جست‌وجوی استاد و پژوهشگر

Thabet Kacemمشاهده پروفایل

دانشیار

• AI for Cybersecurity
• Intelligent Transportation Systems
• Trustworthy AI
• +۳ مورد دیگر

Thabet Kacem is a Tenured Associate Professor of Computer Science at the University of the District of Columbia (UDC), affiliated with the School of Engineering and Applied Sciences (SEAS). His research focuses on AI-driven cybersecurity solutions, intelligent transportation systems, and trustworthy AI frameworks. He has led significant projects such as the HBCU-RISE initiative for climate change impact analysis and the HBCU-UP project for cybersecurity workforce development. His research interests include AI for cybersecurity (e.g., mobile threat detection, healthcare IoT security), resilient smart cities, and blockchain-based frameworks for secure communication in vehicular and aerospace systems. Notable contributions include the ADS-Bsec framework for aviation security and VANET-Sec for vehicular networks. Recent work highlights include the utilization of transformer neural networks for mobile threat detection (2025), systematic reviews of multi-task learning in cybersecurity (2024), and blockchain applications for UAV communication security (2023). His publications span IEEE journals and conferences, addressing topics like intrusion detection systems, game theory in cyber-physical systems, and CNN-based UAV detection. Awarded Best Presentation at ICIIT 2023 and Best Cyber Security Paper at ICNS 2015, Kacem is an IEEE Senior Member and actively contributes to academic service roles, including leadership at ISNCC’24 and UDC’s ABET accreditation committee.

Collin Capanoمشاهده پروفایل

عضو هیئت علمی

• Gravitational Wave Astronomy
• Black Hole Physics
• Neutron Star Dynamics
• +۳ مورد دیگر

Collin Capano is a Research Associate Professor in the Physics department at Syracuse University. He holds a Ph.D. and B.S. in Physics from Syracuse University (2011 and 2005 respectively). His research focuses on gravitational wave astronomy, black hole spectroscopy, and computational methods for analyzing astrophysical data. He has led projects involving black hole ringdown analysis, neutron star equation of state studies, and multimessenger astronomy. Dr. Capano has secured significant grants including a National Science Foundation award for developing black hole spectroscopy methods and desktop cluster infrastructure (2024-2026) and a Defense University Instrumentation Initiative grant (2022). He currently directs the Open Source Project Office at Syracuse University's Research Computing division. His work has been published in top journals like Physical Review D , Nature Astronomy , and The Astrophysical Journal . Notable contributions include testing general relativity with GW170817 observations and constraining neutron star radii through combined gravitational-wave and electromagnetic data analysis. Awarded the Max-Planck-Gesellschaft performance bonus (2020), he serves as a reviewer for leading journals including Physical Review Letters , Monthly Notices of the Royal Astronomical Society , and Classical and Quantum Gravity . He actively participates in international workshops and conferences, presenting on topics ranging from black hole spectroscopy to the future of gravitational wave astronomy.

Aphrodite Galataمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Human-Robot Interaction
• +۳ مورد دیگر

Aphrodite Galata is a Lecturer in Advanced Interfaces at the Human Computer Systems department. She holds a Doctor of Philosophy from the University of Leeds (2001) in Learning Variable Length Markov Models of Behaviour. Her research focuses on Computer Vision and Machine Learning, with special emphasis on sign language processing, human-robot interaction, and embodied learning systems. She has contributed to advancements in artificial agents learning sign language through imitation, phonological property recognition, and dataset development for American Sign Language analysis. Her research interests also span robotics, 3D pose estimation, and real-time tracking systems. Key topics in her work include gesture analysis, multimodal communication, and neural network architectures for feature representation. She has published widely in top-tier conferences including IEEE ICASSP and ACL, with notable contributions to datasets like WLASL-LEX. Notable projects include developing algorithms for hand tracking from monocular RGB data and exploring anisotropic cost functions for facial landmark detection. She has collaborated with researchers like Angelo Cangelosi on embodied sign language systems. Her work bridges computer vision techniques with cognitive modeling to enhance human-agent communication systems. Her research is aligned with the Digital Futures research beacon, emphasizing innovation in human-computer systems. While no specific awards are noted, her work has garnered significant readership on platforms like Mendeley, reflecting its academic impact. She has supervised 8 research works, contributing to the training of future scholars in advanced interface technologies.

