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Marjan SirjaniView profile

Professor

• Formal Methods in Software Engineering
• Cyber-Physical Systems Analysis
• Actor Model and Verification
• +3 more

Marjan Sirjani is a Professor at Mälardalen University's School of Innovation, Design and Engineering (IDT), located in Västerås, Sweden. She leads the Cyber-Physical Systems Analysis research group and has held academic leadership roles, including founding the Formal Methods Laboratory at the University of Tehran and co-founding the Icelandic Center for Research on Software Engineering at Reykjavik University. Her professional background includes over a decade as Managing Director of Behin System Company before transitioning to full-time academia. Her research focuses on applying formal methods to software engineering, with specialization in modeling and verification of concurrent, distributed, and real-time systems using actor-based approaches. Key contributions include the Rebeca modeling language and its extensions for analyzing network applications and autonomous systems. Current work emphasizes safety, security, and performance evaluation of cyber-physical systems (CPS). Marjan has led major research initiatives such as the SACSys project (2019-2023), Serendipity (2018-2023), and MACMa (2016-), all addressing critical challenges in CPS and autonomous systems. She pioneered the use of timed actors for system analysis and developed the Afra model checking tool, which remains central to her work. Notably, she co-authored foundational papers on Constraint Automata semantics for coordination languages like Reo. Professional Roles: Steering Committee Chair of FSEN conference series Former Editor of Science of Computer Programming journal Expert panel member on Engineering and Physical Sciences at RANNÍS (Iceland) Recent Projects: SACSys: Safe & Secure Adaptive Collaborative Systems Serendipity: Safe and Reliable Platforms for Autonomy MACMa: Modeling and Analyzing Cyber-Physical Systems Her publications consistently explore actor model advancements, CPS security, and model-driven development. Recent work highlights integration of AI (e.g., LLM and ChatGPT) with formal verification techniques, as well as ROS2 system analysis. She has delivered keynote speeches and invited talks on topics like autonomous systems trustworthiness and real-time distributed system analysis across international venues including MIT and UC Berkeley. Awards: 2016: Distinguished Alumni Award from Sharif University of Technology Marjan's academic service includes chairing multiple conferences (SEFM, iFM, COORDINATION) and serving on program committees for major events like DATE and FSEN. Her teaching spans institutions including Reykjavik University (Software Engineering II, Advanced Software Engineering) and University of Tehran (Modeling & Verification of Concurrent Systems).

• Computer Vision
• Historical Image Analysis
• Machine Learning
• +3 more

Marvin Burges is a PreDoc Researcher at the Department of Computer Vision within the Faculty of Informatics at TU Wien. His research focuses on applying computer vision and machine learning techniques to historical aerial imagery, with a particular emphasis on object detection, semantic classification, and interactive analysis. He is actively involved in the DoRIAH project (2020–2024), exploring applications such as bomb crater detection and semi-automated object recognition in archival materials. Burges teaches courses including 3D Vision and Introduction to Visual Computing . His work bridges historical preservation, geospatial analysis, and modern AI methods, with publications in venues like GeoSearch, IEEE WACV, and OAGM workshops. His methodologies include self-supervised learning, vision-language models, and human-in-the-loop systems to enhance the interpretability of legacy datasets. Key technical contributions include developing algorithms for historical aerial image classification, crater detection, and novel class identification in remotely sensed imagery. His research addresses challenges in digitizing and analyzing large-scale historical archives through interdisciplinary approaches.

Dong MaView profile

Assistant Professor

• Mobile Computing
• Wearable Sensing Systems
• Health Monitoring
• +3 more

Roles & Affiliations: Assistant Professor at Singapore Management University's School of Computing and Information Systems (since 2022). Visitor at University of Cambridge (April 2025–present), with planned move as Associate Professor in Jan 2026. Previously postdoc at University of Cambridge (2020–2022) and PhD from University of New South Wales (2019). Research Interests: Focuses on mobile/wearable computing, health monitoring via earables, and tiny machine learning on embedded devices. Develops systems to analyze human behavior (activity/interaction) and physiological states (e.g., vital signs) in real-world scenarios. Recent work includes respiratory monitoring using earbuds and energy-efficient sensing systems. Awards & Recognition: Google South Asia & Southeast Asia Research Award (2024) Mark Weiser Best Paper Award at IEEE PerCom (2024 & 2025) EPFL Engineering PhD Summit (2019): 1 of 11 worldwide Academic Service: Serves on TPC for major conferences (MobiCom, PerCom, CHI) and editorial boards (IMWUT). Organized workshops like BodySys@MobiSys'24 and co-chair roles in conference committees. Labs/Teams: Leads research groups in wearable sensing and edge AI at SMU. Collaborates with institutions like Nokia Bell Labs, CSIRO, and University of Cambridge on projects funded by ERC and industry grants.

