جست‌وجوی استاد و پژوهشگر

Thomas G. Thomasمشاهده پروفایل

دانشیار

• Robotics
• Smart Grid Systems
• Hyperspectral Imaging
• +۴ مورد دیگر

Thomas G. Thomas is an Associate Professor in the Department of Electrical and Computer Engineering at the University of South Alabama , where he contributes to both undergraduate and graduate education in electrical and computer engineering disciplines. Education: Ph.D. Electrical Engineering, University of Alabama Huntsville (1997) M.S. Electrical Engineering, University of Alabama Birmingham (1987) B.S. Electrical Engineering, University of South Alabama (1984) B.S. Chemistry, University of South Alabama (1977) Research Interests: Dr. Thomas's work spans robotics , smart grid systems , hyperspectral imaging , neural networks , and cybersecurity for industrial control systems . His research often integrates advanced machine learning techniques with practical engineering applications, particularly in autonomous systems and educational technology. Publication Trends: His recent publications (2004–2024) demonstrate a strong focus on robotics , machine learning , and engineering education . Notable contributions include autonomous navigation systems, cybersecurity in SCADA/PLC networks, and innovative educational programs to enhance student retention in engineering. Teaching: He instructs courses such as Virtual Instrumentation , Programmable Logic Controllers , and Introduction to Robotics , fostering hands-on learning in electrical and computer engineering.

Muhammad Nouman Nafeesمشاهده پروفایل

مدرس

• Cyber-physical systems security
• Smart grid cybersecurity
• Industrial Internet of Things security
• +۵ مورد دیگر

Muhammad Nouman Nafees serves as a Lecturer in Cyber Security and Forensic Computing within the Faculty of Science and Engineering at Anglia Ruskin University (ARU), specializing in the School of Computing and Information Science. His research bridges cybersecurity and critical infrastructure protection with focus areas including cyber-physical systems security, smart grid resilience, and physics-informed machine learning applications for threat detection. His academic credentials include: PhD in Computer Science (near completion), Cardiff University BSc (Hons) in Computer Networks and Cyber Security, First-Class Honours, Northumbria University Nafees' research program integrates operational technology security with physics-informed artificial intelligence to address vulnerabilities in power systems. Key initiatives include developing moving target defense strategies for grid infrastructure and contextual anomaly detection models that incorporate physical system constraints. His work spans: Cyber-physical systems security Smart grid & industrial IoT protection Indicators of compromise analysis Operational technology resilience frameworks Physics-informed deep learning for stealthy attack identification Analysis of his 2020-2023 publications reveals consistent focus on power system security through machine learning integration, with 60% of works appearing in ACM/IEEE flagship conferences. His research trajectory shows increasing specialization in physics-informed models (33% of recent works) and moving target defense mechanisms (22%), demonstrating evolving methodologies for critical infrastructure protection. Scientific recognition includes: Multiple conference grants (ACM CCS, NDSS, ACSAC, IEEE S&P) Cardiff University full PhD scholarship Nafees actively mentors cybersecurity talent through PhD supervision in interdisciplinary security projects. His research is supported by institutional funding at ARU and conference travel grants from major security organizations. Current supervision focuses on operational technology security and physics-informed AI applications. As an active IEEE and ACM member, he contributes to the cybersecurity ecosystem through peer review and conference participation. His work suggests collaboration with power systems research groups focused on securing next-generation grid infrastructure through innovative defense-in-depth strategies.

Olena Syrotkinaمشاهده پروفایل

استاد

• SCADA Diagnostics
• Big Data Analytics
• Discrete Mathematics
• +۲ مورد دیگر

Dr. Olena Syrotkina is a Professor at the University of Windsor's School of Computer Science. She holds a Ph.D. in Mathematical Simulation from Ukrainian State University of Science and Technologies and an M.Sc. in Computer Science from Dnipro University of Technology. Her research focuses on SCADA diagnostics, big data analytics, discrete mathematics, and machine learning algorithms. Research Interests: Her work spans computational methods for large-scale data analysis, including machine learning applications in industrial systems and theoretical frameworks for data optimization. Primary areas include: SCADA system reliability and diagnostics Big data processing and resource optimization Machine learning-driven predictive modeling Research Trends: Her recent publications emphasize mathematical optimization in big data contexts, with themes like quantum computing applications, fault detection in complex systems, and e-commerce analytics. Over 80% of her 2020-2024 publications involve scalable algorithms for industrial data. Honors & Advising: No awards are listed. She currently mentors graduate students but no named advisees were specified. Laboratory & Teams: Leads research in computational methods at UWindsor, collaborating with industrial partners on SCADA and data infrastructure projects.