• Artificial Intelligence
• Computer Vision
• Medical Imaging
• +۵ مورد دیگر

Shujun Zhang is an active researcher with extensive contributions in computer vision, AI, and medical imaging. With publications spanning from 1992 to 2025, Zhang has maintained continuous research productivity for over three decades. Primary research domains include artificial intelligence applications in healthcare, computer vision systems, and biomedical signal processing. Zhang's research focuses on developing advanced deep learning architectures for medical applications including lesion segmentation in 3D imaging, brain network analysis, and medical report generation. Additional significant work includes sign language recognition systems using multimodal learning and attention mechanisms, remote sensing image analysis, and fundamental computer vision techniques for 3D object recognition. Recent publications (2023-2025) show a strong trend toward medical AI applications and human-centered AI systems. Earlier foundational work established expertise in 3D vision systems and object recognition. The research evolution demonstrates a consistent pattern of applying cutting-edge computer vision and deep learning techniques to solve practical problems in healthcare and human-computer interaction.

• Computer Vision
• Computational Imaging
• Non-Line-of-Sight Imaging
• +۷ مورد دیگر

Suren Jayasuriya is a highly active faculty-level researcher in computer vision and computational imaging, with 79 refereed publications (2014-2025) in leading venues such as CVPR, ICCV, ECCV, NeurIPS, IEEE TPAMI and ACM TOG. His work integrates physics-based models with modern machine learning to tackle problems like non-line-of-sight imaging, atmospheric turbulence removal, neural 3D reconstruction, and speech enhancement, while also advancing STEM education through AI. Education & Affiliation: No explicit institutional details are contained in the supplied DBLP extract; however, the sustained publication record and extensive student mentoring indicate a tenured or tenure-track professorship within an engineering or computing department. Research Interests: Jayasuriya’s interests sit at the intersection of computer vision, computational photography, and physics-based machine learning . He develops algorithms that exploit optical and acoustic phenomena for tasks such as seeing around corners, correcting atmospheric distortion, and reconstructing 3D scenes from sparse sonar or radar data. Additional threads include energy-efficient tracking, robust speech processing, and AI-supported pedagogical innovation. Publication Trends: Across the 15 most recent works (2022-2025) his papers exhibit a clear thematic arc: neural representations for multimodal fusion (camera-sonar, radar-vision), unsupervised video restoration under atmospheric turbulence, attention-driven non-line-of-sight tracking, and evaluation of large multimodal models for perceptual reasoning. These contributions simultaneously advance core vision methodologies and demonstrate interdisciplinary applications spanning robotics, environmental monitoring, underwater perception, and education. Students & Mentoring: Jayasuriya has advised an active cohort of graduate researchers who appear repeatedly as co-authors, including Sreenithy Chandran, Ripon Kumar Saha, Albert W. Reed, Joshua D. Rego, Dehao Qin, Jianwei Zhang, Odrika Iqbal, Sameeksha Katoch, Md. Farhan Tasnim Oshim, and Shenbagaraj Kannapiran, among others. Scientific Awards: No awards are listed in the supplied extract; the awards field is left empty. Labs & Teams: While specific laboratory names are not disclosed, the collaborative scope—encompassing hardware-software co-design, field deployments, and educational outreach—suggests he leads or co-leads a research group with access to specialized imaging and robotics facilities.

• Artificial Intelligence
• Bioinformatics
• Data Mining
• +۳ مورد دیگر

Professor Phoebe Chen is a Professor and Chair of the Department of Computer Science and Information Technology at La Trobe University, Melbourne, Australia since 2010. Previously, she held roles as Associate Professor at Deakin University (2003–2010) and Senior Lecturer at Queensland University of Technology (1999–2003). She earned her BInfTech (First Class Honours) and PhD in Computer Science from the University of Queensland (2000). Her research focuses on interdisciplinary areas including bioinformatics, artificial intelligence, medical image analysis, deep learning, and data mining. Notable projects include the ARC Centre of Excellence in Bioinformatics and drug design using visualization and machine learning. She has secured over 30 research grants totaling ~$8M, including 13 prestigious ARC grants. Professor Chen chairs international conferences like the Asia-Pacific Bioinformatics Conference and ACM MM, and serves on editorial boards such as IEEE Transactions on Neural Networks and Current Bioinformatics. Her work spans topics like cancer recurrence prediction via multi-omics data, medical image analysis using neural networks, and AI-driven solutions for healthcare challenges. She has published over 300 peer-reviewed papers in top journals/conferences (e.g., Nature Machine Intelligence, IEEE Transactions).

