic warfare, information, and cyber systems with experimental work in nanofabrication and device testing.

Cem Direkoğluمشاهده پروفایل

استادیار

• Computer Vision
• Pattern Recognition
• Image and Video Analysis
• +۷ مورد دیگر

Cem Direkoğlu is a faculty member in the Department of Electrical and Electronics Engineering at Middle East Technical University - Northern Cyprus Campus (METU NCC), within the College of Engineering. His research is centered on computer vision and image analysis, with a strong emphasis on feature extraction, human behavior modeling, and video understanding. Research Interests: His primary research areas include computer vision, pattern recognition, image and video analysis, and signal processing. He focuses on human (individual and group) behavior analysis in sports and surveillance contexts, video event detection, object detection and localization, motion analysis and tracking, and feature/shape/skeleton extraction and segmentation. His ongoing projects involve crowd behavior analysis in surveillance videos, team behavior analysis in sports, and player detection and classification. Publication Trends: His recent work (2004–2014) shows a consistent focus on applying physical analogies (e.g., heat flow) and mathematical models to shape and feature extraction. Later publications shift toward higher-level video understanding, particularly in sports and surveillance, using motion trajectories, team activity modeling, and information retrieval methods for event detection. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no information available regarding students supervised or research grants received. However, his involvement in projects like SAVASA (TRECVid) suggests participation in collaborative research initiatives. Labs and Research Teams: While no specific lab is named, his work appears to be conducted within the research ecosystem of the Electrical and Electronics Engineering department at METU NCC, likely involving collaboration with researchers in multimedia and computer vision, as evidenced by co-authorship with researchers from institutions like Dublin City University.

• Deep Learning
• Speech Analysis
• Image and Video Analysis
• +۶ مورد دیگر

Ali Shariq Imran is an Associate Professor at the Department of Computer Science, Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. He holds a Ph.D. from the University of Oslo and a Master's from NUST, Pakistan. His research focuses on deep learning applications in speech analysis, image/video processing, semantic web technologies, and eLearning. He has published extensively in journals like IEEE Access, Engineering Applications of Artificial Intelligence, and Frontiers in Computational Neuroscience, with notable work on hate speech detection, biomedical signal analysis, and sentiment analysis in social media contexts. His awards include the HEC and UIUC scholarships and a 2015 Best Paper Award at HCI Intl. He serves on the board of the HCI International Conference and is an IEEE member. Teaching includes courses on coding/compression, software engineering, and web development. His competencies span AI, digital signal processing, and multimedia systems. Research interests include contextual understanding, OCR, and object identification in multimedia, alongside eLearning innovations like MOOCs and LMS integration. He has co-edited special issues on healthcare systems and software standards in journals like JMIHI and IEEE Access. His work frequently employs explainable AI (XAI) to enhance transparency in medical diagnostics and social media analysis.