Jianying Zhouمشاهده پروفایل

استاد

• Applied Cryptography
• Network Security
• Cyber-Physical System Security
• +۵ مورد دیگر

Jianying Zhou serves as Professor and Centre Director at Singapore University of Technology and Design (SUTD), leading the iTrust Centre for Research in Cyber Security. He holds a PhD in Information Security from Royal Holloway, University of London and maintains active leadership roles including co-founding ACNS, chairing ACM AsiaCCS steering committee, and serving on Asiacrypt steering committee. PhD in Information Security, Royal Holloway, University of London His research spans applied cryptography, cyber-physical systems security, and mobile/wireless security with emphasis on industrial control systems, blockchain, and automotive networks. Recent work focuses on practical security solutions for critical infrastructure including maritime systems, smart grids, and IoT ecosystems, combining cryptographic innovation with real-world deployment challenges. Analysis of his 2025 publications reveals concentrated efforts in blockchain security (selfish mining, payment channels), industrial IoT protection (PLC authentication, CAN bus intrusion detection), and privacy-preserving machine learning (federated learning attacks/defenses). Maritime cybersecurity and automotive security represent rapidly growing application domains reflecting real-world critical infrastructure needs. Scientific recognition includes: ESORICS Outstanding Contribution Award (2020) ACM Distinguished Member status As iTrust Centre Director, Zhou oversees multi-institutional research teams developing security frameworks for critical infrastructure. While specific student advising details aren't public, his leadership in major conferences indicates extensive mentorship activities. His research program demonstrates strong industry collaboration focus with practical security implementations for maritime, healthcare, and industrial systems. iTrust operates as SUTD's flagship cybersecurity research center under Zhou's direction, bringing together interdisciplinary teams to address cyber-physical security challenges through testbed development, protocol design, and risk assessment frameworks for critical infrastructure protection.

Elena Sitnikovaمشاهده پروفایل

دانشیار

• Critical Infrastructure Protection
• Cyber-Physical Systems (CPS)
• Intrusion Detection Systems (IDS)
• +۳ مورد دیگر

Elena Sitnikova is an Associate Professor of Cybersecurity and Networking at Flinders University's College of Science and Engineering . As a Certified Secure Software Lifecycle Professional , she leads cutting-edge research in Critical Infrastructure protection, focusing on intrusion detection for SCADA systems and Industrial Internet of Things (IIoT). Her work explores system antifragility and AI-driven analysis of abnormal data in cyber-physical systems. Education : PhD in Computer Technology (Mathematical Modelling of Space Communication Systems), Bachelor of Electrical Engineering with Honours. Elena's research spans Cyber-Physical Systems Security , Critical Infrastructure Protection , and SCADA Systems Cybersecurity , with a focus on intrusion detection, antifragility, and AI applications. Her recent work includes datasets for IIoT intrusion detection and frameworks for cyberworthiness assessments. She has received numerous awards , including the Women in AI Cybersecurity Award , Senior Fellowship of the Higher Education Academy , and Australian Office for Learning and Teaching Citation award . Elena has secured over AU$1.5 million in research grants and supervised six PhD completions . Leadership : Academic Lead of the Australian Affiliate of Women in Cyber Security (WiCyS), Associate Editor for the Australian Journal of Multidisciplinary Engineering, and Chair/Editor for international conferences/journals. Teaching : Innovates in online cybersecurity education and research-led teaching, pioneering student engagement strategies.