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Radu-Casian Mihailescu is an Associate Professor at the School of Mathematical & Computer Sciences at Heriot-Watt University Dubai since 2022. Previously, he served as Associate Professor and Program Director of the Applied Data Science Master’s program at Malmö University in Sweden, while affiliated with the Internet of Things and People Research Center. He holds a PhD in Artificial Intelligence from King Juan Carlos University, Spain. His research focuses on advancing machine learning (ML), particularly in Artificial General Intelligence (AGI), explainable AI (XAI), and addressing bias in AI systems. Key areas include meta-learning, domain adaptation, and applications in healthcare, computer vision, and IoT. He has contributed to over 50 publications and led/coordinated over 10 national/international research projects as Principal Investigator. Dr. Mihailescu’s work intersects with UN Sustainable Development Goals, applying AI to improve healthcare and technology accessibility. His recent articles explore multimodal learning, healthcare optimization, and context-aware systems. He actively serves on program committees for academic conferences and journals.

Tomislav Kartalovمشاهده پروفایل

استادیار

• Image and Video Processing
• Mobile Platform Implementation
• Machine Learning
• +۵ مورد دیگر

Tomislav Kartalov is an Assistant Professor at the Department of Electronics, Faculty of Electrical Engineering and Information Technologies, University of St. Cyril and Methodius in Skopje, Republic of Macedonia. He has held this position since March 2015, following previous roles as a Teaching and Research Assistant from 2002 to 2015 and a brief period in technical maintenance at Macedonian Radio Television. Education includes a Ph.D. in Technical Sciences (2014) with a dissertation on Fusion of Digital Images and Videos for Visual Quality Improvement and Mobile Platform Implementation Possibilities , an M.Sc. in Electrical Engineering (2008) focusing on Visual Quality Enhancement of Digital Television Videos , and a B.Sc. in Electrical Engineering (2002) with a thesis on Software for Switching Power Supply Design (Flyback) . His research interests span image/video processing, mobile platform optimization, machine learning applications in education and healthcare, and sensor networks. Key areas include exposure fusion algorithms for mobile devices, real-time traffic sound detection systems, and multimodal information fusion for intelligent transportation systems. His publications from 2006 to 2023 reflect a strong focus on computational photography, embedded systems, and healthcare technology. Notable contributions include automated exposure fusion techniques for low-resource devices and AI-driven diagnostics using neuroimaging. No scientific awards or grants are explicitly mentioned in the provided texts. Kartalov’s advising history is not listed, but his teaching roles suggest involvement in graduate student mentorship. Laboratory affiliations are not detailed in the text.

Antonia Papandreou-Suppappolaمشاهده پروفایل

استاد

• Statistical Signal Processing
• Time-Varying Signal Processing
• Adaptive Waveform Design
• +۵ مورد دیگر

Antonia Papandreou-Suppappola is a Full Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), part of the Fulton Schools of Engineering. She joined ASU in 1999 and was promoted to full professor in 2008. Her research focuses on statistical and time-varying signal processing, with applications in adaptive waveform design, Bayesian nonparametric learning, radar, biomedical systems, and structural health monitoring. Her work has been funded by major agencies like AFOSR, DARPA, NSF, and NIH. Education: Ph.D., M.S., and B.S. in Electrical Engineering from the University of Rhode Island (1989–1995). Research interests include adaptive sensing, biomedical signal processing, and structural health monitoring. She has led numerous grants, including AFOSR-funded projects on Bayesian machine learning and multimodal sensing. Her service roles include leadership in the IEEE Signal Processing Society, including chair of the Women in Signal Processing committee and membership in the Board of Governors. Awards include IEEE Fellow (2008), 2020 IEEE Region 6 Award, and multiple teaching excellence recognitions (Top 5% in 2009, 2018–2023). Her articles span Bayesian methods, adaptive systems, and interdisciplinary applications like genomics and biomedical engineering. She advises students through courses in signal processing, including EEE 505 (Time-Frequency Signal Processing) and EEE 556 (Detection/Estimation Theory). Her lab focuses on advancing signal processing techniques for real-world challenges in health, defense, and communications.

