جست‌وجوی استاد و پژوهشگر

• Applied Artificial Intelligence
• Machine Learning
• Computer Vision
• +۱۳ مورد دیگر

Christos Tachtatzis is a Professor in Applied Artificial Intelligence in the Department of Electronic and Electrical Engineering at the University of Strathclyde. He rejoined the university in 2011, was awarded a Chancellor’s Fellow in 2016, promoted to Senior Lecturer in 2018, Reader in 2021, and Professor in 2024. He leads key strategic initiatives including the Measurement, Digital and Enabling Technologies (MDET) Strategic Theme, co-directs the Laboratory for Innovation in Autism, and serves as Strathclyde lead for the UKRI AI CDT SUSTAIN. He is also a member of the HealthTech Cluster and advises The Data Lab and the Scottish Government on AI applications in agriculture and natural resources. Research Interests: His research spans applied AI with focus on computer vision, multimodal learning, domain adaptation, and explainability. These are applied to sustainable agri-food systems (livestock and arable), digital health, advanced manufacturing, and cybersecurity. His technical expertise includes deep learning, time series analysis, anomaly detection, remote sensing, hyperspectral imaging, and edge/cloud computing analytics. Recent Research Trends: His recent publications reflect a strong trend toward interdisciplinary AI applications, including environmental monitoring via satellite imagery inpainting, urban CO2 emission modeling, synthetic data generation for power grids, infant behavioral analysis, and precision livestock farming using monocular depth estimation. These works highlight his focus on real-world, data-driven solutions across environmental, health, and industrial domains. Scientific Awards: Innovate UK KTP Engineering Excellence Award (2021) Finalist, Herald Higher Education Awards – Outstanding Business Engagement (2022) Strathclyde Team Medal for Innovation in Autism (2018) SIN 2014 Best Paper Award Advising and Grants: He is actively involved in supervising research and leading externally funded projects from UKRI, InnovateUK, and H2020. He is Principal Investigator on multiple grants including Deep Learning for Woodland Soil Biodiversity, FLORA-SAGE (federated learning in agriculture), and the UKRI AI CDT SUSTAIN. He is a co-investigator on projects in digital dairy, infant interaction, and species assessment. He welcomes PhD students and regularly advertises opportunities through SUSTAIN and his professional networks. Labs and Teams: He co-directs the Laboratory for Innovation in Autism and leads the MDET Strategic Theme. He is embedded in the SUSTAIN CDT and collaborates extensively with the HealthTech Cluster, contributing to interdisciplinary research at the intersection of AI, engineering, and societal challenges.

Heather Christine Lentمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Machine Translation
• Low-resource Languages
• +۳ مورد دیگر

Heather Christine Lent is a Researcher in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. Her work focuses on advancing natural language processing (NLP) for low-resource and marginalized languages, particularly Creoles, and addressing security challenges in multilingual systems. She leads the "Multilingual Modelling for Resource-Poor Languages" project (2022–2025), funded by Carlsbergfondet and Google. Her research integrates computational linguistics with ethical considerations, emphasizing equitable technology access. Notable contributions include the CreoleVal benchmark for multilingual evaluation and studies on NLP security in multilingual contexts. She collaborates internationally, including with institutions like George Mason University, and engages in community-driven dataset creation. Publications highlight themes like adversarial attacks on multilingual models, data quality audits, and cross-lingual transfer learning. She actively participates in workshops and tutorials aimed at fostering inclusive NLP practices, such as the "Connecting Ideas in Lower-Resource Scenarios" initiative.

Anastasija Collenمشاهده پروفایل

مدرس

• Cybersecurity
• IoT Security
• Automotive Systems Cybersecurity
• +۳ مورد دیگر

Anastasija Collen is a Lecturer at the Research Institute for Statistics and Information Science at the University of Geneva . She holds a Ph.D. from the same institution and specializes in cybersecurity, IoT security, and automotive systems. Her work focuses on developing secure architectures, cryptographic protocols, and regulatory frameworks for connected and automated vehicles. Her research spans Automotive cybersecurity standards and certifications Trust modeling in distributed networks Blockchain applications for IoT security Human-centric risk visualization Data privacy compliance (GDPR) Recent publications emphasize secure automotive systems , including trusted platform modules (TPM), lightweight cryptographic protocols (LOKI-2), and certification roadmaps. She also explores IoT security through frameworks like anomaly-based intrusion detection and blockchain-enhanced defense mechanisms. Collen’s work has been presented at conferences such as IEEE ICCP , ARES , and IEEE EuroS&PW , with over 40 peer-reviewed articles since 2018. She maintains an active presence on ResearchGate and Google Scholar .