Jose M Alcaraz CaleroView profile

Professor

• 5G Networks
• Network Slicing
• IoT Security
• +5 more

Prof Jose M Alcaraz Calero is a Full Professor in Next-Generation Cyber Security at Aston University's School of Computer Science and Digital Technologies. He holds positions within the Department of Software Engineering and Cybersecurity under the College of Engineering and Physical Sciences. His academic credentials include a PhD and MEng, alongside SMIEEE and FHEA honors. His research focuses on 5G networks, IoT security, AI-driven systems, and automated driving technologies. Notable areas of exploration include network slicing, cybersecurity for connected systems, and AI applications in healthcare and environmental monitoring. He collaborates internationally on projects involving smart sensor networks and edge computing solutions. Recent work demonstrates innovations in AI-based odour detection systems using 5G IoT frameworks, stock price prediction via speech models, and autonomous vehicle reference architectures. His research outputs emphasize practical implementations of emerging technologies in real-world scenarios. Prof Alcaraz Calero has contributed 202+ peer-reviewed publications across journals and conferences. His work bridges theoretical advancements with industrial applications, particularly in cybersecurity, telecommunications, and smart systems deployment.

• Security
• Privacy
• Network Security
• +3 more

Kotzanikolaou Panagiotis serves as an Associate Professor specializing in Security and Privacy of New Generation Networks at the Department of Informatics, University of Piraeus, where he also directs the Department's Security Laboratory. With a strong academic foundation including a degree in Informatics and a PhD in information systems security, both from the University of Piraeus, he has established himself as a leading expert in network security and privacy domains. University of Piraeus - BSc in Informatics University of Piraeus - PhD in Information Systems Security Professor Kotzanikolaou's research focuses on the critical intersection of security and privacy within next-generation communication networks. His work addresses fundamental challenges in securing modern network infrastructures against evolving threats while preserving user privacy. He has developed expertise in cryptographic techniques, authentication mechanisms, and security protocols specifically designed for emerging network technologies including 5G, IoT, and edge computing environments. His research approach combines theoretical rigor with practical implementation considerations, ensuring relevance to real-world security challenges faced by communication systems today. Analysis of his publication record reveals a consistent focus on addressing security challenges in evolving network technologies, with increasing attention to AI-driven security solutions, quantum-resistant cryptography, and privacy-preserving techniques for distributed systems. His work demonstrates a progression from traditional network security approaches to more sophisticated methods addressing the unique challenges posed by next-generation networks, including 5G infrastructure, IoT ecosystems, and edge computing environments. CISSP (Certified Information Systems Security Professional) ISO 27001 Lead Auditor certification Professor Kotzanikolaou actively participates in research and development projects for both public and private sector entities in the field of Information and Communication Systems Security. He serves on organizing committees for international conferences and contributes as a guest editor and reviewer for prominent scientific journals in security. His professional engagement extends to practical applications through previous work as a Special Scientist at the Communications Privacy Assurance Authority (ADAE) and as a systems security consultant in the private sector. As director of the Department's Security Laboratory at the University of Piraeus, he leads research initiatives focused on cutting-edge security challenges in modern communication networks. The laboratory serves as a hub for collaborative research involving academic researchers, industry partners, and government agencies working together to develop innovative security solutions for next-generation network infrastructures.

• Human Activity Recognition
• Wireless Sensor Networks
• Internet of Things (IoT)
• +1 more

