جست‌وجوی استاد و پژوهشگر

• Deep Learning
• Federated Learning
• Computer Vision
• +۷ مورد دیگر

Alexandre BENOIT is a Professor at Polytech Annecy-Chambéry, Université Savoie Mont-Blanc, and a permanent member of the LISTIC laboratory. His research focuses on deep learning, federated learning, computer vision, remote sensing, and explainable AI, with applications in astrophysics, environmental monitoring, and healthcare. He leads projects on glacier modeling, federated learning bias mitigation, and satellite image analysis. His teaching activities include courses on deep learning (TensorFlow/PyTorch), image processing (Matlab/OpenCV), and programming (C/C++/Python) at undergraduate and graduate levels. He has supervised over 10 PhD students and collaborates with industries like Total, Renault, and startups on AI integration. Research highlights include developing the GammaLearn framework for Cherenkov Telescope Array data analysis and bio-inspired retina models integrated into OpenCV. He co-organized major conferences such as CBMI 2012 and EUSFLAT 2011, and serves on editorial boards for IEEE Transactions on Image Processing and other journals. Current projects address federated learning fairness, glacier thickness estimation via deep learning, and oil slick detection using SAR imagery. His work emphasizes frugal models, physically informed AI, and ethical AI practices in collaborative environments.

Alexis Kirkeمشاهده پروفایل

عضو هیئت علمی

• Music Technology
• Computational Music
• Human-Computer Interaction
• +۳ مورد دیگر

Dr Alexis Kirke is a Senior Research Fellow in Computer Music at the School of Art, Design and Architecture (Faculty of Arts, Humanities and Business) at the University of Plymouth. As a composer-in-residence, he specializes in interdisciplinary research at the intersection of music, computing, and healthcare. His work includes developing adaptive music systems like RadioMe for dementia care, applying quantum computing to music composition, and exploring affective computing through brain-computer interfaces. Teaching roles include associate lecturer positions in modules such as Collaborative Practice (BA Sound and Music Production), Psychology (BA Music), and MRes Computer Music. He has supervised five PhD students as a second supervisor and served as an internal PhD examiner. His research focuses on algorithmic composition, music technology for healthcare, quantum computing applications in music, and multi-agent systems inspired by natural phenomena like humpback whale song evolution. Recent projects include the Plymouth Marine Institute collaboration and the Cloud Chamber performance involving real-time interaction with subatomic particles. Key contributions include innovative systems like RadioMe, which combines adaptive radio with reminder systems for dementia patients, and Q-Muse, a quantum computer music system. His work bridges computational creativity with human-centric applications, emphasizing ethical and accessible technology. Grants & Awards: No specific awards listed, but active in collaborative research projects Lab/Teams: Plymouth Marine Institute, Cloud Chamber Project

CHANG Ee Chienمشاهده پروفایل

دانشیار

• Information Security
• Cloud Security
• Adversarial Machine Learning
• +۶ مورد دیگر

CHANG Ee-Chien is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He serves as the Lead Principal Investigator of the National Cybersecurity R&D Laboratory (NCL) and previously served as Deputy Director of the Center for Technology, Robotics, Artificial Intelligence and the Law (TRAIL) from 2019 to 2022. He also chaired the Joint Academic Committee for the Bachelor of Computing in Information Security program from 2016 until April 2023. Dr. Chang received his Ph.D. in Computer Science from New York University in 1998, following his M.Sc. and B.Sc. in Mathematics from the National University of Singapore in 1993 and 1991 respectively. He completed postdoctoral work with DIMACS at Rutgers University and NEC Labs America. His research focuses on information security, multimedia security, and their intersection. His work spans from foundational research in cryptographic techniques for noisy data and image watermarking to contemporary challenges in data privacy, cloud security, and adversarial machine learning. Dr. Chang has published extensively in top-tier security and computer science venues including CCS, EUROCRYPT, USENIX Security, and ACM Multimedia. Analysis of his recent publications reveals a strong emphasis on practical security solutions using hardware features like Trusted Execution Environments, innovative approaches to watermarking using generative models, and addressing emerging threats in machine learning systems. His work bridges theoretical cryptography with real-world security challenges, particularly in cloud infrastructure and privacy-preserving technologies. Active leadership in National Cybersecurity R&D Laboratory Extensive service on program committees for major security conferences Consultancy roles with Huawei, i-Sprint, and Singapore Accreditation Council Media commentary on cybersecurity issues for major Singaporean outlets Dr. Chang has mentored numerous research fellows and students who have gone on to academic and industry positions worldwide. His educational contributions include developing cybersecurity curriculum and innovative teaching methods such as penetration testing using real-world university applications. He has taught core security courses including CS2107 Introduction to Information Security continuously since 2014.