• 6G Communications
• Software Defined Networking
• Machine Learning
• +۵ مورد دیگر

Matthew Robinson is a Research Fellow at the University of Hertfordshire within the Department of Engineering and Technology, School of Physics, Engineering & Computer Science. His work focuses on next-generation communication systems and security applications, primarily through European and commercially funded projects including those with the European Space Agency (ESA), Horizon 2020, and industry partners like Global Invacom and Meazon. Education: MEng in Digital Communications and Electronics, University of Hertfordshire (2010-2014) Research Interests: Robinson specializes in 6G communications infrastructure with emphases on Software Defined Networking (SDN) , Machine Learning for sensor systems, Software Defined Radio (SDR) , and Satellite Communication for video broadcasting. His fingerprint analysis reveals strong activity in Connected Vehicles (80%), Software Defined Networking (80%), Machine Learning (50%), and Heterogeneous Transport Networks (40%). Projects demonstrate practical applications in energy disaggregation, vehicle sensor reliability, and secure smart metering. Research Trends: Recent publications (2021-2025) show a strategic pivot from video broadcasting systems toward cybersecurity applications for connected infrastructure. His work increasingly integrates unsupervised machine learning with hardware-software co-design, particularly for real-time threat detection in smart grids and autonomous vehicles, while maintaining satellite communication expertise. Awards: GB Patent GB2612705A: "Improvements to video data distribution networks" Projects & Grants: Robinson has contributed to four major initiatives: (1) SAT>IP WiFi Live Mobile TV (ESA-funded, with BBC R&D/Global Invacom), (2) EDIoT (H2020-funded, with Meazon), (3) AutoTrust (ESA-funded, with RL Automotive/Surrey), and (4) CPU/GPU Satellite Modem (ESA-funded). These projects secured substantial European funding and yielded patents, conference papers, and deployable systems for video broadcasting, energy monitoring, and vehicle sensor networks. Laboratory Affiliation: Works within the Networks and Security Research Centre, leveraging facilities for Software Defined Radio experimentation, satellite communication testing, and machine learning validation for IoT systems.

• Hardware Security
• Internet of Things
• Embedded Systems
• +۵ مورد دیگر

Thibaut Vandervelden is a Researcher at Vrije Universiteit Brussel's Faculty of Engineering, Department of Electronics and Informatics. His work focuses on hardware security, embedded systems, and cryptographic implementations for resource-constrained environments. With an h-index of 4, he has produced significant research output since 2019, primarily in Sensors, IEEE Access, and Future Generation Computer Systems. His research interests center on practical security implementations for IoT devices and embedded systems, with particular expertise in Rust programming for secure embedded development, cryptographic protocol optimization, and network security for low-power wireless networks. His work bridges theoretical security concepts with practical implementation challenges in constrained environments. Analysis of his recent publications reveals a strong trend toward secure embedded systems development, with increasing focus on Rust programming language applications, zero-knowledge proofs for privacy-preserving systems, and optimization of cryptographic operations for IoT devices. His research demonstrates consistent collaboration with colleagues including Ruben De Smet, An Braeken, and Kris Steenhaut. Scientific recognition includes: IACR RWC'21 Cryptohackathon on Functional Encryption: 2nd prize (2021) Vandervelden actively participates in research community building through organizing workshops like RustIEC and presenting at events focused on embedded systems security. His work has been referenced in Wikipedia pages and garnered attention across academic and professional platforms, with media contributions on cybersecurity research in Flanders. He leads and participates in research teams focused on wireless community development and the Internet of Batteryless Things, contributing to both theoretical frameworks and practical implementations in hardware security.