• Reinforcement Learning
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Ann Nowe is a Professor at the Department of Electronics and Informatics (ETRO) at Vrije Universiteit Brussel. Her research focuses on reinforcement learning, multi-agent systems, robotics, and energy management. She leads the Federated labs AI and Robotics, emphasizing interdisciplinary collaboration in artificial intelligence and sustainable robotics. With a prolific publication record (555+ outputs) and 152 active projects, her work spans theoretical advancements and applied solutions in AI ethics, explainability, and energy systems. Her research interests include AI security, neuro-symbolic integration, and federated robotics. Notable contributions include frameworks for explainable AI in multi-agent environments and predictive maintenance algorithms for wind turbines. She has received multiple awards, including the ALA Best Paper Award (2018) and Best Demonstration Award (2019). Dr. Nowe actively participates in conferences and workshops on trustworthy AI, co-organizing events like the World Conference on eXplainable AI (2025). Her work integrates ethical considerations and human-centric design principles across robotics, energy systems, and collective decision-making.

• Network Security
• Cyber Security for Operational Technologies (OT)
• Industrial Control Systems (ICS)
• +۷ مورد دیگر

Dr. Kieran McLaughlin is a Reader at Queen's University Belfast, leading research in cyber security for operational technologies (OT), including smart grids and industrial control systems (ICS). He focuses on threat analysis, intrusion detection/prevention, and digital twins for incident response. His work addresses SCADA systems, ICS protocols (e.g., IEC61850), and cyber-physical resilience. He leads projects like XANDAR (H2020) and CAPRICA, and collaborates with UK's RITICS institute. He teaches Network Security (CSC3064) and actively publishes in top-tier venues. His research spans over 100 publications, with recent focus on digital twins, AI-driven malware detection, and secure IoT communications. Projects include EU-funded initiatives targeting critical infrastructure security and embedded system safety. He advises on cybersecurity for energy sectors and contributes to global standards like DO-326A for avionics systems. Key areas of innovation include deception platforms using digital twins, lightweight AI for IoT security, and blockchain-based predictive maintenance in vehicular networks. His work integrates machine learning for automated response and explores novel networking architectures (e.g., Named Data Networking) to enhance security.

Shadi Jaradatمشاهده پروفایل

پژوهشگر

• Data Analytics
• Deep Learning
• Natural Language Processing (NLP)
• +۳ مورد دیگر

Shadi Jaradat is a Researcher at Queensland University of Technology (QUT) in Australia, affiliated with the School of Civil & Environmental Engineering. He holds a PhD in Civil & Environmental Engineering and a Master’s degree in Computer Science from the University of Queensland. His research focuses on data analytics, deep learning, NLP, and traffic safety, particularly leveraging AI techniques to enhance transportation safety through crash narrative analysis and multimodal data fusion. Shadi’s PhD research explores Deep Natural Language Processing-based mining of crash narratives , aiming to uncover insights for improving traffic safety. His academic background bridges computer science and civil engineering, enabling interdisciplinary approaches to real-world challenges. Key research areas include crash severity analysis, cyberattack detection in industrial systems, and multimodal data applications for traffic safety. His recent publications emphasize AI-driven methodologies for traffic safety, such as text mining for crash patterns, GPT models for multimodal analysis, and vision transformers for infrastructure inspection. Shadi collaborates with the Centre for Data Science at QUT, advancing data-driven solutions in transportation and cybersecurity domains. While no scientific awards are listed, his work demonstrates impactful contributions to traffic safety and AI applications. Shadi actively engages in cross-cultural crash analysis and contributes to projects addressing urban mobility challenges through advanced analytics.

Joy Ezeمشاهده پروفایل

مدرس

• Machine Learning
• Internet of Things (IoT)
• Telecommunications
• +۳ مورد دیگر

Dr. Joy Eze is a Lecturer in Computer Science at the Department of Computing, Goldsmiths, University of London. She holds a PhD in Applied Computing from the University of Bedfordshire (2021). Her research focuses on interdisciplinary applications of machine learning, data science, and health technology, with notable contributions to IoT cybersecurity, smart supply chain management, and vehicular networks. Key publications include work on AI-driven temperature control for perishable foods, 5G/6G-enabled Internet of Vehicles, and vulnerability detection in industrial IoT systems. She has presented at international conferences such as DASA 2024 and ITED 2022.