Emanuele Lattanzi is an Associate Professor in the Department of Pure and Applied Sciences (DiSPeA) at the University of Urbino Carlo Bo, specializing in Information Processing Systems (IINF-05/A). His research focuses on human activity recognition, wireless sensor networks, IoT systems, and artificial intelligence applications. He obtained his Laurea degree in 2001 and PhD in 2003, followed by a visiting scholar position at Pennsylvania State University's Department of Computer Science and Engineering. His educational background forms the foundation for his current research in sensor-based AI systems. Lattanzi's research interests center on developing energy-efficient solutions for human activity recognition using wearable sensors, with particular emphasis on optimizing machine learning models for resource-constrained IoT devices. His work bridges theoretical AI advancements with practical implementation challenges in real-world sensor deployments. Analysis of his 2025 publications reveals a strong thematic focus on human activity recognition, with recurring exploration of energy-accuracy tradeoffs, novel neural network architectures (including transformers and Siamese networks), and self-supervised learning techniques specifically adapted for inertial sensor data. His research demonstrates consistent collaboration with multiple co-authors across international conferences. As an educator, Lattanzi teaches core computer science courses including Operating Systems (12 credits) and IoT Programming (6 credits) across multiple Informatics degree programs. His teaching spans from 2021/2022 through the current 2025/2026 academic year, demonstrating sustained contribution to the department's curriculum in both foundational systems programming and emerging IoT technologies.

• Cyber Security
• Intrusion Detection Systems
• Internet of Things (IoT)
• +5 more

Dr. Ayyaz-Ul-Haq Qureshi is a Lecturer in Cyber Security and Networks at Glasgow Caledonian University, United Kingdom. His academic profile demonstrates active engagement in research and teaching within the field of cyber security, with a particular focus on practical security implementations for IoT environments. Dr. Qureshi's research interests span multiple critical areas in modern cyber security: Intrusion Detection Systems for IoT devices and smart homes Machine learning applications in network security Lightweight security implementations for resource-constrained devices LoRaWAN and wireless network security Neural network approaches to security challenges Malware detection through API call analysis His recent publication record shows consistent research output with publications in reputable journals including IEEE Access, Sensors, and Future Internet. Dr. Qureshi's work demonstrates a strong focus on practical security solutions that bridge theoretical machine learning approaches with real-world implementation challenges in IoT environments. His fingerprint analysis reveals particularly strong concentrations in Random Neural Networks (100%), Intrusion Detection Systems (90%), and LoRaWAN applications (85%). Dr. Qureshi has accumulated a respectable h-index of 7 with 117 citations, indicating growing impact in his research areas. His work shows increasing citation metrics, with several papers accumulating 20+ citations, suggesting growing recognition in the academic community. As a lecturer, Dr. Qureshi contributes to cyber security education at Glasgow Caledonian University, likely teaching courses that align with his research expertise in network security and IoT protection. His research collaborations span multiple institutions, suggesting an active research network focused on addressing contemporary security challenges in connected environments.

Gregory GangerView profile

Professor

• Computer Systems
• Cloud Computing
• Distributed Systems
• +6 more

Gregory Ganger is the Stephen J. Jatras Professor at Carnegie Mellon University's College of Engineering, holding joint affiliations with the Computer Science Department and CyLab. He has served as Director of the Parallel Data Lab (PDL) since 2000 and teaches Storage Systems (Fall) and Advanced Cloud Computing (Spring) courses. Education: Ph.D. in Computer Science & Engineering (1995, University of Michigan) M.S. in Computer Science & Engineering (1993, University of Michigan) B.S. in Computer Science (1991, University of Michigan) Dr. Ganger's research spans computer systems, focusing on: cloud computing, distributed systems, storage architectures, and machine learning infrastructure. His work addresses challenges in resource scheduling, non-volatile memory optimization, and scalable infrastructure design through projects like BigLearning, CILES, HeART, and Zoned Storage systems. Recent publications demonstrate expertise in: DNN training optimization (GraphPipe, Nonuniform-Tensor-Parallelism), sustainable storage (Storage Emissions, FairyWREN), and cloud efficiency (MACARON Cache). These works intersect machine learning, storage systems, and hardware-software co-design. Scientific Awards: 2021 OSDI Best Paper 2021 SOSP Best Paper 2021 SoCC Test of Time Award 2021 R&D 100 Award As advisor to 8 graduate students, he contributes to training the next generation of systems researchers. His lab (PDL) collaborates with industry partners on cutting-edge storage and cloud infrastructure problems.