• Artificial Intelligence
• Machine Vision
• Distributed Computing
• +۱۱ مورد دیگر

Dr. Dan Ionescu is a Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, Department of Electrical and Computer Engineering. He has been affiliated with the university since 1985, contributing extensively to teaching and research. His career includes visiting professorships at École Nationale Supérieure de Télécommunications (Paris) and Universitat Politècnica de Catalunya (Barcelona). He founded the Machine Intelligence Research Laboratory (1988) and the Network Computing and Control Research Laboratory (NCCT, 1999), which he currently directs. His research spans Artificial Intelligence, Machine Vision, Distributed Computing, Network Control, and Formal Methods. Notable contributions include methodologies in Expert Systems, Image Processing, Temporal Logic, and Network Management. His work has received industrial and governmental grants from CITO, Nortel, NSERC, and others. Recent research focuses on Web-based collaborative platforms (e.g., UC-IC, Watch-Together), Autonomic Computing, and AI applications in gesture control and 3D IR camera systems. Dr. Ionescu’s technical leadership includes pioneering the first distributed network management platform with industry partners and designing the NCIT*net 2 network architecture. His current interests include AI-driven solutions for cybersecurity, medical imaging, and IoT-enabled disaster response systems. He has advised numerous projects in AI ethics, quantum computing, and real-time control systems. His research trends emphasize interdisciplinary innovation, combining AI with healthcare (e.g., MRI analysis, deepfake detection), cybersecurity (GAN-based intrusion detection), and IoT (Salv AIoT platform). Collaborations with IBM CAS and Diatem Networks highlight his industry-academia integration. His legacy includes over 40 years of impactful contributions to computing and engineering education.

Raymond Chengمشاهده پروفایل

پژوهشگر

• Security
• Privacy
• Distributed Systems
• +۵ مورد دیگر

Raymond Cheng is a Researcher and blockchain lecturer with a focus on security, privacy, and distributed systems. He earned his Ph.D. in Computer Science from the University of Washington and holds degrees in Physics, Electrical Engineering, and Computer Science from MIT. His research spans blockchain scalability, privacy-preserving protocols, and decentralized infrastructure. Key projects include Kariba Labs , MASQ Foundation , and Open Source Observer , where he works on tools for public goods funding, secure smart contracts, and open-source analytics. Raymond's publications highlight advancements in blockchain-based databases (FalconDB), confidential smart contracts (Ekiden), and privacy-preserving messaging (Talek). His work often intersects with internet freedom and secure, scalable systems. He co-founded multiple organizations, including the Decentralization Institute and Public Goods Foundation , and has been featured in Forbes , Wired , and MIT Technology Review for his contributions to privacy and blockchain innovation.

Cunjian Chenمشاهده پروفایل

عضو هیئت علمی

• Computer Vision
• Deep Learning
• Medical Imaging
• +۴ مورد دیگر

Cunjian Chen is an Adjunct Lecturer at the Department of Data Science & AI, Monash University, and a Research Fellow at Monash University Suzhou. He holds a PhD in Computer Science from West Virginia University and serves as an Associate Editor for journals like Neural Processing Letters and IET Image Processing. His research focuses on computer vision, deep learning, adversarial machine learning, and cross-spectral biometric systems. Notable contributions include advancements in thermal-to-visible face recognition, medical image segmentation, and robust adversarial defense mechanisms. Dr. Chen has received the Outstanding Area Chairs award at ICME 2021. He actively contributes to academic governance roles, including Tutorial Chair for IJCB and Area Chair for ICME, ICIP, ICASSP, IJCNN, and FG. He is currently accepting PhD and research assistant students in computer vision and deep learning, emphasizing innovative applications in security and healthcare. His research collaborations span global institutions, with recent work addressing challenges in cross-spectral imaging, medical AI, and adversarial robustness. Professional activities include patent engagements with the Monash Suzhou Research Institute. For further details, visit his personal website or Google Scholar profile .

