جست‌وجوی استاد و پژوهشگر

Dalin Zhangمشاهده پروفایل

دانشیار

• EEG-based Emotion Recognition
• Time Series Forecasting
• Graph Neural Networks
• +۴ مورد دیگر

Dalin Zhang is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He holds a position within the Technical Faculty of IT and Design. His research focuses on machine learning, sensor data analysis, and their applications in healthcare, smart cities, and human-computer interaction. Key areas include EEG-based emotion recognition, time series forecasting, and graph neural networks. He leads the PREDICTION-ENABLED DYNAMIC SCHEDULING OF HOUSEHOLD ELECTRICITY USAGE project (2023–2024), demonstrating expertise in energy systems optimization. His work bridges theoretical advancements with practical applications, such as developing lightweight models for real-time traffic flow prediction and creating benchmarks for emotion recognition systems like LibEER. Research interests span: EEG signal processing for emotion and intention recognition Efficient time series analysis and forecasting Graph neural networks and adversarial defense mechanisms Data privacy in wearable sensor systems Recent publications highlight innovations like Pure-GNN (graph neural networks against adversarial attacks), LightCast (traffic forecasting), and Self-Supervised EEG Representation Learning. He advises two PhD students but their names are not listed in the provided texts. His work often emphasizes computational efficiency and real-world deployment across healthcare, smart infrastructure, and consumer electronics.

• Automation Technology
• Industrial Automation
• Energy Systems
• +۶ مورد دیگر

Professor Alexander Fay is a faculty member at Ruhr University Bochum, leading the Automation Engineering department within the Faculty of Electrical Engineering and Information Technology. His roles include Head of Department and a prominent researcher in automation technology, Industry 4.0, and cyber-physical systems. Educated at TU Braunschweig, he has extensive industry experience from ABB and academic leadership at HSU/UniBw Hamburg. His research focuses on automation systems, energy optimization, and autonomous robotics, with notable contributions in digital twin frameworks, agent-based control, and industrial data management. He has been honored with awards such as the MIT Review's 'Top 100 Young Innovators' and the Golden Badge of Honour from NAMUR. His work integrates interdisciplinary approaches, emphasizing practical applications in manufacturing, energy systems, and aerospace maintenance. Key collaborations include projects on modular electrolysis plants and U-space drone traffic management. His academic output spans over 70 publications in top-tier journals and conferences, reflecting expertise in automation theory and industry-relevant solutions.

Nuno Antunes Ribeiroمشاهده پروفایل

استادیار

• Transportation & Aviation
• Operations Research
• Optimization Methods
• +۳ مورد دیگر

Nuno Antunes Ribeiro is an Assistant Professor at Singapore University of Technology and Design (SUTD), part of the Engineering Systems and Design Pillar. He serves as Deputy Director of the Aviation Studies Institute (ASI), leading research collaborations with the Civil Aviation Authority of Singapore (CAAS) on projects like "Resilient Airspace Operations for Changi Airport" and "Airfield Management and Economics." He holds a Ph.D. in Transport Systems from the University of Coimbra (Portugal), with visiting research stints at MIT and Carnegie Mellon University. His expertise lies in applying operations research, optimization, and machine learning to aviation challenges. Key research areas include airport slot allocation, terminal airspace management, and predictive analytics for capacity planning. Teaching includes courses on Simulation Modeling, Metaheuristics Optimization, and Airport Systems Analysis. Awards include the AGIFORS Anna Valicek Medal (2018) and INFORMS Best Dissertation (2019). Current projects focus on resilient air traffic systems, AI-driven defect inspection, and multi-objective aircraft routing frameworks. Supervises PhD candidates and postdocs, with recent publications in Transportation Science and Journal of Air Transport Management. Editorial roles include INFORMS Aviation Applications Section Secretary and Transp. Res. Part C Board Member.

• Distributed Systems
• Mobile Networking
• Transportation Engineering
• +۵ مورد دیگر