Martin WeigelView profile

Professor

• Human-Computer Interaction
• Virtual Reality
• Wearable Computing
• +3 more

Prof. Dr. Martin Weigel leads the Interactive Novel Technologies Lab at Technische Hochschule Mittelhessen in Gießen, focusing on Human-Computer Interaction (HCI) with specializations in Virtual Reality (VR), wearable computing, and haptic interfaces. His work explores novel interaction techniques, motion guidance systems, and on-body interfaces. Research interests span VR/AR applications, wearable devices, and tactile feedback systems. Key contributions include VRSketchPen (haptic sketching), Tactile Vectors (arm guidance), and DeformWear (wearable deformation input). Recent projects address telepresence robotics in caregiving and drone-human body interaction. Publications highlight advancements in haptic feedback design, user studies on motion guidance, and multimodal interaction systems. Awards include a Best Paper Award for iSkin (2015), a flexible on-body sensor system. Current lab members include researchers like Kevin Linne and student assistants. Patents include EP4147830B1 (2025) for tele-robotic systems and EP4120223B1 (2024) for drone landing mechanisms. Advised PhD student Dr. Hesham Elsayed (2023) and oversees a team engaged in cutting-edge HCI research.

Yuchou ChangView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Biomedical Imaging
• +3 more

Dr. Yuchou Chang is an Associate Professor in the Department of Computer & Information Science at the University of Massachusetts Dartmouth's College of Engineering. His research focuses on artificial intelligence, machine learning, biomedical imaging, intelligent robotics, and brain-computer interfaces. He received a $20,000 award from the Office of Naval Research for research on Tiny Machine Learning for Low-Power Unmanned Undersea Vehicles. Education: PhD from University of Wisconsin-Milwaukee (2012) ME from Shanghai Jiao Tong University (2006) BE from Northwestern Polytechnical University (2003) Teaching: Courses include Artificial Intelligence, Master's Project/Thesis, and Advanced Data Mining, covering expert systems, neural networks, and machine learning applications.

Shigang YueView profile

Professor

• Artificial Intelligence
• Computer Vision
• Robotics
• +4 more

Professor Shigang Yue is a Professor of Computer Science at the University of Leicester's Computing and Mathematical Sciences department. His research focuses on artificial intelligence, computer vision, robotics, neuroscience, and bio-inspired systems. He has published over 200 papers and led EU-funded projects. He holds a PhD from Beijing University of Technology (1996) and was an Alexander von Humboldt Research Fellow (2000-2001). Research interests include bio-inspired visual neural networks, autonomous robotics, medical image analysis, machine learning, and swarm intelligence. He has coordinated EU FP7 and Horizon 2020 projects and examined over 20 PhD theses. His work bridges biological systems and robotics, emphasizing collision avoidance, motion detection, and swarm robotics. Notable contributions include the I2Bot platform for insect navigation simulation, bio-inspired collision detection models (e.g., LGMD), and frameworks for small target tracking. He is an EPSRC peer reviewer and editorial board member for international journals. Labs and teams: Active in developing bio-inspired robotics systems and collaborative platforms like Colcos φ for swarm robotics. His group welcomes PhD and visiting students in AI and robotics.

Mahdi AbbasiView profile

Associate Professor

• Edge Computing
• Machine Learning
• Cloud Computing
• +3 more

Mahdi Abbasi serves as an Associate Professor in the Department of Computing Science at Umeå University, Sweden, where he conducts research at the intersection of machine learning and distributed computing systems with emphasis on resource-constrained environments. His research portfolio spans critical domains in modern computing: Edge Computing infrastructure optimization Tiny Machine Learning (Tiny ML) applications Cloud-Edge continuum workload distribution Distributed systems autonomy Computational resource allocation Latency-sensitive processing frameworks Analysis of his recent publications reveals a focused trajectory toward developing autonomous, compact machine learning models that enable intelligent workload management across distributed computing environments, reducing cloud dependency while improving operational efficiency in IoT and mobile ecosystems. No scientific awards were documented in the provided source material. Available information contains no records of graduate student supervision, research grant acquisitions, or specialized laboratory affiliations associated with Dr. Abbasi's academic activities.

Riccardo BertaView profile

Associate Professor

• Tiny Machine Learning
• Reinforcement Learning
• Deep Learning on Microcontrollers
• +5 more

Riccardo Berta is an Associate Professor at the Department of Naval, Electrical, Electronic, and Telecommunications Engineering (DITEN), University of Genoa. His research focuses on TinyML, deep learning on microcontrollers, and embedded systems for automotive and IoT applications. Current Affiliation: Department of Naval, Electrical, Electronic, and Telecommunications Engineering (DITEN), University of Genoa Academic Role: Associate Professor, Electronics (IINF-01/A) His research interests include: Tiny Machine Learning (TinyML) for resource-constrained devices Deep Reinforcement Learning in automated driving and gaming Edge Computing and Structural Health Monitoring IoT frameworks for data management and serious games Recent work explores the application of neural networks, adversarial training, and synthetic dataset generation in automotive scenarios. He is a member of the Scientific Council of the University Service Center for Simulation and Advanced Training (SIMAV) and the Department Board of DITEN.