Mark W. Pattonمشاهده پروفایل

مدرس ارشد

• Agent-based systems
• Cybersecurity
• Security informatics
• +۵ مورد دیگر

Mark W. Patton is a Senior Lecturer in the Department of Management Information Systems at the University of Arizona and serves as Program Administrator for the AZSecure Cybersecurity Program. His expertise spans cybersecurity, security informatics, and decision support systems. PhD in Management Information Systems (University of Arizona, 2009) MBA (University of Michigan, 1999) BS in Mining Engineering and Computer Science (Montana Tech of the University of Montana, 1990) His research focuses on cybersecurity and security informatics, particularly in automated deception identification, SCADA vulnerabilities, and enterprise security management. He has contributed to studies on Tor networks, IoT vulnerabilities, and AI-driven cybersecurity frameworks. Mark's publications highlight his work in cybersecurity analytics, network reconnaissance, and AI-based deception detection systems. His recent work addresses SCADA vulnerabilities, honeypot technologies, and dark web marketplaces. He is affiliated with the Artificial Intelligence Laboratory at the University of Arizona and has developed kiosk-based systems for deception detection and network security analysis.

• Machine Learning
• Offshore Engineering
• Wind Energy
• +۷ مورد دیگر

Arvind Keprate is a Professor at Oslo Metropolitan University and teaches at Kristiania University College . He holds a Ph.D. in Offshore Engineering (2017) from the University of Stavanger, an M.Sc. in Marine & Subsea Technology (2014), and a B.Tech in Mechanical Engineering (2007). He previously worked at NASA's Prognostics Center of Excellence. Research Focus: Prognostics and Health Management (PHM) of complex socio-ecological technical systems like wind farms, combining machine learning, probabilistic techniques, and digital twins. Leadership Roles: Leads the Mechanics, Mechatronics, and Materials Technology (M3T) Research Group and manages the GrønnMet Lab . Grants: Funded by the Research Council of Norway (RCN), Norwegian Directorate for Higher Education and Skills (HK-dir), Norwegian AI Research Consortium (NORA), and RegionaleForskningFond (RFF). Industrial Expertise: 10+ years in pipeline engineering, fatigue analysis, and asset integrity (Reliance Industries, DNV). Editorial Roles: Associate Editor for the International Journal of System Assurance Engineering and Management and Topic Editor for Frontiers in Energy Research.

• Cybersecurity
• Data Protection
• Network Security
• +۶ مورد دیگر

Dr. Jerry I. den Hartog is an Assistant Professor in the Security Group at the Department of Mathematics and Computer Science, Eindhoven University of Technology. His research focuses on data protection in collaborative systems, network situational awareness, intrusion detection, access control, trust management, and formal verification. He applies this work to critical infrastructure, industrial control systems, smart mobility, energy systems, and IoT. He joined TU/e in 2008 after completing his PhD and MSc at Vrije Universiteit Amsterdam, and has held research roles at CWI, TU/e, and the University of Twente. Academic Background: PhD and MSc in Formal Methods from Vrije Universiteit Amsterdam Research roles at CWI (Centrum Wiskunde & Informatica) and University of Twente Joined TU/e in 2008 to establish the Security Group Research Interests: Data protection mechanisms for collaborative systems Trust management and access control frameworks Intrusion detection in industrial and IoT systems Cybersecurity for critical infrastructure (e.g., power grids, healthcare networks) Projects and Grants: INTERSECT: NWA.1160.18.301 (Internet of Secure Things) SECREDAS GA 783119 (Security and Privacy in Data Fusion) Active projects in modular IoT architectures and cyber resilience Teaching: Courses include Automata and Formal Languages , Computer Networks and Security , and Verification of Security Protocols . Labs/Teams: Leads the Security Group within the Mathematics and Computer Science department, focusing on applied cybersecurity research with industry collaboration.

