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Javad ZarrinView profile

Senior Lecturer

• Artificial Intelligence
• Distributed Systems
• Graph Neural Networks
• +2 more

Javad Zarrin is Senior Lecturer in Artificial Intelligence at Abertay University's School of Design & Informatics. With a PhD in Computer Science from University of Aveiro, he specializes in distributed systems, AI, and machine learning applications. His research program focuses on: Graph neural networks for behavior analysis Cybersecurity threat detection AI applications in environmental monitoring Blockchain decentralization frameworks Deep learning for medical imaging Dr. Zarrin teaches courses on Data Structures, Algorithms, and Artificial Intelligence, and supervises postgraduate research in machine learning and distributed systems. He has contributed to EU and UK research projects including FP7, Horizon 2020, and EPSRC initiatives, maintaining collaborations with Cambridge University and Anglia Ruskin University.

Michael KochView profile

Professor

• Human-Computer Interaction (HCI)
• Computer-Supported Cooperative Work (CSCW)
• Collaboration Technology
• +3 more

Michael Koch is a Professor of Human-Computer Interaction (HCI) at the University of Bundeswehr Munich in Germany. He holds a doctorate (PhD) and Habilitation in Informatics from TU München. His research focuses on collaboration technologies, socio-technical systems design, and integrating user interface innovations for team and community support. He leads the Cooperation Systems Center Munich (CSCM) and chairs the HCI Special Interest Group in the German Computing Society (GI). He has held industry roles at Xerox Research Centre Europe and academic positions at TU München, University of Dortmund, and University of Bremen. Education: PhD in Informatics, TU München (1997) Habilitation in Informatics, TU München (2003) Research Interests: Designing usable systems for work and leisure contexts Computer-Supported Cooperative Work (CSCW) Socio-technical system integration for team collaboration Human-Computer Interaction innovations Professional Roles: Fellow of the German Computing Society (GI) Member of ACM SIGCHI Chapter and EUSSET boards Editorial roles in i-com Journal and Lecture Notes in Informatics Advising & Collaboration: Guided over 10 PhD/Master’s students Active in European cyber defense research through Forschungszentrum Cyper Operations His research bridges technical innovations with human-centric design principles, emphasizing real-world applicability in enterprise and community settings.

• Machine Learning
• Computer Vision
• Safety-Critical Systems
• +7 more

Ran Wei is a researcher at the University of Science and Technology of China, Department of Chemistry, with extensive contributions across interdisciplinary domains. His work bridges Computer Science , Biomedical Imaging , and Transportation Systems , focusing on active inference models, digital twins, and multimodal learning. Academic Affiliation : University of Science and Technology of China, Department of Chemistry Research Themes : Ran Wei's research explores active inference for adaptive systems, multimodal fusion in computer vision, and domain-specific knowledge graphs for engineering applications. He also investigates federated learning on heterogeneous networks and automated safety analysis for cyber-physical systems. Recent Article Trends : His publications from 2025–2021 span AI-driven engineering (e.g., construction project management), biomedical imaging (lesion detection, radiomics), and transportation systems (driver behavior modeling). Methodologically, he integrates transformers , Bayesian inference , and active learning frameworks. Scientific Collaborations : Ran Wei collaborates with experts in autonomous vehicles (Alfredo García, Anthony D. McDonald), biomedical engineering (Qiaolin Ye, Yifan Cai), and safety-critical systems (Tim Kelly, Simon Foster).

Georgios KornarosView profile

Associate Professor

• Digital Design
• Computer Architecture
• Embedded Systems
• +5 more

Georgios Kornaros is an Associate Professor at the Department of Electrical and Computer Engineering (E.M.E.) of the Hellenic Mediterranean University (EL.ME.PA), specializing in Digital Design. He directs the ISCA Lab (Intelligent Systems and Computer Architecture Laboratory) under the School of Engineering. His teaching responsibilities include courses on Computer Organization, Digital Circuits, Computer Architecture, and Embedded Systems. Education: PhD, Electronic and Computer Engineering, Technical University of Crete (2013) Master's Degree, Computer Science, University of Crete (1997) Diploma in Engineering, Computer Engineering and Informatics, Technical University of Patras (1992) Research Interests: Multicore/heterogeneous architectures, embedded systems, cyber-physical systems, energy-efficient and secure processing. His work includes contributions to EU projects like VIRTICAL, SAVE, TRESCCA, DREAMS, TAPPS, and AVANGARD. He has published over 70 papers and edited the book “MultiCore Embedded Systems” (CRC Press). Professional Experience: Collaborated with the Institute of Informatics (ITE) on VLSI system design, led the Digital Integrated Systems group at ISD SA, and worked on network processor architectures at Ellemedia Technologies. He contributed to projects like EU/FP6 Micro2DNA and Lab-On-Chip-ATEI-CRETE. Labs/Teams: Director of the ISCA Lab, focusing on advanced embedded systems and cyber-physical systems design.

• Internet of Things (IoT)
• Wireless Sensor Networks
• Cyber-Physical Systems
• +3 more

Fernando Boavida is a Professor at the University of Coimbra, Portugal, specializing in Internet of Things (IoT), Cyber-Physical Systems (CPS), and Human-in-the-Loop systems. His work focuses on integrating privacy-preserving technologies, sustainable smart city solutions, and industrial IoT frameworks. Research Interests : IoT and Wireless Sensor Networks Cyber-Physical Systems (CPS) Data Privacy and Security Human-in-the-Loop Systems Industrial IoT and Federated Learning Smart City Sustainability Key Contributions : Developed models for Human-in-the-Loop CPS, emphasizing privacy and edge computing. Advanced federated learning frameworks for LoRaWAN-enabled IIoT. Designed systems for unobtrusive healthcare sensing and pandemic response. Published extensively on IoT management protocols and industrial automation. Grants and Projects : Co-PI on projects addressing IoT integration in construction and healthcare sectors. Contributed to EU-funded initiatives on smart city sustainability and privacy preservation. Teams and Labs : Boavida leads interdisciplinary teams at the University of Coimbra, collaborating with industry partners to deploy real-world IoT solutions, including the iSenior elderly support system and iHorse equine monitoring platform.

