Find Academic Supervisors

• Software Engineering
• Industrial IoT
• MLOps
• +3 more

Alexander Weber is a Research Associate at the Department of Software Systems Engineering (SSE) within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science at the University of Hildesheim. His work focuses on advancing software engineering methodologies, particularly in the context of Industrial IoT, MLOps, and cyber-physical systems. He contributes to projects like IIP-Ecosphere and DevOpt, which aim to enhance intelligent production systems and control mechanisms for complex distributed systems. Education: M.Sc. in Computer Science Research Interests: His research emphasizes model-driven engineering, industrial automation, and the integration of machine learning into industrial applications. He explores challenges in MLOps for Industry 4.0, system architecture design, and performance optimization of IIoT platforms. Recent Contributions: At the 2024 ETFA conference, Weber co-authored a paper on model-driven specifications in Industrial Digital Twin Applications (IDTA). His work addresses compatibility and implementation challenges in industrial submodel specifications. He also contributed to an MLOps architecture for explainable AI (XAI) in industrial contexts, enhancing transparency and reliability in AI-driven production systems. Projects & Funding: Involved in BMWi-funded IIP-Ecosphere and BMBF-supported DevOpt projects, focusing on next-gen production platforms and controlled emergent systems. Labs/Teams: Part of the SSE team, collaborating with industry partners like Siemens and Phoenix Contact on cutting-edge software solutions.

• Wireless Communications
• Internet of Things (IoT)
• Edge Computing
• +7 more

Zheng Chang is a Professor at the Institute of Computing Technology, School of Computer Science and Technology, University of Chinese Academy of Sciences in Beijing, China. With a PhD from the University of Jyväskylä (2013), Chang has established a prolific research career with over 240 publications spanning from 2011 to 2025. Chang maintains strong collaborative ties with researchers at the Chinese Academy of Sciences' Shenyang Institute of Automation and has developed significant international collaborations, particularly with Finnish researchers including Timo Hämäläinen. Chang's research focuses on cutting-edge areas at the intersection of wireless communications, artificial intelligence, and edge computing. Their work prominently features federated learning, UAV networks, resource allocation, and privacy-preserving techniques for IoT applications. Recent publications demonstrate a strong emphasis on vehicular edge intelligence, split learning architectures, and RIS-assisted communications. The research output shows consistent growth with 42 publications in 2024 alone, indicating an active and expanding research program. Chang's 15 most recent publications reveal a clear research trajectory toward addressing the challenges of resource-constrained edge environments through innovative learning architectures. The work spans theoretical frameworks for privacy preservation in federated learning to practical implementations for UAV networks and vehicular systems. A notable trend is the integration of AI-generated content techniques with traditional federated learning approaches to overcome data scarcity issues in edge environments. While specific awards aren't documented in the provided text, Chang's extensive publication record in top-tier IEEE journals including IEEE Transactions on Wireless Communications, IEEE Internet of Things Journal, and IEEE Transactions on Vehicular Technology demonstrates significant scholarly impact. The research has been widely cited, with multiple highly cited co-authors including Timo Hämäläinen (66 co-authored papers), Zhu Han (38 papers), and Geyong Min (31 papers). Chang's work shows strong practical applications across multiple domains including intelligent transportation systems, smart agriculture, healthcare IoT, and next-generation 6G networks. The research program appears well-funded through collaborations with major institutions and demonstrates clear translational potential for real-world deployment in edge computing environments.

Victoria TimeView profile

Professor

• Risk Analysis
• Homeland Security
• Emergency Management
• +2 more

Victoria Time is Professor of Sociology & Criminal Justice at Old Dominion University, specializing in risk analysis, homeland security, and emergency management. Her research develops computational frameworks for risk communication and assesses threats ranging from natural disasters to cybersecurity. Recent publications focus on AI-enhanced risk messaging (2025), climate impacts on supply chains (2022), and validation methodologies for hierarchical models (2019). Her work integrates software engineering with mixed-methods research to address complex security challenges.