• Intelligent Systems
• Predictive Maintenance
• Condition Monitoring
• +۱۰ مورد دیگر

Dr Miguel Ángel Sanz Bobi is a Professor at the Institute for Research in Technology (IIT) of Comillas Pontifical University, Madrid, Spain. Since joining IIT in 1985 and becoming faculty on 1 September 1986, he has led over 60 research projects for utilities and industry and advised 20 PhD theses to completion, with four more ongoing. He currently holds the Endesa Chair for AI-driven maintenance. Education: PhD in Industrial Engineering, Universidad Politécnica de Madrid, 1992. Thesis: “Metodología de mantenimiento predictivo basada en análisis espectral y temporal de la historia de equipos industriales…” Research Interests: His work integrates artificial intelligence with power and industrial engineering . Key themes include: Condition monitoring and predictive maintenance of complex industrial assets (power plants, wind turbines, trains). AI techniques: expert systems, fuzzy logic, machine learning, reinforcement learning, generative adversarial networks. Digital twins for anomaly detection and prognosis. Reliability, risk assessment and asset management in power systems. Publication Trends: Recent publications (2020-2025) focus on deep reinforcement learning for microgrids, machine-learning-based health indicators for batteries and insulators, ensemble methods for gas-turbine diagnostics, and open-source asset-management toolkits. Earlier work covers neural networks, multi-agent systems, and probabilistic models for maintenance scheduling. Scientific Awards & Fellowships: No specific awards are listed in the supplied text. Grants & Projects: Dr Sanz-Bobi has coordinated or participated in more than 60 projects, including: Horizon 2020: REDREAM (€957837), ATTEST (€864298) Endesa Chair: 2023-2028 and 2026-2028 cycles on AI-driven maintenance EU/UIC: Guidelines for data-driven railway maintenance (2024-2027) Industry: Iberdrola, Gas y Electricidad Generación, SATE s.r.l., Verescence, Repsol, Ford, Robert Bosch, Abengoa, Alstom, Union Fenosa, Canal de Isabel II, etc. Laboratories & Teams: He heads the Intelligent Systems research area at IIT and leads the Endesa Chair laboratory, which develops AI solutions for predictive maintenance and asset management in power generation, transmission and distribution networks.

Adam Maricelمشاهده پروفایل

استاد

• Electrical Engineering
• Power Systems
• Power Electronics
• +۷ مورد دیگر

Prof. Adam Maricel is a Professor at the Department of Electrical Engineering, Faculty of Electrical Engineering, Technical University of Iasi. His research focuses on electrical equipment monitoring, power electronics applications (e.g., FACTS devices), thermal stress analysis, and railway electric traction systems. He specializes in infrared thermography, condition monitoring, and smart grid technologies. His work addresses challenges in circuit breaker diagnostics, renewable energy integration, and fault detection in power systems. He has pioneered methods for non-intrusive load monitoring, wireless communication in electrical equipment, and vibration analysis for mechanical health assessment. Recent publications (2023–2024) highlight innovations in BLDC motor optimization, SAW sensor networks, and biomedical applications of nanostructured devices. His research bridges electrical engineering with biomedical engineering through interdisciplinary projects like protein filtration for neurodegenerative disease treatment. No scientific awards are listed, though his extensive publication record reflects sustained contributions to the field. He coordinates research projects involving railway infrastructure, substation automation, and renewable energy systems integration.

• Manufacturing Systems
• Operations Research
• Optimization
• +۶ مورد دیگر

Eirinakis Pavlos is an Associate Professor at the Department of Industrial Management & Technology, University of Piraeus. His affiliations include the Maritime and Industrial Studies school. He specializes in analytical methods in industrial systems and optimization techniques. His research focuses on digital twins, reconfigurable manufacturing systems, and maritime logistics optimization. Pavlos' work integrates advanced technologies like machine learning and cyber-physical systems to enhance industrial processes and supply chain resilience. Research interests span manufacturing systems optimization, energy-efficient robotics, and emissions control in maritime industries. He explores algorithmic solutions for collaborative logistics and mathematical programming under uncertainty. His recent work emphasizes cognitive digital twins for production resilience and modular manufacturing architectures. Publications highlight contributions to digital twin applications, stochastic optimization, and maritime big data analytics. His articles often bridge theoretical optimization frameworks with practical industrial challenges, such as LPG production quality control and cargo loss prevention via AIS-enabled systems. Pavlose@unipi.gr is his official contact. His office is located at 503/Delig. No listed scientific awards or advising students are mentioned in the provided texts.