Angelo Furno is an Associate Professor and Researcher at the LICIT Laboratory of Université Gustave Eiffel-ENTPE in Lyon, France. He holds a PhD in Computer Engineering from the University of Sannio, Italy. His academic roles include teaching courses on Intelligent Transportation Systems, Data Science, and Big Data at ENTPE. He is affiliated with INRIA’s AGORA team and the CITI-Lab at INSA Lyon. His research focuses on urban mobility analysis, transportation networks, mobile networking, and P2P systems. Key interests include leveraging multi-source traffic data for smart city planning, resilience engineering, and graph-based algorithms. Education: PhD in Computer Engineering (2014), University of Sannio, Italy MSc in Computer Engineering, University of Sannio, Italy Research Interests: Distributed systems, mobile data analytics, social network analysis, transportation engineering, and software engineering. Recent work emphasizes mobility pattern recognition, traffic demand estimation, and urban resilience strategies. Publications: Over 80 peer-reviewed articles in journals like Transportation Research Part C and conferences such as TRB. Recent trends focus on stochastic optimization for transport networks and deep learning for traffic prediction. Awards: Best Paper Award at IEEE Conference on Parallel, Distributed and Network-Based Processing (2013). Grants & Collaborations: Led projects like MyOpenGov and contributed to ANR ABCD. Collaborates with INRIA, INSA Lyon, and industry partners on urban mobility and data-driven solutions. Labs/Teams: LICIT Lab (transportation systems), INRIA AGORA (mobile networking), and CITI-Lab (communication networks).

Sherif Saadمشاهده پروفایل

استاد

• Cybersecurity
• Machine Learning
• Blockchain Technology
• +۵ مورد دیگر

Sherif Saad is a Professor in the Department of Computer Science at the University of Windsor. His research focuses on cybersecurity, machine learning, blockchain technology, and adversarial defense mechanisms. He has contributed to projects involving fake news detection, threat intelligence systems, and privacy-preserving frameworks in vehicular networks and decentralized systems. Recent research highlights include developing novel approaches to federated learning for privacy, ensemble defense strategies against adversarial attacks, and hybrid machine learning techniques for robotics and medical imaging. His work bridges theoretical advancements with practical applications in cyber-physical systems and autonomous vehicle technologies. Key areas of exploration include improving blockchain-based frameworks for supply chain logistics, analyzing adversarial robustness in NLP models, and enhancing anomaly detection in drone systems. His research often integrates evolutionary algorithms and genetic techniques for optimization problems in automated machine learning. While no formal awards are listed, his publications demonstrate significant contributions to cybersecurity, AI ethics, and interdisciplinary applications of machine learning. Collaborative efforts include international research with institutions like South Korea’s Ewha Womans University on autonomous vehicle comfort optimization.

Sheng-feng Qinمشاهده پروفایل

استاد

• Digital Design Technology
• Smart Product and Service Design
• Design Optimization
• +۶ مورد دیگر

Professor Sheng-feng Qin is a faculty member at the Northumbria School of Design, Northumbria University, since 2014. He holds the academic rank of Professor and serves as the Director of the MA Design Programme. His research focuses on digital design and manufacturing, smart technologies, and sustainable urbanization. He leads the Smart Design Lab, exploring AI-driven design methods and interdisciplinary approaches for smart products and services. Professor Qin's educational background includes a PhD in Craft, Design, and Technology (2000) and a BEng in Manufacturing Engineering (1983). He is a Fellow of the Higher Education Academy (FHEA) and a member of IEEE. His work has been recognized with the 2019 Newton Prize for collaborative research on sustainable urbanization. His research interests encompass digital design technology, smart cities, crowdsourcing in product development, and emotion-centric design. He has supervised multiple PhD students focusing on topics like digital twins for elderly care, smart transportation systems, and emotion regulation tools. Professor Qin collaborates internationally, including as a Visiting Professor at Southwest Jiaotong University and through joint labs with Chinese institutions. He also serves as Editor-in-Chief of the International Journal of Rapid Manufacturing and on editorial boards of other journals.

Alexander Gegovمشاهده پروفایل

دانشیار

• Computational Intelligence
• Artificial Intelligence
• Explainable Artificial Intelligence (xAI)
• +۱۳ مورد دیگر