Lan Wangمشاهده پروفایل

استاد

• Internet Architecture
• Network Security
• Wireless Sensor Networks
• +۷ مورد دیگر

Lan Wang is a Professor in the Department of Computer Science at the University of Memphis, temporarily assigned as a Program Director at the NSF. She holds a PhD in Computer Science from UCLA (2004). Her research focuses on Internet architecture, network security, and wireless sensor networks, with major grants from NSF, NIST, and DoD, including a $15M NSF-funded 'Named Data Networking' project. She has served as Department Chair (2016-2023) and is an IEEE Senior Member. Dr. Wang's research emphasizes scalable, reliable, and secure Internet infrastructure. She has pioneered advancements in named-data networking (NDN), including protocols for adaptive forwarding, secure access control, and efficient traffic management. Her work extends to applications in smart cities, public safety, and healthcare data sharing. She has received prestigious awards including the Willard R. Sparks Eminent Faculty Award (2022) and Dunavant Professorship (2021). Her teaching spans courses like Networking and Information Assurance, Advanced Computer Networks, and Wireless and Mobile Computing. She actively promotes gender diversity in CS, co-organizing events like the 'Networking Networking Women' panel at SIGCOMM. Her grants total over $15M, including NSF’s NDN project and university-funded initiatives. She advises the Women in Computing student chapter and has served on over 50 conference committees.

• Complex Event Processing
• Healthcare Informatics
• Semantic Computing
• +۵ مورد دیگر

Fabio Persia is a Professor at the University of Naples Federico II with an extensive publication record spanning 15 years (2009-2025), demonstrating continuous academic engagement. His research portfolio spans multiple institutions through collaborations with over 70 co-authors, most notably Daniela D'Auria (43 publications), Mouzhi Ge (17), and Giovanni Pilato (14). Dr. Persia's research interests focus on the intersection of semantic computing, event processing, and practical applications. His work evolved from foundational contributions to multimedia recommender systems (2013) to developing the ISEQL interval-based surveillance event query language (2016), and most recently to healthcare AI applications. He has pioneered complex event processing frameworks for video surveillance, created multi-agent systems for epilepsy detection (PredictMed-epilepsy), and explored social sensing for personalized routing during the pandemic. His recent work increasingly integrates large language models with healthcare monitoring systems, reflecting current AI trends. Analysis of his publication trends since 2020 reveals a strong healthcare focus (65% of recent work), particularly in patient monitoring architectures, clinical decision support, and medical AI integration. His publications demonstrate consistent methodological rigor across domains, often combining semantic computing with real-time event processing to address practical challenges in healthcare and social computing contexts. Dr. Persia has served as guest editor for six special issues in the International Journal of Semantic Computing (2023-2025) covering Robotic Computing, Transdisciplinary AI, and Multimedia Computing, confirming his leadership in these research communities. His editorial roles complement his extensive publication record, which includes 25 journal articles and 76 conference papers. His collaborative research includes significant projects with Stefania Costantini on patient monitoring systems, with Mouzhi Ge on multimedia recommenders, and with Sven Helmer on interval joins and event detection. While specific grant information isn't detailed in publications, his sustained output suggests successful funding from multiple sources. His current research trajectory indicates growing emphasis on LLM integration in medical contexts and context-aware systems for public health applications.

• Natural Language Processing
• Automatic Text Summarization
• Sentence Generation
• +۵ مورد دیگر

Juan-Manuel Torres Moreno is an Associate Professor (Maître de Conférences HDR HC) at the University of Avignon (UAPV), where he conducts research in Natural Language Processing at the Laboratoire Informatique d'Avignon (LIA). His academic position includes the HDR (Habilitation à Diriger des Recherches), a post-doctoral qualification in France that enables supervision of PhD students. His primary research interests focus on Natural Language Processing, with particular emphasis on automatic text summarization, sentence generation, and phrase compression algorithms. His work spans both theoretical and applied aspects of NLP, incorporating machine learning techniques and artificial intelligence approaches. His research has significant applications in multilingual processing, text mining, and information extraction systems. Torres Moreno's publication record demonstrates a consistent trajectory in advancing text summarization techniques, with recent work exploring cross-lingual approaches, multimedia content processing, and deep learning applications. His research often bridges the gap between theoretical linguistic concepts and practical implementation, with publications spanning from fundamental NLP algorithms to applied systems for video summarization, speech processing, and multilingual document analysis. He actively collaborates with researchers across multiple institutions including École Polytechnique de Montréal (with 50 joint publications), Laboratoire Informatique d'Avignon (83 publications), and Universidad Nacional Autónoma de México. His work appears in reputable journals such as Computer Speech and Language, Data and Knowledge Engineering, and Pattern Recognition Letters. Within the Laboratoire Informatique d'Avignon, Torres Moreno contributes to the Language Processing research theme, working with colleagues on projects related to multilingual information access, opinion mining, and text analysis. His research group has participated in several evaluation campaigns including DEFT (Défi Fouille de Textes) challenges, focusing on information retrieval and sentiment analysis tasks.

