جست‌وجوی استاد و پژوهشگر

Hector Santos-Villalobosمشاهده پروفایل

استادیار

• Computer Vision
• Signal Processing
• Machine Learning
• +۷ مورد دیگر

Hector Santos-Villalobos is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. His research focuses on advanced imaging systems, biometric recognition, seismic analysis, and nondestructive evaluation techniques. He holds a PhD in Electrical and Computer Engineering from Purdue University (2010), and MS/BS in Computer Engineering from the University of Puerto Rico Mayagüez (2005/2003). Education: PhD, Electrical and Computer Engineering, Purdue University (2005–2010) MS, Computer Engineering, University of Puerto Rico Mayagüez (2003–2005) BS, Computer Engineering, University of Puerto Rico Mayagüez (1998–2003) Research interests span computer vision applications in biometric systems (e.g., iris recognition, facial screening), seismic imaging for geothermal energy projects (EGS Collab), and ultrasound-based nondestructive evaluation of materials. His work emphasizes privacy-preserving algorithms, iterative reconstruction techniques, and multimodal data fusion. Notable projects include developing the BRIAR dataset for long-distance facial recognition and creating handheld multi-sensor biometric devices. Publications highlight trends in: Biometric security (privacy-preserving facial screening, bias-mitigated face ID) Ultrasound imaging for concrete defect detection Geothermal energy exploration via seismic tomography Deep learning applications in seismic phase picking and image reconstruction Grants and collaborations involve geothermal energy initiatives (e.g., EGS Collab), Department of Energy projects, and National Science Foundation funding. His work bridges computational methods with practical engineering challenges in imaging and material analysis.

David Sinclairمشاهده پروفایل

دانشیار

• Formal Methods
• Distributed Systems
• Real-time Systems
• +۲ مورد دیگر

Dr. David Sinclair is an Associate Professor and Year Head in the School of Computing at Dublin City University, affiliated with the Dependable Systems, Software Engineering, and Security research groups. His research spans formal methods, distributed systems, real-time systems, and AI applications in strategic games. Teaching responsibilities include foundational and advanced courses: CSC1018 Logic CSC1048 Computability & Complexity CSC1098 Compiler Construction CSC1141 Concurrent Programming CSC1049 Third Year Project supervision His publications demonstrate strong focus on real-time systems, motion prediction, and immersive technologies, with recent work exploring AI-driven virtual environments and light field processing. Research consistently addresses computational efficiency and formal verification challenges.

Hassan Aqeel Khanمشاهده پروفایل

مدرس ارشد

• Medical Imaging AI
• Neurotechnology
• Computational Pathology
• +۲ مورد دیگر

Dr. Hassan Aqeel Khan is a Senior Lecturer in Applied AI & Robotics at Aston University's School of Computer Science and Digital Technologies. His research focuses on AI/ML applications in healthcare, particularly medical imaging, neuroscience, and resource-constrained settings. He holds a Ph.D. from Michigan State University and is a Fellow of the Higher Education Academy (FHEA). Research Focus: Key innovations include: Eye-tracking systems for histopathology annotation Open-source EEG analysis tools (e.g., NeuroAssist) AI diagnostics for low-resource healthcare environments Publication Trends: Recent works (2021-2024) emphasize accessible AI tools for neurological monitoring and cancer diagnosis, with 60% focusing on open-source medical datasets and algorithms. Teaching: Teaches Programming Languages, Enterprise Computing, and Machine Learning. Previously developed curricula at University of Jeddah and NUST Pakistan. Laboratory: Leads healthcare AI research in the Engineering for Health group and Aston Centre for AI Research.

• Biological and Artificial Vision
• Human Sensory Augmentation
• Secure Data Analytics
• +۳ مورد دیگر

Dr. Noel Murphy is an Associate Professor and Head of the School of Electronic Engineering at Dublin City University (DCU). He has held various leadership roles, including Associate Dean for Education in the Faculty of Engineering and Computing (2005-2007), Head of School (2007-2012, 2015-2018, and returning in 2021). He has been teaching at DCU since 1986 and has contributed extensively to academic governance, including roles on Academic Council and Governing Authority committees. Dr. Murphy holds a BA (Mod) in Theoretical Physics from Trinity College Dublin (TCD), a PhD from DCU (2003) on Information Theory models for Biological and Artificial Vision, and a part-time MA in Pure Mathematics from Maynooth University (2015). His research focuses on: Biological and Artificial Vision: Including Information Theoretic models and the work of Stephen Grossberg. Secure Data Analytics: Particularly leveraging SGX-enabled applications for data ownership control. Algebraic Number Theory: Exploring elliptic curves and Apéry's Constant. IoT and Edge Computing: Focusing on secure processing for IoT applications. Human Sensory Augmentation and Multimedia Information Retrieval. In addition to his teaching and leadership roles, Dr. Murphy has supervised numerous PhD and Master's students to completion. He has secured funding as Principal Investigator or co-Investigator on national and EU-funded research projects. He is affiliated with DCU's Entwine Research Centre. Outside academia, Dr. Murphy is Honorary Secretary of the Irish Light Aviation Society, holds a Private Pilot’s License, and is involved in aircraft restoration projects, including a 1940 Piper J5 and a Vans RV-8.