Alexander Gegov is Associate Professor in Computational Intelligence at the School of Computing, Faculty of Technology, University of Portsmouth, and also serves as Visiting Professor in Control Theory at the English Faculty of Engineering, Technical University of Sofia. He is affiliated with the Centre of Excellence in Defence, Risk & Resilience and the Portsmouth AI and Data Science Centre. He has previously held leadership roles as Associate Dean for Research and has been actively involved in numerous international research collaborations. PhD in Cybernetics, Bulgarian Academy of Sciences DSc in Artificial Intelligence, Bulgarian Academy of Sciences Humboldt Guest Researcher, Germany EU Visiting Researcher, Delft University of Technology, Netherlands His research focuses on the development and application of artificial intelligence and machine learning methods—particularly fuzzy systems, neural networks, and evolutionary algorithms—for modeling and simulating complex systems characterized by nonlinearity, uncertainty, and high dimensionality. His work emphasizes explainability, transparency, and real-world feasibility in domains such as public security, financial forecasting, medical diagnosis, and environmental modeling. He actively contributes to the advancement of Explainable Artificial Intelligence (xAI) and its integration with fuzzy logic and causal models. The most recent publications highlight a strong trend toward explainable, interpretable, and ethically grounded AI, with applications in security, healthcare, project management, and intelligent control systems. His work frequently involves hybrid models, ensemble methods, and real-world validation in collaboration with NATO and other international bodies. National award for best young researcher, Bulgarian Union of Scientists Award for Outstanding Innovation in Digital Health and Social Care (2020) Excellence in Creating a Smarter World International Innovation Award (2020) Alexander Gegov has supervised numerous PhD students and served as Principal or Co-Investigator on research projects funded by NATO, EU, EPSRC, and Innovate UK. He is an active PhD supervisor and examiner. His leadership extends to editorial roles in top journals such as IEEE Transactions on Artificial Intelligence and IEEE Transactions on Fuzzy Systems, as well as participation in IEEE and NATO technical committees on explainable AI and computational intelligence. He has also organized international events such as the Alan Turing Institute Fringe Festival. He is a key member of several research groups including the Soft Computing Technical Committee of IEEE SMC, the IEEE Working Group on Explainable Artificial Intelligence, and the NATO Exploratory Team on Explainable AI. His work contributes to UN Sustainable Development Goals related to health, industry innovation, and responsible AI.

Smita Sahuمشاهده پروفایل

مدرس ارشد

• Mathematical Modeling
• Lithium-Ion Batteries
• Computational Methods
• +۷ مورد دیگر

Smita Sahu is a Senior Lecturer in the School of Mathematics & Physics within the Faculty of Technology at the University of Portsmouth. She is actively involved in research and teaching, with a focus on mathematical modeling of energy systems, particularly lithium-ion batteries, and numerical methods for partial differential equations. She contributes to the Centre for Environmental and Renewable Energy Solutions and is accepting PhD students. Education: Ph.D. in High-order and coupled schemes for Hamilton-Jacobi-Bellman equations, University of Rome La Sapienza (awarded 21 Jul 2015) MSc from Indian Institute of Technology Delhi (awarded 8 Aug 2010) Fellow of the Higher Education Academy (FHEA), awarded 24 Jul 2024 Research Interests: Her work centers on mathematical modeling for lithium-ion batteries, computational and numerical methods for Hamilton-Jacobi equations, nondestructive testing of metals, and traffic flow models. She is currently developing novel mathematical models and fast solvers for physics-based battery models. She is a key developer of DandeLiion, an ultra-fast solver for electrochemical models, developed in collaboration with the University of Southampton, Imperial College London, and funded by the Faraday Institution. Publication Trends: Her recent publications demonstrate a strong focus on mathematical modeling in energy storage, particularly lithium-ion batteries. She integrates numerical analysis, uncertainty quantification, and data-driven methods into electrochemical models. Her work spans theoretical development, solver implementation, and experimental validation, showing a cohesive research trajectory in computational electrochemistry and applied mathematics. Scientific Awards: Second prize for a presentation (26 Jun 2021) Advising and Grants: She is currently supervising PhD student Joseph Petrassem De Sousa on a project titled 'Mathematical modelling of coffee brewing'. She serves as a Co-Investigator on the active 'Multi-Scale Modelling' project funded by the Faraday Institution, focusing on physics-based modeling of sodium-ion and lithium-ion batteries. Her research contributes to UN Sustainable Development Goals related to clean energy and innovation. Labs and Teams: She is part of the DandeLiion development team, a collaborative effort between the University of Portsmouth, University of Southampton, and Imperial College London. She also contributes to the Centre for Environmental and Renewable Energy Solutions at the University of Portsmouth, working on computational models for sustainable energy technologies.

Majd Latahمشاهده پروفایل

پژوهشگر

• Security of mission-critical systems
• Software-defined networking
• Decentralized security
• +۳ مورد دیگر

Dr. Majd Latah is a Researcher (Postdoc) at the Department of Informatics, University of Hamburg, working under Prof. Mathias Fischer. His research focuses on cybersecurity for mission-critical systems, software-defined networking (SDN), and blockchain integration in network security. He holds a PhD from Ozyegin University. Key research areas include secure aircraft systems, decentralized security frameworks, and digital twin networks. His work emphasizes blockchain-based authentication protocols (e.g., HostSec, DPSec) and hybrid intrusion detection systems for SDNs. He has collaborated on projects like DTN-Core and CWT-DPA, addressing cross-domain authorization and distributed security challenges. Latah has published extensively on SDN security, blockchain applications, and AI-driven network optimization. His contributions span both theoretical frameworks (e.g., AI-enabled SDN overviews) and practical implementations (e.g., DoS attack detection systems). Affiliations include the Computer Networks research group at UHH, where he contributes to lab activities and team projects. His educational background includes studies at Ozyegin University under Dr. Kübra Kalkan.