Daniele Antonioliمشاهده پروفایل

استادیار

• Applied System Security
• Privacy
• Wireless Communication Security
• +۱۳ مورد دیگر

Daniele Antonioli is an Assistant Professor at EURECOM, affiliated with the Software & System Security (S3) group. His research spans applied system security and privacy, focusing on wireless communication (Bluetooth, Wi-Fi), embedded and mobile systems (e.g., smartphones, cars, fitness trackers), and cyber-physical systems like industrial control systems. His research interests include Applied System Security , Privacy , Wireless Communication Security , Embedded Systems Security , Automotive Security , IoT Security , Applied Cryptography , and Threat Modeling . His work frequently targets real-world protocols and proprietary systems, uncovering critical vulnerabilities in Bluetooth (e.g., KNOB, BIAS, BLURtooth), FIDO2 (CTRAPS), and IoT ecosystems (BreakMi, E-Spoofer). Recent publications demonstrate a strong trend in automotive and IoT security , protocol-level attacks , and privacy-preserving technologies , with work published in top venues like IEEE S&P, ACM CCS, NDSS, and USENIX Security. He has contributed to the DP3T/GAEN contact tracing framework, now used in Android and iOS. Mille E Una Lode Award CLUSIT Award'22 He has advised multiple PhD and Master’s students and received grants from the German Research Foundation (DFG). He teaches courses such as Mobile System Security and a Bluetooth-focused course at EURECOM. He has also been active in the research community, serving as Associate Editor for ACM TOPS and co-chairing NDSS'26 Artifact Evaluation. He leads or contributes to research projects including the AttackDefense Framework (ADF) , ENCOPIA , and ORSHIN , focusing on threat modeling, privacy assurance, and resilient IoT systems.

Gunnar Teegeمشاهده پروفایل

استاد

• Formal Verification
• Virtualization
• Distributed Systems
• +۷ مورد دیگر

Prof. Dr. Gunnar Teege is a faculty member at the Institute for Computer Engineering within the Faculty of Computer Science at Bundeswehr University Munich. His career spans over two decades, focusing on distributed systems, virtualization, and formal verification of operating system components. He has led numerous research projects, including collaborations with institutions like the Runder Tisch GIS eV and the Bavarian State Office for Surveying and Geoinformation. Education: Diploma in Computer Science from Technical University of Munich (1985), Doctorate in Computer Science from TUM (1991) Affiliations: Member of GI (Gesellschaft für Informatik) and ACM (Association for Computing Machinery) Research interests include formal verification of software components for secure systems, virtualization frameworks, distributed systems, military communications, geospatial data interoperability via OpenGIS standards, computer-supported cooperative work (CSCW), and E-Learning platforms. His work often bridges theoretical and applied domains, particularly in defense IT systems and education technology. He has contributed to projects like Targeteam (XML-based adaptable teaching materials), HoBIT (high-security embedded systems), and MiKscHA (microkernel applications for high-security environments). His publications emphasize modularity, tailorability, and security in software design. Teaching activities at Bundeswehr University Munich include courses on operating systems, distributed systems, virtualization, and cybersecurity. Previously, he taught problem-based learning in computer science at TU Munich and developed virtual lectures for the Virtual University of Bavaria (vhb) from 2001–2005.

• Computer Science
• Sensor Networks
• Robotics
• +۵ مورد دیگر

Ákos Lédeczi is a prolific researcher with significant contributions to computer science, sensor networks, robotics, and K-12 education. His work spans hardware-software co-design, wireless localization, and accessible programming environments. Co-developed NetsBlox and DeepForge for novice-friendly distributed computing. Created PinPtr high-precision GPS cloud service and inTrack mobile node tracking systems. Pioneered Browser-based robotics simulation and smartphone IoT education for K-12 students. Explored RF interferometry , Doppler shift tracking , and acoustic shooter localization in urban environments. Contributed to medical capsule robot design and security co-design for embedded systems. His recent focus includes project-based AI curricula for female high school students and collaborative visual programming environments . While specific university affiliations aren't detailed here, his collaborations with Péter Völgyesi, Miklós Maróti, and others highlight his role in advancing distributed and sensor network research.

• Biomedical Engineering
• Artificial Intelligence
• Machine Learning
• +۱۲ مورد دیگر