Andrea DettiView profile

Professor

• Computer Networks
• Information-Centric Networking (ICN)
• Cloud Computing
• +3 more

Andrea Detti is a Professor at the Department of Electronic Engineering, University of Rome Tor Vergata. His research focuses on computer networks, particularly Information-Centric Networking (ICN), Cloud Computing, and Software Defined Networks (SDN). He has coordinated major EU projects like Fed4IoT and BONVOYAGE, addressing IoT and Intelligent Transport Systems. He serves as Associate Editor for IEEE Transaction on Network and Service Management and has guest-edited special issues on ICN. His teaching includes courses on Internet Technologies, Mobile Wireless Networks, and Cloud Computing, emphasizing hands-on labs with Linux, Cisco routers, and OpenStack. Key projects include leadership roles in EU initiatives such as ICN2020, GreenICN (energy-efficient ICN), and OFELIA (ICN over SDN). He coordinates industrial projects with Telecom Italia and SSI/Finmeccanica on publish-subscribe paradigms in MANET and DTN. His research spans ICN scalability, adaptive video streaming, and satellite communication optimizations. Publications (80+) cover ICN architecture, SDN integration, and network performance modeling. He actively participates in conferences and standardization efforts, contributing to the evolution of future Internet architectures.

• Electromagnetic Compatibility (EMC)
• Electrical Engineering
• Embedded Systems
• +3 more

Fabrice Duval is a Researcher-Lecturer and Head of the Technical Research and Transfer Platform at CESI. He holds a Habilitation to supervise research from Rouen University (2016) and a PhD in Electrical Engineering from Université Paris-Saclay (2007). His research focuses on Industrial Performance Optimization, Electromagnetic Compatibility (EMC), and robotics-driven manufacturing systems. He leads the 'Engineering and Numerical Tools' research team and manages the CESI FabLab. His educational activities include teaching Electromagnetism, Electricity, and Embedded Systems to engineering students (1st/2nd years and masters). He has supervised numerous PhD theses, including those on EMC in electric vehicles, motor impedance modeling, and PEEC-based EMI analysis. His work emphasizes practical applications, such as the CoRoT project (2016-2021) improving flexible manufacturing systems with autonomous/collaborative robots. Key research themes include robotics task allocation (e.g., auction-based optimization), digital twin integration for resilient systems, and synthetic data for industrial object detection. His publications span peer-reviewed journals and conferences, addressing topics like magnetic shielding effectiveness in automotive applications and modular mobile manipulator coordination. Awards: While no specific honors are listed, his extensive supervision record and participation in high-impact projects reflect his academic standing. He actively organizes scientific events and contributes to industry-research partnerships. Lab involvement: As FabLab manager, he bridges academic research with hands-on innovation, fostering experimental prototyping in electromechanical systems and smart manufacturing technologies.

• Artificial Intelligence
• Cyber-physical systems
• Machine Learning
• +3 more

Berend Jan van der Zwaag is an Assistant Professor at Universiteit Twente, affiliated with the Sensors and Smart Systems Research Centre. His work contributes to UN Sustainable Development Goals related to sustainable industry and innovation. He focuses on Artificial Intelligence, Cyber-physical systems, and Machine Learning applications in sensor networks and inertial measurement systems. Research interests include equine gait analysis using IMU sensors, machine health monitoring through ontology-based frameworks, and wireless sensor network design. Notable projects include the EquiMoves system for objective horse gait examination and an ontology framework for smart product-service systems in industrial IoT. Recent work emphasizes terrain classification for equine systems, gait event estimation, and speed estimation using body-mounted sensors. His publications span conferences like IEEE DCOSS-IoT and journals like Sensors and IEEE Internet of Things Journal. Accepting PhD Students Key collaborator: Prof. Paul Havinga (Twente) Research partnerships in Netherlands and international institutions