• High Performance Computing
• Embedded Systems
• Medical Imaging
• +۴ مورد دیگر

Elisa Marenzi is an Assistant Professor at the University of Pavia's Department of Industrial and Information Engineering. She holds a PhD in Bioengineering and Bioinformatics (2014) and master's/bachelor's degrees in Biomedical Engineering from the same institution. Her research focuses on High Performance Computing (HPC), embedded systems for biomedical monitoring, signal processing in rehabilitation/automotive fields, and cerebellar neural circuit modeling. Education: PhD in Bioengineering (2014): Thesis on pressure ulcer monitoring systems Master's in Biomedical Engineering (2010): Thesis on capacitive sensor systems Bachelor's in Biomedical Engineering (2007): Thesis on medical guideline formalization Research Highlights: Her work spans HPC optimization for hyperspectral imaging, wearable medical devices, and neurocomputational modeling of cerebellar circuits. Notable contributions include: STRATUM project: AI-driven 3D decision support tools for neurosurgery HBP (Human Brain Project) simulations of hippocampal dynamics Hyperspectral imaging applications in skin cancer diagnosis FPGA-based real-time medical signal processing systems Awards: ETIC Award (2014) for PhD thesis Lifebility Award (2012) for social tech innovation NVIDIA GPU Research Center accreditation (2016) Professional Activities: She has held roles including Systems Administrator (2020-2023) and post-doctoral research in neurocomputation (2017-2019). Active in editorial roles for Microprocessors and Microsystems and Frontiers in Computational Neuroscience. Co-chair for ASHWPA special session at Euromicro DSD 2024. Labs/Teams: Key affiliations include the Neurocomputation Laboratory (DBBS) and collaboration with NVIDIA GPU Research Center.

• Natural Language Processing
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Dr. Vimal Kumar Kumar serves as a Research Fellow at the University of Limerick's Department of Computer Science and Information Systems, affiliated with Lero – the Irish Software Research Centre. His academic career includes prior faculty positions as Assistant Professor at Jaypee University of Information Technology (2009-2022) and Lecturer at SRM Institute of Science and Technology (2007-2009), followed by a Postdoctoral Researcher role at University College Dublin (2022-2023). His educational background includes: PhD in A Novel sense based Hindi to Tamil machine translation system from Jaypee Institute of Information Technology (2019) Masters from Anna University Chennai (2005-2007) Bachelor from PSG College of Technology (2000-2004) Dr. Kumar's research centers on Natural Language Processing with specialized focus on machine translation for Indian languages (Hindi, Tamil), text summarization, and sentiment analysis. He extends this work into computer vision applications for medical imaging and activity recognition, plus deep learning implementations for IoT-based agricultural monitoring. His methodology frequently addresses challenges in low-resource language processing and develops optimized neural network architectures for mobile deployment. Analysis of his publication trends reveals consistent interdisciplinary innovation, particularly in healthcare applications (retinal disease detection), agricultural technology (crop yield prediction), and social media analytics. His work demonstrates strong emphasis on practical AI solutions for resource-constrained environments and underrepresented linguistic communities, often leveraging generative models and contextual embeddings. Dr. Kumar actively contributes to Lero's research initiatives, aligning his technical work with UN Sustainable Development Goals through software solutions addressing global challenges in health, food security, and digital inclusion.

• Robotics
• Autonomous Vehicles
• Computer Vision
• +۹ مورد دیگر

Dr. Asanka Perera is a Lecturer in Mechatronic Engineering at the University of Southern Queensland (UniSQ) within the School of Engineering. He holds a PhD from the University of South Australia (UniSA), an MSc in Industrial Automation from the University of Moratuwa, and a BSc in Electrical and Electronic Engineering from the University of Peradeniya. His research focuses on robotics, autonomous systems, computer vision, and machine learning with applications in drones, adversarial attacks on 3D point clouds, and medical imaging. Notable projects include bio-inspired navigation systems, multi-robot collaboration, and non-contact vital signs monitoring. Dr. Perera has secured grants including AUD 17,546 from the US Department of Air Force for bio-inspired navigation research and AUD 68,645 for heterogeneous robot swarms. He actively supervises PhD students exploring topics like robust 3D object recognition and vision-based localization in low-light conditions. He has taught courses at UNSW Canberra and UniSA, including Robotics & AI labs, autonomous systems design, and control systems. His professional memberships include IEEE, Engineers Australia, and the Australian Computer Society. Dr. Perera is affiliated with UniSQ's Centre for Future Materials and Centre for Astrophysics. His work has been featured in media outlets such as Scientific American and ABC Australia.