Nidhi Simmonsمشاهده پروفایل

مدرس

• Ultra-Reliable Communications
• Machine Learning for Communications
• 5G/6G Networks
• +۳ مورد دیگر

Nidhi Simmons is a Lecturer and Royal Academy of Engineering Research Fellow at the Centre for Wireless Innovation (CWI), Queen’s University Belfast. Her research focuses on ultra-reliable communications in 5G/6G networks using machine learning approaches. She holds an M.Sc. in Wireless Communications and Signal Processing (University of Bristol, 2012) and a Ph.D. in Electrical Engineering (Queen’s University Belfast, 2018). She secured the £500k Royal Academy of Engineering Research Fellowship (2020–2026) and the EPSRC New Investigator Award (2025–2028), totaling ~£900k in funding. Her work includes wireless channel modeling, physical-layer security, and ML-driven solutions for reliable networks. Research interests span ultra-reliable communications, ML for 5G+/6G networks, and channel characterization. She has published over 30 IEEE articles and received the Best Paper Award at IEEE ANTS 2023. She serves on technical committees for IEEE conferences (GLOBECOM, VTC) and promotes diversity through mentorship programs for underrepresented engineering students. Awards include the Royal Academy of Engineering Fellowship, EPSRC New Investigator Award, and EPS Postdoc Prize. She actively mentors early-career researchers and supports initiatives like IEEE Women in Engineering and Women in Tech Belfast.

J. Todd McDonaldمشاهده پروفایل

استاد

• Software Protection and Assurance
• Secure Software Engineering
• Anomaly Detection
• +۵ مورد دیگر

Dr. J. Todd McDonald is a Professor of Computer Science at the University of South Alabama's School of Computing. He serves as Director of the Center for Forensics, Information Technology, and Security (CFITS) and has held academic roles including Associate Professor (2011-2014) and Assistant Professor at the Air Force Institute of Technology (2006-2009). His education includes a Ph.D. in Computer Science from Florida State University (2006), MS in Computer Engineering from AFIT (2000), MBA from University of Phoenix (1996), and BS from the U.S. Air Force Academy (1990). Research Interests: Focus on software/hardware protection (obfuscation, watermarking), malware analysis, secure embedded systems, and cybersecurity. Notable projects include Phase-Space Analysis for seizure prediction, CAN network security, and development of the Program Encryption Toolkit (PET) for circuit protection research. Publications: Over 80 peer-reviewed articles in journals/conferences (e.g., IEEE, ACM) covering topics like side-channel analysis, malware detection, and cybersecurity frameworks. Recent work includes 2025 research on embedded system anomaly detection and 2021 studies in Android malware classification. Awarded Best Poster (IEEE HOST 2016) and Best Paper (CISR 2014) Managed NSF Scholarship for Service program ($6M+ funding) Advised 20+ graduate students in cybersecurity and software protection Led USA's DayZero Cyber Defense Competition teams (5th NCCDC 2017) Labs/Teams: SPERG (System Protection & Exploitation Research Group), CFITS directorship, and leadership in SSPREW workshop series. Active in developing cybersecurity education curricula and industry partnerships.

• Statistics
• Forensic Science
• Health Analytics
• +۳ مورد دیگر

Dr. Melissa Humphries Senior Lecturer in the School of Computer and Mathematical Sciences at the University of Adelaide. Specializes in statistical methodologies applied to forensic science, health analytics, and AI integration. Advocates for end-user collaboration in research and equity in STEM. Affiliated with the Faculty of Sciences, Engineering and Technology. Research Interests: Development of statistical tools for decision-making transparency, forensic DNA analysis, explainable AI, and interdisciplinary applications in health and criminal justice. Active in bridging statistical models with physical systems and machine learning. Key Contributions: Published work on GAN-based DNA signal simulation, outlier importance in forensics, and methadone toxicity trends. Specializes in integrating advanced statistical techniques with real-world challenges in criminal justice and healthcare. Labs/Teams: Collaborates with forensic science groups and AI research teams at Adelaide. Engaged in multi-disciplinary projects combining statistics with engineering and medical applications.