Yuri BorgianniView profile

Associate Professor

• Engineering Design
• Product Development
• Design Creativity
• +7 more

Yuri Borgianni is an Associate Professor at the Faculty of Engineering, Free University of Bozen-Bolzano (unibz), Italy. He teaches courses including Technical Drawing, Reverse Engineering & Rapid Prototyping, and Design Creativity. His research focuses on engineering design, product development, and sustainable innovation. Borgianni has established himself as a prominent researcher in design methodology, with particular expertise in TRIZ (Theory of Inventive Problem Solving) and Additive Manufacturing applications. Dr. Borgianni earned his Master's Degree in Mechanical Engineering from the University of Florence in 2005, followed by a Ph.D. in Industrial Engineering from the same institution in 2014. His academic journey has led him to specialize in the intersection of design creativity, sustainable development, and advanced manufacturing technologies, positioning him at the forefront of innovative research in engineering design. Professor Borgianni's research interests span value innovation, ideation within engineering design, creative product development, industrial process re-engineering, and computer-aided innovation. He has made significant contributions to understanding how sustainability principles can be effectively integrated into product design processes. His work often combines theoretical frameworks with practical applications, particularly exploring the potential of Additive Manufacturing for sustainable production. Borgianni has also investigated user experience aspects in design evaluation through advanced methodologies like eye-tracking and virtual reality. His recent publications reveal a strong focus on sustainable design education, manufacturing process selection using machine learning, circular economy implementation in product development, and the application of TRIZ for environmental problem-solving. Borgianni's work demonstrates a consistent trend toward integrating digital technologies with sustainable design principles, with increasing attention to circular economy metrics and the human factors involved in sustainable product evaluation. While specific scientific awards are not prominently mentioned in the available information, Professor Borgianni's extensive publication record (over 160 publications) and his role as editor for special issues on design processes indicate significant recognition within his field. His work has been cited over 50,000 times according to ResearchGate metrics, reflecting substantial impact in engineering design research. Professor Borgianni actively supervises research projects and mentors students in the Faculty of Engineering at unibz. His research has been supported by various grants focused on sustainable product development, additive manufacturing applications, and design methodology innovation. He collaborates extensively with researchers across Europe, particularly in Italy, Germany, and the UK, contributing to international projects that bridge academic research with industrial applications in sustainable manufacturing. At the Free University of Bozen-Bolzano, Borgianni is involved in research initiatives related to sustainable engineering design, working with interdisciplinary teams that combine expertise in mechanical engineering, computer science, and environmental science. His work with the university's engineering faculty contributes to the institution's focus on innovative, sustainable technologies and their practical implementation in industry.

• Semantic Web
• Knowledge Representation and Reasoning
• Artificial Intelligence
• +5 more

Floriano Scioscia is an Associate Professor at the Polytechnic University of Bari, Italy, specializing in Information Processing Systems (SSD ING-INF/05). His research bridges Semantic Web technologies, Knowledge Representation, and Artificial Intelligence to develop innovative solutions for Internet of Things, Blockchain, and smart city infrastructures. His primary research domains include Semantic Web, Knowledge Representation and Reasoning, Internet of Things, Blockchain, Edge Computing, and Artificial Intelligence. He applies these to solve critical problems in smart mobility (e.g., blockchain-enhanced ridesharing), last-mile logistics optimization, energy infrastructure management, and healthcare decision support. His work consistently focuses on creating lightweight, scalable frameworks like Tiny-ME for resource-constrained environments and semantic-enhanced platforms for real-world system integration. Analysis of his 2023-2025 publications reveals a dominant trend toward edge-centric semantic technologies. Key themes include OWL reasoning on constrained devices (Tiny-ME Wasm), blockchain integration for transparent service platforms (RideMATCHain), and AI-driven optimization for logistics/smart cities. His work demonstrates exceptional cross-domain applicability, with consistent emphasis on explainability, scalability, and practical implementation in transportation, energy, and healthcare infrastructure. The publications show strong synergy between theoretical knowledge representation advances and deployable system architectures.