Christos P. Loizouمشاهده پروفایل

استاد مدعو

• Medical Imaging
• Video Analysis and Processing
• Motion Analysis
• +۶ مورد دیگر

Christos P. Loizou is an Adjunct Professor at the Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Cyprus. With over 25 years of experience, his expertise spans medical imaging, video analysis, signal processing, and their applications in medicine. Education: PhD, 2005, Kingston University MSc, 1989, Kaiserslautern University BSc, 1987, Kaiserslautern University Research Interests: His work focuses on medical imaging techniques, video processing, motion analysis, and biosignal analysis (e.g., ultrasound and MRI). He also explores pattern recognition and voice stress analysis, with applications in healthcare and medical informatics. Grants/Advising: No specific grants or advisees listed in the provided information. Labs/Teams: No affiliated labs or teams mentioned.

• Deep Learning
• Neural Networks
• High-Performance Digital Systems
• +۴ مورد دیگر

Larry Pearlstein is an Associate Professor of Electrical and Computer Engineering at The College of New Jersey (TCNJ). He specializes in deep learning, neural networks, high-performance digital systems, and video/image processing. He served as Chairman of the ATSC Video Specialists Group, developing standards for digital TV in the US. With over 70 patents in video processing and high-performance computing, his work spans frame rate conversion, digital video recording, and embedded systems design. Education: Ph.D. in Electrical Engineering, Princeton University (1987) M.S. in Electrical Engineering, Princeton University (1984) B.S. in Electrical Engineering, Drexel University (1982) Research Interests: His research focuses on deep learning applications, high-performance digital systems, and video/image processing. He explores transfer learning for fault diagnosis, GAN-based synthetic image generation, and IoT anomaly detection. His work integrates AI into medical devices (e.g., mHealth smoking cessation tools) and cybersecurity (malware classification). Publications: Recent work includes studies on transfer learning for bearing fault diagnosis, DCNN optimization for image fusion, and parameterized synthetic image generation via GANs. He also contributes to medical device development (e.g., T-COM) and video coding efficiency. Advising & Grants: While no specific advisees are listed, he teaches advanced courses in neural networks, digital signal processing, and embedded systems, likely mentoring student projects. His patents and grants reflect industry-academia collaboration in video processing and hardware design.

Navrati Saxenaمشاهده پروفایل

دانشیار

• 5G wireless
• 6G wireless
• IoT
• +۷ مورد دیگر

Navrati Saxena is an Associate Professor in the Department of Computer Science at San José State University (SJSU), California, USA. She previously served as an Assistant/Associate Professor and Director of the Mobile Ubiquitous System Information Center (MUSIC) at Sungkyunkwan University (SKKU), South Korea. Her research focuses on next-generation wireless networks (5G/6G), IoT, vehicular communications, satellite networks, and machine learning applications in telecommunications. She holds a Ph.D. in Informatics and Communications Engineering from the University of Trento, Italy. Education: Ph.D. in Informatics and Communications Engineering, University of Trento, Italy (2005). Research Interests: 5G/6G wireless systems, IoT, social networking, smart grids, D2D communications, vehicular networks, satellite communications, and machine learning-driven network optimization. Her work spans energy efficiency, network architecture design, and disaster-resilient connectivity. Grants & Awards: Secured over $465,000 in research grants from institutions like the Korea Research Foundation and Samsung. Notable awards include the Young Scientist Best Paper Award (Indian Science Congress Association, 2006-2007). Professional Contributions: Supervised 12 Ph.D. students and 22 MS researchers at SKKU. Authored/co-authored one book, two book chapters, and over 90 international journal/conference papers. Served as a guest editor for journals and TPC member for IEEE conferences. Active in diversity initiatives at SJSU, including leadership roles in DEI and anti-racism committees. Labs & Teams: Leads the Mobile Ubiquitous Systems Information Center (MUSIC) at SJSU, focusing on edge-cutting research in wireless networks and education.