• Virtual Reality
• Augmented Reality
• Mixed Reality
• +۷ مورد دیگر

Pascal Knierim is a researcher in the Department of Computer Science at Ludwig Maximilian University of Munich, Germany. He completed his PhD at the same institution in 2020 with a dissertation titled "Enhancing interaction in mixed reality: the impact of modalities and interaction techniques on the user experience in augmented and virtual reality." His research focuses on human-computer interaction, particularly in virtual and augmented reality environments, with an ORCID identifier 0000-0001-9578-9953. Knierim's research interests span virtual reality, augmented reality, mixed reality, ubiquitous computing, and extended reality systems. He has made significant contributions to understanding user interaction in immersive environments, privacy considerations in VR/AR, biometric identification using thermal imaging, and universal interaction frameworks. His work often combines technical innovation with user-centered design principles, resulting in numerous publications at top-tier venues including CHI, MUM, UbiComp, and IEEE Pervasive Computing. Recent work has explored content blocking in extended reality, social anxiety in VR proxemics, user awareness of privacy permissions, and framework development for ubiquitous research preservation. Knierim has collaborated extensively with researchers such as Thomas Kosch, Florian Alt, and Albrecht Schmidt, forming a productive research group within LMU Munich's computer science department. He has also contributed to the academic community through editorial roles for major conferences including Mensch und Computer 2023 and the 22nd International Conference on Mobile and Ubiquitous Multimedia (MUM 2023), demonstrating leadership within his research community. His research demonstrates a strong commitment to both theoretical advancement and practical applications of immersive technologies, with particular attention to user experience, privacy, and accessibility considerations.

• Cybersecurity
• Applied Cryptography
• Identity & Access Management
• +۴ مورد دیگر

Dr. Amna Qureshi is an Assistant Professor in Cybersecurity at the Department of Computer Science, University of Bradford. Previously, she served as a Senior Researcher at Universitat Oberta de Catalunya in Barcelona. Her research focuses on secure cryptographic protocols, distributed systems, IoT security, privacy preservation, and cyberbullying detection. She holds a Fellowship from the Higher Education Academy (FHEA) and received a 2018 grant from Spain’s INCIBE for cybersecurity research. Her teaching includes modules like Ethical Hacking (COS7029-B) and ISO27000 Framework (COS7030-B). Research highlights include over 25 peer-reviewed publications in systems security and privacy, with a focus on Zero Trust Architecture, blockchain integration in finance, and AI-driven cybersecurity solutions. She actively reviews for JCR-indexed journals and serves on conference program committees. Key contributions include frameworks for multi-layered DDoS defense in 5G networks, AI-based fake news detection, and Urdu-language cyberbullying identification. Her work bridges theoretical cryptography and practical applications in IoT, cloud computing, and financial systems. Grants: Advanced Cybersecurity Team Excellence Grant (INCIBE, 2018) Awards: FHEA Fellowship Her publications emphasize Zero Trust models, blockchain applications, and privacy-preserving techniques. Current research trends focus on integrating AI with cybersecurity to address emerging threats in distributed systems and social media.

Simon S.Y. Shimمشاهده پروفایل

استاد

• Internet Computing
• Database Management
• Machine Learning
• +۳ مورد دیگر

Simon S.Y. Shim is a Professor in the Computer Engineering Department at San Jose State University (SJSU). He holds a PhD in Computer Science from the University of Minnesota (1997). His research focuses on Internet Computing, Database Management, Machine Learning, and Information Retrieval. He has extensive industry experience as a Director at SAP Labs, working on in-memory database systems. Shim has served on the editorial board of IEEE Computer, including a 5-year tenure as Area Editor for Internet Computing. He has guest-edited special issues on High-Speed Security (2004), CAP Theorem (2012), and Next-Gen Memory (2013). He co-authored one technical book and holds multiple patents. His professional contributions include organizing IEEE Data Engineering workshops, IEEE Mobile Commerce, and IEEE Electronic Commerce events. Shim has published over 50 peer-reviewed articles and actively participates in academic service roles. His work emphasizes interdisciplinary applications of computer science in e-commerce, distributed systems, and multimedia technologies.