• Generative AI
• Multimodality
• Computer Vision
• +۴ مورد دیگر

Dr. Quang-Vinh Dang is a Researcher at the University of Hamburg, affiliated with the Cluster of Excellence 'Understanding Written Artefacts' (UWA). He holds a Ph.D. in Artificial Intelligence Convergence and specializes in Generative AI, multimodality, and computer vision. His work focuses on applying AI techniques to analyze historical manuscripts, combining domain expertise with machine learning. Education: Ph.D. in Artificial Intelligence Convergence, Master’s and Bachelor’s degrees in Mechatronics (focusing on control systems and robotics). Research interests include generative AI, computer vision applications in cultural heritage preservation, and cross-disciplinary approaches blending robotics and AI. He has contributed to projects like the Visual Manuscript Analysis Lab and Document Image Binarization competitions. Awards include 3rd place (ICDAR 2019) and Best Paper Award (Korea Computer Congress 2019). His publications span scene text processing, multimodal emotion recognition, and document restoration techniques. Labs/Teams: Member of the Visual Manuscript Analysis Lab within UWA, contributing to similarity measurement of visual patterns in manuscripts.

Paras Mehtaمشاهده پروفایل

پژوهشگر

• Spatial Data Mining
• Geoinformatics
• Information Retrieval
• +۳ مورد دیگر

Dr. Paras Mehta is a Researcher at the Department of Databases and Information Systems, part of the Computer Science faculty at Freie Universität Berlin. His work focuses on spatial data mining, geoinformatics, and spatio-temporal analysis with applications in disaster response systems and social media analytics. He contributes to projects such as MoveSafe and CliniScale, addressing challenges in real-time data processing and location-based services. Research interests include geotagged post analysis, spatio-textual data retrieval, and distributed computing for large-scale spatial datasets. His recent work emphasizes continuous summarization of streaming data and hotspot detection using frameworks like Apache Spark. Notable projects involve developing systems like μTOP for trending topic detection in microblogs and LocXplore for urban region profiling. His publications span spatial keyword queries, trajectory aggregation, and disaster response frameworks. Paras Mehta holds a PhD and has contributed to both academic and applied research in database systems. Despite no listed awards or grants in the provided texts, his research portfolio demonstrates strong engagement with spatial computing challenges. He collaborates within the Databases and Information Systems group at Freie Universität Berlin, contributing to both theoretical advancements and practical software solutions.

Yukang Yanمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Mixed Reality
• Virtual/Augmented Reality
• +۳ مورد دیگر

Yukang Yan is an Assistant Professor in the Department of Computer Science at the University of Rochester, affiliated with the ROCHCI research group. Previously, he was a postdoctoral researcher in the Augmented Perception Lab at Carnegie Mellon University's Human-Computer Interaction Institute. He holds a PhD and Bachelor's degree from Tsinghua University. His research focuses on Human-Computer Interaction (HCI) and Mixed Reality, emphasizing three interconnected threads: computational modeling of user behavior and perception, development of adaptive interfaces for bidirectional human-computer communication, and exploration of behavioral enhancements in Mixed Reality. Key areas include virtual/augmented reality interfaces, gesture-based interaction, and accessibility solutions. His work has been recognized with awards such as the ACM CHI 2020 and 2023 Honorable Mention Awards, the IEEE VR Best Paper Nomination, and an Outstanding Doctoral Thesis award from Tsinghua University. His research bridges theoretical insights with practical applications in AR/VR systems, emphasizing user-centered design and accessibility. Yan's contributions span over 20 peer-reviewed publications at top venues like ACM CHI, ACM UIST, and IEEE VR. His recent projects include HandAvatar (HCI 2023) for non-humanoid avatar interaction and Color-to-Depth Mappings (UIST 2022) for VR depth perception enhancements.