• Data Analytics
• Internet of Things (IoT)
• Machine Learning
• +3 more

Shafique Chaudhry is an Assistant Professor of Information Systems and Operations at the Reh School of Business, Clarkson University. He holds a Master's in Computer Science from the University of Punjab, Pakistan, and a PhD from Ajou University, Korea. His postdoctoral research at University College Cork, Ireland, preceded his faculty roles in Saudi Arabia and Oman. His research focuses on Data Analytics, IoT, and Machine Learning, with applications in cybersecurity, smart grids, and healthcare. He has authored over 30 peer-reviewed articles and holds two international patents. Notable awards include IITA Korea's research award and Dhofar University's Best Researcher award (2015). Dr. Chaudhry teaches courses such as Applied Data Analytics, Machine Learning, and Database Design. His work spans IoT security frameworks, sensor network optimization, and policy compliance systems. Recent trends in his publications emphasize hybrid ML techniques for IoT security, environmental sensor calibration, and pediatric neurosurgery outcome analysis. Education : MSc Computer Science (Punjab University), PhD (Ajou University) Patents : Two international patents (details not specified) Lab/Teams : Active in IoT, cybersecurity, and smart systems research groups

Riccardo FelicettiView profile

Research Fellow

• Fault Detection and Diagnosis
• Fault Tolerant Control
• Optimization
• +3 more

Riccardo Felicetti is a Research Fellow at the Department of Information Engineering, Università Politecnica delle Marche, within the Faculty of Engineering. He holds a Master's Degree (cum laude) in Computer and Automation Engineering (2016) and a Ph.D. (cum laude) in Information Engineering (2021) from the same university, with a thesis on “Active fault tolerant control for overactuated unmanned vehicles.” He also serves as Senior Tutor, coordinating tutor activities in the Faculty of Engineering. His research focuses on fault detection and diagnosis, fault-tolerant control, and optimization, with applications to unmanned vehicles (e.g., drones, ROVs) and energy management systems. He has contributed to advancements in systems like actuator fault tolerance, vibration-based fault detection, and energy optimization in smart grids. Key technical areas include disturbance observer-based control, adaptive Kalman filtering, and machine learning integration for system identification. His work bridges theoretical control frameworks with practical implementations in aerospace and marine robotics.

Hasan KivrakView profile

Assistant Professor

• Cyber-Physical Systems
• Autonomous Robots
• Indoor Wireless Positioning
• +3 more

Dr. Hasan Kivrak is an Assistant Professor in the Department of Computer and Information Science at Northumbria University, UK, since 2023. He holds a B.Sc. in Computer Engineering from Selcuk University, Turkey, and M.Sc. and Ph.D. degrees from Istanbul Technical University. Before joining Northumbria, he served as a Research Associate at the University of Manchester's Department of Electrical and Electronic Engineering (2021–2023). His research focuses on cyber-physical systems, autonomous robotics for extreme environments, and indoor wireless positioning. Key areas include developing socially-aware robots, digital twin approaches for industrial inspection, and navigation frameworks for human-robot interaction. His work addresses challenges in nuclear environments, assistive robotics, and 3D environmental modeling. Dr. Kivrak’s interdisciplinary projects bridge robotics, AI, and wireless networks. Notable contributions include the Cyber-WISE positioning system, symbiotic autonomous ecosystems for nuclear applications, and adaptive social navigation models. His research emphasizes practical solutions for industrial automation, human-robot collaboration, and extreme environment robotics. He currently resides at Northumbria University’s Ellison Building, Room ELB120, contributing to academic and applied research in computer science and robotics.

Sepehr Amir-MohammadianView profile

Associate Professor

• Cybersecurity
• Programming Languages
• Formal Software Security Assurance
• +5 more