Ignacio De Castro Arribasمشاهده پروفایل

مدرس

• Social Computing
• Internet Economics
• Network Analysis
• +۵ مورد دیگر

Dr. Ignacio De Castro Arribas is a Lecturer in Data Analytics at the School of Electronic Engineering and Computer Science, Queen Mary University of London. His research focuses on social computing, internet economics, internet measurements, and web systems. He chairs the Research and Analysis of Standardisation Processes (RASP) Research Group at the IRTF. Teaching responsibilities include the postgraduate Cloud Computing module, covering virtualization technologies, service paradigms, and ethical/security considerations. He is affiliated with the Centre for Networks, Communications and Systems, contributing to interdisciplinary research on decentralized systems and online moderation. Research highlights include studies on Fediverse migrations, Mastodon social network dynamics, and content moderation in decentralized platforms. He has received grants from EPSRC for projects like 'Understanding Transitions when Tackling Online Harms' and 'Fediverse Observatory.' Grants: £339k EPSRC grant (2022–2025), £15k Fediverse Observatory grant (2024) Key Projects: Analysis of IPFS storage optimization, toxicity detection in decentralized web platforms, and editor curation in news ecosystems Affiliations: Centre for Networks, Communications and Systems

• Artificial Intelligence
• Computer Science
• Financial Modeling
• +۴ مورد دیگر

José Ignacio Olmeda Martos is a Professor at the Computer Science Department of the University of Alcalá, Spain. His research focuses on artificial intelligence, neural networks, financial modeling, and applications in tourism and e-learning. He leads the CSRG-UAH Cognitive Science Research Group and the AUDITAI Group for AI Software Development. He earned his Ph.D. in 1996 with a thesis on nonlinear financial models under Dr. Sergio Barba-Romero. His work bridges computational methods with real-world challenges in finance, tourism, and education. Research interests include predictive analytics, algorithmic optimization (genetic algorithms, SOMs), and accessibility in digital systems. His contributions span hybrid models in credit scoring, volatility forecasting using neural networks, and e-commerce adoption analysis. Over 25 years, he has published extensively on topics like tourism demand prediction (PLAZA project), web accessibility metrics, and financial market predictability. Key projects include developing internet-based tourism reservation systems for Castilla-La Mancha and applying wavelet filtering in financial time series. His research emphasizes interdisciplinary applications, integrating AI with tourism management and financial engineering. Education: Ph.D., Universidad de Alcalá, 1996 Publications reflect his expertise in computational finance, tourism technology, and machine learning. Despite no listed awards, his work demonstrates sustained impact in both academic and applied domains. Advising and grant activities are not detailed in the provided texts.

• Web Science
• Scientific Workflow Systems
• Semantic Web
• +۳ مورد دیگر

David Newman serves as a Senior Enterprise Fellow within the Electronics and Computer Science department at the University of Southampton's Faculty of Engineering and Physical Sciences. His research focuses on the intersection of web science, scientific workflow systems, and semantic technologies, with particular emphasis on developing infrastructure for collaborative research environments. His primary research interests center around Web Science , Scientific Workflow Systems , and Research Objects , where he investigates how digital platforms can enhance scientific collaboration. Newman's work explores the social dimensions of scientific computing through projects like myExperiment, examining how researchers share and reuse computational workflows. His research bridges technical infrastructure development with social computing aspects of scholarly practice, contributing to the evolution of virtual research environments that support modern scientific collaboration across disciplines. Analysis of Newman's publication history reveals consistent contributions to the development of semantic platforms for scientific collaboration, particularly through the myExperiment project. His work spans from foundational research on scientific social objects (2011) to practical implementations like Erica the Rhino (2016) that demonstrate real-world applications of workflow systems. The publications show progression from theoretical frameworks for research objects toward applied implementations in digital art and news enrichment, reflecting both technical depth and interdisciplinary reach. While no specific scientific awards are documented in the available materials, Newman's contributions to the myExperiment platform represent significant impact in the research infrastructure community. His work has helped shape how scientists share and discover computational workflows, contributing to more efficient and collaborative research practices across multiple disciplines. As a Senior Enterprise Fellow, Newman contributes to the university's research ecosystem through development of digital research infrastructure rather than traditional student supervision. His work focuses on creating platforms that support researcher collaboration at scale, with implications for how scientific communities organize and share knowledge in the digital age. The myExperiment platform, in particular, has served as an important testbed for concepts now mainstream in research computing. Newman's research is closely associated with the Web & Internet Science group at Southampton, where he has contributed to the development of semantic technologies for research communities. His work represents an important strand of the university's leadership in web science and digital research infrastructure, connecting technical innovation with practical applications for scholarly communication and collaboration.