Associate Professor Rifai Chai is a faculty member in the Department of Biomedical Engineering at the School of Engineering, Swinburne University of Technology. He serves as the Academic Director (Partnerships), reflecting his leadership in academic-industry collaboration. His research focuses on the intersection of biomedical engineering and artificial intelligence, with applications in brain-computer interfaces, medical technologies, robotics, and embedded systems. University: Swinburne University of Technology School: School of Engineering Department: Biomedical Engineering Position: Associate Professor Email: rchai@swin.edu.au His educational and professional background includes over a decade of experience in product development in hardware, firmware, and software design in Indonesia and Australia from 2000 to 2011. His research interests are extensive and include: Artificial Intelligence and Machine Learning Brain-Computer Interfaces Medical Device Design Assistive Technology (e.g., smart wheelchairs, exoskeletons) Cognitive Fatigue and Workload Monitoring Back Pain Assessment and Rehabilitation Embedded Systems EEG and Physiological Signal Processing Rifai Chai's recent publications demonstrate a strong focus on AI-driven healthcare solutions. His work spans advanced computational intelligence in medical imaging (e.g., lung cancer and thyroid cancer detection), EEG-based systems for driver fatigue and emotion classification, rehabilitation technologies using exoskeletons, and cybersecurity in industrial IoT. He employs deep learning, hybrid models (e.g., MLP-BiLSTM), and advanced signal processing techniques to solve real-world biomedical problems. His research consistently targets practical, non-invasive, and real-time applications in clinical and industrial settings. His scientific contributions include numerous high-impact publications in journals such as IEEE Access, Sensors, and Medical & Biological Engineering & Computing, as well as book chapters with Elsevier and Springer. He has secured multiple research grants from industry partners and the Australian Research Council, supporting projects in breast electromagnetic scanning, solar-powered charging poles, and haptically-enabled motion simulation. Rifai Chai actively supervises PhD and Master’s students, with current HDR projects covering areas such as brain-computer interfaces for prosthetics, cognitive workload monitoring, distracted driving detection, and AI in medical imaging. He leads a multidisciplinary research team working at the forefront of biomedical innovation. His lab integrates AI, robotics, and physiological sensing to develop technologies that improve health outcomes and human performance.

• Cyber-Physical Systems
• Industry 4.0
• Building Automation
• +۴ مورد دیگر

Prof. Dr.-Ing. Dipl.-Wirt. Ing. Diethelm Bienhaus is a faculty member at the Faculty of Information Technology (MNI) at Technische Hochschule Mittelhessen . His academic focus spans Cyber-Physical Systems , Industry 4.0 , and Assistive Technologies , with long-term projects in building automation and age-appropriate assistance systems. Research Interests: Cybersecurity for industrial systems, robotics, embedded systems, and human-computer interaction. Committee Roles: Member of the MNI Department Council, Head of Curriculum Development for Computer Engineering, and Chair of multiple appointment committees. Funded Projects: Led initiatives in secure digitalized healthcare production, dendrochronological analysis automation, and industrial connectivity platforms. Email: diethelm.bienhaus@mni.thm.de

• Augmented Reality
• Virtual Reality
• Image Recognition
• +۲۱ مورد دیگر

Prof. Dr. Martin Przewloka is a faculty member at the Department of MND at Technische Hochschule Mittelhessen. His expertise spans technologies such as Augmented Reality, Virtual Reality, Image Recognition, Parallel Computing, and Industry 4.0, with a focus on applications in healthcare, logistics, and developing countries. Department of MND, Technische Hochschule Mittelhessen Email: martin.przewloka@mnd.thm.de His research interests include Mobile Applications , Real-time Systems , 3D Printing , and Internet of Things (IoT) . He explores how these technologies intersect with Industrial Process Management , Smart Grids , and Public Sector Innovations . Recent work highlights Generative AI in self-service retail and mobile health solutions for chronic disease management. Prof. Przewloka's publications reflect trends in Mobile Empowerment , Crowdsourcing , and In-Memory Computing . His 2024 articles address AI industrialization and gamification in healthcare, while earlier works (2013–2015) focus on disruptive mobile business models and SAP HANA's role in enterprise analytics.

Husam Al-Magsoosiمشاهده پروفایل

عضو هیئت علمی

• Hardware Security
• Physical Unclonable Functions (PUFs)
• Machine Learning in Security
• +۴ مورد دیگر

Husam Al-Magsoosi is an Assistant Research Fellow at the Budapest University of Technology and Economics , affiliated with the College of Engineering and the Department of Automation and Applied Informatics . His work focuses on hardware security, particularly in the development and analysis of Physical Unclonable Functions (PUFs) . Research Interests : Hardware Security, PUFs, FPGA Design, Machine Learning in Security, Embedded Systems, Wireless Sensor Networks. His recent publications highlight advancements in configurable ring oscillator PUFs, addressing security, performance optimization, and environmental factors like temperature sensitivity. He explores FPGA-based implementations and evaluates vulnerabilities to machine learning attacks. His work also extends to energy-efficient routing protocols for sensor networks and embedded system design. Scientific Awards : No specific awards mentioned in the provided data.