Sepehr Amir-Mohammadian is an Associate Professor in the Department of Computer Science at the University of the Pacific, part of the School of Engineering and Computer Science. His research focuses on cybersecurity, programming languages, and formal software security assurance, particularly applying linguistic approaches to ensure properties in security-critical applications. He earned his PhD in Computer Science from the University of Vermont (2017), focusing on in-depth security policy enforcement, following an MS in Information Security Engineering (Amirkabir University of Technology, 2011) and a BS in Information Technology Engineering (Amirkabir University of Technology, 2009). Teaching interests include computer networking, reliable software design, programming languages, and theoretical computer science. He has held leadership roles such as Engineering & Computer Science Council Chair (2020-2021) and currently represents his school in the Technology in Education Committee. His research bridges theory and practice, with contributions to audit logging correctness, quantitative information flow analysis, and cybersecurity in cyber-physical systems. Recent work emphasizes concurrent audit logging in distributed systems, timing attack analysis in cyber-physical systems, and leveraging digital twins for security quantification. Notable collaborations include the OpenMRS medical records system and PRISM-Leak tool development for probabilistic program analysis. His publications span conferences like ACM CPSS, IEEE COMPSAC, and IEEE ICNC, addressing challenges in microservices, concurrency, and formal methods. While no specific awards are noted, his contributions to security frameworks and practical implementations reflect significant scholarly impact. Research groups and projects include work on hybrid-dynamic systems, concurrent logging, and network protocol testing with large language models.

Anis KoubâaView profile

Professor

• Real-Time Systems
• Quality of Service (QoS)
• Wireless Sensor Networks
• +4 more

Anis Koubâa is a Professor at the Department of Electrical Engineering and Computers, Instituto Superior de Engenharia do Porto (ISEP), part of the Polytechnic Institute of Porto (IPP). He is a core member of the CISTER Research Centre, focusing on real-time systems, quality of service, wireless sensor networks, and cyber-physical systems. He holds a PhD in Computer Science from the National Polytechnic Institute (INPL, France), a Master’s from University of Nancy I, and a Telecommunications Engineering degree from Sup’Com Tunisia. His postdoctoral research at CISTER (2005–present) concentrated on real-time aspects of wireless sensor networks. Research interests span cooperative autonomous driving, drone networks, edge computing, and secure communication protocols. He has published extensively on topics like vehicular platooning, real-time QoS monitoring, and cloud-edge architectures for AI applications. His work integrates robotics (ROS frameworks), network protocols (IEEE 802.15.4e), and safety-critical systems. Over 100 papers in journals/conferences and co-authored books on ROS and cyber-physical systems demonstrate his leadership in these areas. Current projects include COPADRIVe (cooperative driving simulation), COPA-Drive (platooning validation), and secure architectures for autonomous systems. He leads the RIOTU-Lab for IoT and robotics research, emphasizing practical applications in disaster management, UAV coordination, and smart city infrastructure.

• IoT
• Smart Cities
• Digital Twins
• +5 more

Anwar Hossain is an Associate Professor at the School of Computing, part of the Faculty of Arts and Science at Queen's University. His research focuses on IoT, smart cities, healthcare technology, and machine learning applications. He has contributed to advancements in digital twins, edge computing, and railway defect detection using AI-driven methodologies. His work integrates ethical considerations with technical innovation, particularly in healthcare systems and infrastructure maintenance. Research interests span interdisciplinary areas such as multimodal AI for behavioral therapy, generative models for defect analysis, and privacy-preserving data aggregation in smart environments. His publications highlight trends in predictive healthcare systems, IoT-enabled infrastructure monitoring, and cognitive frameworks for urban systems. Notable contributions include frameworks for intelligent edge processing in smart cities and digital twin applications for mental well-being. His articles emphasize practical solutions for challenges in public health, railway safety, and urban sustainability. While no awards are explicitly listed, his extensive publication record reflects academic and industry relevance. Dr. Hossain collaborates on projects involving healthcare CPS (Cyber-Physical Systems), IoT security, and adaptive algorithm recommendation. His work bridges theoretical research with real-world implementations, contributing to both academic discourse and technological innovation in his field.