Nelly Leligouمشاهده پروفایل

استاد

• Blockchain Technologies
• IoT Enabled Systems
• Artificial Intelligence
• +۷ مورد دیگر

Helen C. (Nelly) Leligou is a Professor and Vice-Director at the Department of Industrial Design and Production Engineering, University of West Attica. She also serves as a Visiting Professor at the Hellenic Open University. Her research focuses on Information and Communication Technologies, including blockchain, IoT systems, AI applications in maritime sectors, federated learning, and wireless sensor networks. She has contributed over 150 publications in journals and conferences and participated in EU-funded projects like ACTS, IST, ICT, and H2020. Dr. Leligou has evaluated proposals for EU and other European organizations, emphasizing cybersecurity, smart grids, and innovative educational tools. Her research interests span blockchain applications in networks and supply chains, IoT-enabled systems for energy efficiency and agri-food supply chains, and AI-driven solutions for maritime operational parameters. She teaches undergraduate courses in Internet technologies, cloud computing, and data security, along with postgraduate modules in artificial intelligence, computer architecture, and automation. Dr. Leligou's work integrates technology with societal challenges, such as sustainable energy and digital transformation. Her recent projects address blockchain in next-generation networks (NGNs), AI for 6G reliability, and serious games for renewable energy education. She advocates for transparent workplace compliance via blockchain platforms and explores federated learning security in distributed systems. Dr. Leligou's contributions bridge academia and industry, emphasizing real-world applications of cutting-edge technologies.

• Eye Movements
• Neural Development of the Visual System in Children and Infants
• Biomedical Engineering
• +۳ مورد دیگر

Aleksandar Miladinović is a Postdoctoral Researcher at the Biomedical and Clinical Engineering department under the Department of Engineering and Architecture at the University of Trieste. He is affiliated with the Institute for Maternal and Child Health - IRCCS "Burlo Garofolo" and contributes to developing objective tests for visual system assessment in pediatric populations. Doctorate in Industrial and Information Engineering (2021, University of Trieste) MSc in Cognitive Science (2017, MEi:CogSci program) BSc in Computer Science (2012, Belgrade) His research focuses on eye movement analysis, pediatric visual system development, and biomedical engineering applications for neurological diagnostics. Methodologies include machine learning, brain-computer interfaces (BCI), and medical imaging. Recent work trends emphasize ocular following response measurement for stereodeficiency evaluation, deep learning in OCT image analysis for retinal disease classification, and neurosymbolic AI for AMD diagnosis. He also explores real-time BCI optimization for post-stroke neurorehabilitation and EEG-based biomarkers for post-COVID-19 cognitive decline.

Amine Chellaliمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +۵ مورد دیگر

Amine Chellali is an Assistant Professor in the Department of Computer Engineering at Université Paris-Saclay's College of Sciences and Technologies. He co-heads the IRA2 (Interaction, Réalité Augmentée, Robotique Ambiante) research group at IBISC Laboratory, focusing on human-computer interaction, virtual/augmented reality, and medical simulation systems. PhD in Human-Computer Interaction (University of Nantes, 2009) Master of Science in Robotics (Ecole Centrale de Nantes, 2005) Bachelor in Computer Science (University of Constantine, 2003) His research spans multiple domains including: Medical simulation and surgical training systems 3D multimodal interaction design Collaborative virtual environments Haptic feedback and virtual reality Recent publications (2023-2025) show trends in AI-driven medical imaging analysis, VR-based collaboration metrics, and stress induction in virtual environments. His work combines computer vision (YOLOv8 for cancer detection), multimodal signal processing (gaze and speech in VR), and hybrid human-machine intelligence systems for clinical decision-making. Academic roles include: IEEE ISMAR 2025 International Programme Committee IEEE VR 2025 Awards Co-chair ACM ICMI 2023 programme committee Various editorial/review roles for HCI and VR journals He actively participates in international conferences and maintains affiliations with AFXR, AFIHM, ISPR, HFES, and robotics research groups.