Paul Fortierمشاهده پروفایل

استاد

• Digital Communications
• Internet of Things
• Cyber-Physical Systems
• +۷ مورد دیگر

Paul Fortier is a Full Professor at the Department of Electrical Engineering and Computer Engineering , Université Laval, with a career spanning over three decades. His work focuses on Digital Communications , Internet of Things (IoT) , Cyber-Physical Systems , and Wireless Communication Systems , particularly exploring complexity-performance tradeoffs in hardware implementations. PhD in Electrical Engineering, Stanford University (1989) MSc in Statistics, Stanford University (1987) MSc and BSc in Electrical Engineering, Université Laval (1984, 1982) His research integrates Machine Learning with FPGA architectures for applications like real-time hand gesture recognition and hardware-in-the-loop simulation . Current projects include Advanced Digital Twin Development and Quantum-Resistant Communication Protocols under the Quebec Strategic Microsystems Group . Key contributions appear in IEEE Transactions on Vehicular Technology , IEEE Internet of Things Journal , and IEEE Transactions on VLSI Systems . He has secured major grants from Fonds de recherche du Québec and NSERC for initiatives like Hardware-in-the-Loop Simulators and Wireless Cybersecurity Research . Fellow, Engineering Institute of Canada Fellow, Canadian Academy of Engineering Senior Member, IEEE

Selma Tekirمشاهده پروفایل

دانشیار

• Natural Language Processing
• Text Mining
• Machine Learning
• +۵ مورد دیگر

Selma Tekir is an Associate Professor in the Computer Engineering Department at Izmir Institute of Technology. She completed her undergraduate studies in Computer Engineering at Ege University in 2001, followed by a master's degree from Izmir Institute of Technology in 2004, and earned her PhD from Ege University in 2010. In 2009, she worked as a visiting researcher at the University of Konstanz, Germany. Her educational background includes: B.Sc.: Computer Engineering, Ege University (2001) M.Sc.: Computer Engineering, Izmir Institute of Technology (2004) Ph.D.: Computer Engineering, Ege University (2010) Dr. Tekir's research spans multiple areas of natural language processing and computational linguistics, with a particular focus on Turkish language processing. Her work explores text mining, news analysis, information warfare, and the application of deep learning techniques to linguistic problems. She has made significant contributions to counterfactual detection in Turkish, multi-modal language models, and the integration of symbolic reasoning with neural approaches. Her research often bridges theoretical advances with practical applications, particularly in the Turkish language context which presents unique challenges as a morphologically rich agglutinative language. Analysis of her recent publications reveals a strong trend toward advancing natural language understanding systems for Turkish, developing methods for counterfactual detection, enhancing question answering systems with knowledge graphs, and applying graph neural networks to biological sequence analysis. Her work demonstrates growing interest in combining symbolic reasoning with neural approaches and exploring self-reflection capabilities in language agents. Dr. Tekir has received research funding for projects including: Consistency and Reliability Assessment in News Chains (TÜBİTAK ARDEB 3501) Application of Data Analysis and Visualization Techniques on Historical Sources (BAP) She teaches courses including CENG 381 - Stochastic Processes, CENG 613 - Scientific Research Methods in Computer Science, and SEDS 501 - Introduction to Data Science.

• Machine Learning
• Deep Learning
• Medical Informatics
• +۳ مورد دیگر

Nasmin Jiwani is a Research Scholar with 56 publications, an h-index of 20, and 921 total citations. Her academic work spans interdisciplinary applications of machine learning and deep learning techniques across medical diagnostics, cybersecurity, and IoT systems. Her research focuses on Medical Informatics where she develops AI-driven diagnostic tools for liver disease prediction, diabetic retinopathy classification, and acute lymphoblastic leukemia detection. In Cybersecurity , she pioneers image-based malware classification using deep learning architectures. Her IoT Systems research includes supply chain optimization and smart security applications. Recent work shows increasing emphasis on healthcare technology integration, particularly in cancer diagnostics and autism spectrum disorder analysis. Collaboration patterns indicate strong partnerships with Ketan Gupta (30 co-authored papers), J. Logeshwaran, and Neda Afreen across IEEE conferences and journals. Her publication trajectory demonstrates consistent output with 10+ papers annually since 2022, showing particular strength in translating theoretical machine learning models to practical healthcare and security applications.