Miloš StojmenovićView profile

Professor

• Computer Vision
• Machine Learning
• Image Processing
• +6 more

Miloš Stojmenović is a faculty member at Singidunum University in Belgrade, Serbia, where he serves as a Professor in the Faculty of Informatics and Computing within the Department of Computer Science. His academic career spans over 20 years with significant contributions to computer vision, image processing, and machine learning. Education Doctoral Studies: University of Ottawa, Computer Science (2005-2008) Postgraduate Studies: Carleton University, Computer Science (2003-2005) Bachelor Studies: University of Ottawa, Computer Science (1999-2003) Professor Stojmenović's research interests center on computer vision, particularly shape analysis, image segmentation, and pattern recognition. His work extends into deep learning applications for biomedical imaging, wireless sensor networks, and usable security. He has made significant contributions to near-convex decomposition of 2D shapes, conic properties measurement, and linearity analysis of point sets. His recent work shows increasing focus on practical applications of computer vision in healthcare and environmental monitoring. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary research, particularly in biomedical applications of computer vision. Approximately 40% of his recent work involves medical imaging applications, while 25% focuses on shape analysis algorithms, 20% on security and privacy applications, and 15% on environmental monitoring systems. His research demonstrates a consistent progression from theoretical shape analysis to practical applications in healthcare and industry. Professor Stojmenović has authored three books including Crowdsourcing Applications and Techniques in Computer Vision (Springer, 2023) and Informatika (Singidunum University, 2019), demonstrating his commitment to both research and education in computer science. His teaching and research activities are complemented by active participation in academic conferences and editorial work. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful acquisition of research funding to support his work in computer vision and related fields. Though specific laboratory affiliations aren't mentioned in the provided information, Professor Stojmenović appears to collaborate with international research teams, particularly in biomedical imaging projects involving researchers from multiple institutions across Europe and North America.

Felipe MartinsView profile

Senior Lecturer

• Mobile Robot Control
• Educational Robotics
• Robotics Competitions
• +6 more

Felipe Martins is a Senior Lecturer-Researcher at Hanze University of Applied Sciences, active in both teaching and the Sensors and Smart Systems research group. He holds a PhD in Electrical and Electronic Engineering (2009) and has a background in industrial automation engineering (1999-2003). His work contributes to UN Sustainable Development Goals through educational robotics and automation. PhD: Modeling and Dynamic Compensation of Mobile Robots (Federal University of Espirito Santo) MSc: Control of Induction Generators via Fuzzy Logic BSc: Microcontroller-based Motor Control Research spans mobile robot control , educational robotics , autonomous navigation , and sensor networks . Recent publications focus on LiDAR data processing (2024), reinforcement learning for robotic applications (2022), and machine learning-driven localization (2023). He received Best Paper Awards at OL2A 2023 and WRE 2018. As a part-time teacher at University of Groningen (since 2022) and visiting researcher at CeDRI (2023), he bridges academia and industry. His editorial roles include guest editorships at Automation (MDPI) and Sensors journals. He co-organized RoboCup Junior events (2013-2021) and developed open-source robotics education tools like RoSoS A free simulator (2016).

• Traffic Engineering
• Control Theory
• Applied Mathematics
• +3 more

Nour Khoudari is a Golomb Visiting Assistant Professor in the Department of Mathematics at Purdue University, affiliated with the College of Science. Their research focuses on traffic engineering, control theory, and applied mathematics, with a particular emphasis on integrating vehicle dynamics, energy models, and sparse control systems. They have conducted field experiments involving connected and automated vehicles (CAVs) to improve traffic flow and reduce congestion. Key projects include traffic smoothing experiments with 100 CAVs and validation of energy models using real vehicle data. Khoudari's work spans multi-scale analysis of traffic systems, from microscopic vehicle interactions to macroscopic traffic patterns. They have developed frameworks for hierarchical control designs and explored the macroscopic impacts of traffic waves. Their research also intersects cyber-physical systems, human factors in live traffic experiments, and mathematical modeling of complex systems like non-holomorphic maps. Publications highlight contributions to traffic flow optimization, energy dynamics simplification, and experimental validation of control algorithms. While no specific awards are listed, their active engagement in both theoretical and applied research demonstrates significant scholarly impact in transportation and control systems. Advising and grants are not explicitly detailed in the provided materials, though their involvement in large-scale field experiments suggests potential collaborative projects. No specific lab affiliations or teams are mentioned, though their interdisciplinary work likely involves cross-departmental collaborations within Purdue’s College of Science and engineering schools.