Shahrukh Atharمشاهده پروفایل

استادیار

• Image and Video Processing
• Perceptual Image Quality Assessment
• Digital Signal Processing
• +۳ مورد دیگر

Dr. Shahrukh Athar is an Assistant Professor in the Department of Electrical & Computer Engineering at McMaster University. His research focuses on perceptual image quality assessment (IQA), degraded-reference and multiply-distorted image analysis, and digital signal/video processing. He also emphasizes educational development, particularly active learning strategies and pedagogical training for colleagues and graduate students. He teaches courses such as COMPENG 2DX3 (Microprocessor Systems Project), COMPENG 2SH4 (Principles of Programming), ELECENG 2CI4 (Introduction to Electrical Engineering), and ELECENG 3CL4 (Introduction to Control Systems). His work bridges machine learning applications in IQA with practical educational methodologies. Dr. Athar's research includes developing robust no-reference IQA algorithms using deep learning and constructing large-scale datasets. He has published extensively on topics like image debanding, blind quality assessment, and FPGA-based signal processing implementations. His LinkedIn and other professional profiles highlight interdisciplinary contributions in both technical and educational domains.

Michael Bruenigمشاهده پروفایل

استاد

• Robotics
• Cybersecurity
• Data Innovation
• +۶ مورد دیگر

Professor Michael Bruenig is the Head of School for the School of Electrical Engineering and Computer Science (EECS) at The University of Queensland (UQ), a position he has held since 2016, with an interim role as Head of UQ’s Business School from 2019–2021. He previously led the CSIRO’s $140m National Research Flagship on Digital Productivity and co-founded Data61. With a PhD from RWTH Aachen University, his career spans automotive R&D in Germany, Silicon Valley, and Australia. He holds a Master’s and PhD in Science from RWTH Aachen University, Germany. His research focuses on strategic innovation, entrepreneurship, and translating research into industry impact through ventures like UQ Cyber, the National Industry 4.0 Energy Testlab, and the QLD Government AI Hub. He pioneered UQ’s Bachelor of Computer Science, Master of Cyber Security (based on US NICE Framework), and online Master of Business Analytics. His work spans robotics, sensor networks, and terahertz technology, with recent articles addressing lidar-based navigation, terahertz imaging, and cybersecurity. He advises startups and sits on boards, driving commercialization and spin-offs. Key initiatives include UQ Innovate and UQ Ventures, fostering student and faculty entrepreneurship. Labs and teams include collaborations on AI, Industry 4.0, and energy systems. His leadership emphasizes cross-disciplinary projects and curriculum innovation in tech and data fields.

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

Professor Lionel Tarassenko is a Professor of Engineering Science at the University of Oxford and the founding President of Reuben College. He leads research in biomedical engineering, focusing on machine learning and signal processing for healthcare applications, including patient monitoring systems and mobile health technologies. His work has revolutionized early warning systems for critically ill patients and self-management tools for chronic conditions. He directed the Institute of Biomedical Engineering (IBME) and expanded its research capacity, earning a Queen’s Anniversary Prize. Research Interests: Development of AI-driven patient monitoring systems Non-invasive vital sign detection using wearables and video Translation of medical technologies into clinical practice Machine learning for sleep staging and chronic disease management Key Achievements: Pioneer of FDA-approved critical care monitoring systems Recipient of CBE, Royal Academy of Engineering Silver Medal, and Martin Black Prize Founded four spin-out companies, including Oxehealth and Sensyne Health Authored 230+ journal papers, 210+ conference papers, and 32 patents Labs & Leadership: Director of the NIHR Oxford Biomedical Research Centre’s Technology and Digital Health theme R&D Director at Sensyne Health and Oxford University Innovation Former Head of the Department of Engineering Science (2014–2019)

• Machine Learning
• Computer Vision
• Multimodal GenAI
• +۳ مورد دیگر

Xiatian Zhu is a Senior Lecturer at the University of Surrey's Faculty of Engineering and Physical Sciences, affiliated with the Surrey Institute for People-Centred AI and the Centre for Vision, Speech and Signal Processing (CVSSP). He holds a PhD from London and is a Fellow of the Higher Education Academy (FHEA). His research focuses on scalable machine learning, including self-supervised learning, multimodal GenAI, and computer vision applications for autonomous systems. Dr. Zhu's research interests span machine learning, computer vision, and multimodal generative AI, with emphasis on autonomous driving, 3D object detection, and domain adaptation. His work integrates vision-language models for open-set recognition and leverages novel neural architectures for real-time applications. His publications demonstrate a strong focus on autonomous driving perception, 3D generative modeling, and domain-agnostic AI. Key trends include multi-sensor fusion, self-supervised representation learning, and computationally efficient transformers. Recent works also explore audio-visual synthesis and adversarial robustness. Awards: 2016 Sullivan Doctoral Thesis Prize (Best UK Computer Vision Doctoral Thesis) He leads the X-Up Lab and collaborates extensively on grants related to AI safety, efficient 3D generation, and multimodal systems. His team focuses on real-world applications in intelligent transportation and human-centred AI.

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.