• Social Network Analysis
• Network Sampling Methods
• Respondent-Driven Sampling
• +۳ مورد دیگر

Brian Kim, Ph.D., is an Assistant Research Professor in the Joint Program in Survey Methodology at the University of Maryland. His research focuses on social network analysis, network sampling methodologies (including respondent-driven sampling), and population size estimation. He holds a Ph.D. in Statistics from the University of California, Los Angeles (2017) and a B.A. in Mathematics and Philosophy from Amherst College (2012). His work emphasizes innovative approaches to survey design and data collection, particularly in addressing challenges related to hard-to-reach populations. Recent publications explore covert communication techniques using adversarial machine learning in wireless networks, reflecting his interdisciplinary expertise in statistics and computer science. Key Research Trends: Kim’s articles highlight advancements in adversarial machine learning for network security, optimization of open RAN systems, and methodological improvements in respondent-driven sampling diagnostics. His studies bridge theoretical statistical frameworks with practical applications in modern communication systems and survey methodologies. Lab/Team Affiliations: His research is conducted within the Joint Program in Survey Methodology, collaborating with institutions and platforms globally to inform public policy and research practices.

Antonio Pecchiaمشاهده پروفایل

پژوهشگر

• Deep Learning
• Cybersecurity
• Empirical Software Engineering
• +۱ مورد دیگر

Antonio Pecchia is a Researcher at the Department of Engineering within the University of Sannio . His work focuses on the dependability and security of distributed software systems , employing methodologies from deep learning , empirical software engineering , and operational data analysis to develop next-generation resilient systems . Academic Rank: Researcher University: University of Sannio School: College of Engineering Department: Department of Engineering Research interests include: Deep Learning for anomaly detection and intrusion prevention Cybersecurity with emphasis on denial-of-service attacks, malware detection, and network traffic analysis Empirical Software Engineering for dependability evaluation and fault tolerance Parallel and Distributed Computing in mission-critical systems His 15 most recent publications (2023–2025) demonstrate a consistent focus on: Intrusion detection using deep autoencoders and federated learning in IoT/CPS environments Critiques on the validity of public datasets like CICIDS2017 for cybersecurity research Fake news propagation analysis through topic modeling and bipartite graphs Proactive security testing frameworks like DEFEDGE for edge-cloud systems Scientific contributions include: IEEE Member Founder of the International Workshop on Reliability and Security Data Analysis (RSDA) Guest Editor for IEEE Transactions on Dependable and Secure Computing Editor-in-Chief of the International Journal of Open Source Software and Process Collaborations span institutions in Europe, Asia, and North America , with co-authors like Marta Catillo , Umberto Villano , and Domenico Cotroneo .

• Security
• Privacy
• Embedded Systems
• +۴ مورد دیگر

Vincent Naessens is a Professor at the Department of Computer Science within KU Leuven's Faculty of Engineering Technology, leading the Subdivision Mobility and Security at the Distributed and Secure Software (DistriNet) research group across Ghent and Aalst campuses. His work bridges academic research and industrial applications in critical security domains. Research Focus: Dr. Naessens specializes in secure mobile platforms , advanced authentication systems , privacy-enhancing technologies , and industrial control system security . His research addresses real-world vulnerabilities in IoT ecosystems and develops novel anonymization techniques that balance data utility with privacy protection. Current work emphasizes self-healing embedded systems and secure smart building infrastructures. Publication Trends: Recent publications (2024-2025) reveal three dominant threads: (1) Advanced dataset anonymization methods addressing temporal data and k-anonymity limitations; (2) Deep security analysis of commercial IoT products exposing critical vulnerabilities; (3) Privacy-preserving collaborative data sharing architectures. His work consistently targets practical implementations with measurable privacy-utility tradeoffs. Research Leadership: As principal investigator for major projects including Towards Self-Healing Embedded Systems (2025-2029) and BUGATTI: Embedded Security Testing (2025-2028), he directs teams exploring exploit prevention and adaptive patching. Key funding sources include FWO and EU programs supporting his work on secure SCADA systems and privacy middleware. Research Environment: Leading DistriNet's Mobility and Security subdivision, Naessens oversees a dynamic team publishing at top venues like WOOT and ARES. The group maintains strong industry ties through projects like TRUSTI (IoT security updates) and SolidLab Flanders, with active participation in the Computer Science Department Council and Faculty Advisory Committee.