Lianhua ChiView profile

Lecturer

• Big Data Analytics
• Artificial Intelligence in Healthcare
• Machine Learning
• +7 more

Lianhua Chi is a Lecturer in the Department of Computer Science and Information Technology at La Trobe University, Melbourne, Australia, since February 2018. He holds a dual PhD in Machine Learning and Data Mining from the University of Technology Sydney (Australia) and Huazhong University of Science and Technology (China). Before joining academia, he worked as a Postdoctoral Researcher at IBM Research Australia on Watson Education and Health, and as a Data Specialist at the University of New South Wales (UNSW). His research focuses on big data analytics, particularly in healthcare applications, AI-driven Autism Screening, and hashing techniques for real-time data processing. Chi has received notable awards including the 'Young Global Changer' title from G20/T20 Germany, 'Top 200 Young Researchers Globally' by the Heidelberg Laureate Forum, and the IBM External Honors award. Education: PhD in Machine Learning and Data Mining (2015), University of Technology Sydney & Huazhong University of Science and Technology Previous roles: Postdoc at IBM Research Australia, Visiting Researcher at UTS Research Interests: Chi’s work emphasizes adaptive hashing algorithms, graph stream classification, geolocation prediction in social media, and time series analysis. He applies these techniques to healthcare challenges such as AI-based Autism Screening and medical imaging diagnostics. His methodologies prioritize scalability and real-time adaptability. Awards & Grants: Best Paper Award (PAKDD13) Romberg Grant (Heidelberg Laureate Forum, 2016) Innovation Connection Grant (NostraData, 2022–2023) Advising & Collaborations: Chi has collaborated with Harvard Medical School’s Gehlenborg Lab and led projects on healthcare AI and cybersecurity. His work integrates advanced hashing techniques with clinical data to improve diagnostic accuracy and patient care efficiency. Labs/Teams: Active in interdisciplinary teams focusing on AI ethics, healthcare analytics, and big data security at La Trobe University.

• Digital Transformation
• E-Learning and Technology-Enhanced Learning (TEL)
• Applied Artificial Intelligence
• +4 more

Fisnik Dalipi is an Associate Professor of Informatics at Linnaeus University (LNU), Sweden, and holds an Associate Professorship in Information Systems at the University of South-Eastern Norway. He specializes in digital transformation, e-learning technologies, AI-driven solutions, and blockchain applications in education and industry. His research bridges computational methods with societal needs, focusing on sustainable data management, technology-enhanced learning, and privacy-preserving systems. Current Affiliations: Department of Informatics, Faculty of Technology at LNU Research Groups: Data-driven Business Innovation (DBI), Interaction Design Research Group, Linnaeus University Systems Community (LNU-STC) Key research areas include: - Digital transformation across agriculture, education, and manufacturing - Blockchain-based systems for academic credentials and product passports - AI applications in education, security, and healthcare - Human-computer interaction and inclusive technologies for special needs He leads EU/VINNOVA-funded projects like STELLA, APPEND, ENTRUST, and FOREST 4.0, aiming to enhance digital solutions in sectors like furniture industry and agriculture. His work emphasizes secure data practices and AI ethics.

• Information Security
• Information Quality
• Systems Thinking
• +7 more

Dr. Patrik Elm is an Associate Professor at Linnaeus University specializing in information security and systems thinking. He holds a PhD in Computer Science from Blekinge Institute of Technology (2007). His research focuses on information security in public sector organizations, organizational complexity, and the interplay between human and technical factors in information systems. Dr. Elm has held leadership roles including department head and has led projects like RAPID (strengthening online education in Pakistan) and DI-PHDICTKES (developing ICT PhD programs in Kosovo). He co-supervises PhD students and reviews for international journals/conferences. His work bridges systems theory with practical governance models, particularly addressing GDPR compliance and data flow management. Education: PhD in Computer Science (2007) from Blekinge Institute of Technology. Leadership Experience: Department head roles, international university collaborations, and organizational development initiatives. Projects include: RAPID: Replicating pandemic-era distance learning practices in Pakistan. DI-PHDICTKES: Establishing ICT PhD programs in Kosovo. Seed Projects: Addressing challenges in lifelong learning and patient empowerment limits. Research Interests: Cybersecurity compliance, public sector IT governance, systems thinking pedagogy, and digital strategy inefficiencies. Publications emphasize secure data governance, pandemic-era educational shifts, and interdisciplinary systems approaches. Grants & Funding: Not explicitly listed but implied through project involvements. Labs/Teams: Involved in Linnaeus Knowledge Environments like Digital Transformations. Advising: Mentors multiple PhD students and participates in exam committees.

Boxiang DongView profile

Associate Professor

• Cybersecurity
• Big Data Analytics
• Deep Learning
• +4 more

Boxiang Dong is an Associate Professor in the Department of Computer Science at Montclair State University. He holds a Ph.D. in Computer Science from Stevens Institute of Technology (2016) and a Bachelor's degree from Dalian University of Technology (2011). His research focuses on the intersection of cybersecurity and big data analytics, including deep learning applications in intrusion detection, causal inference in environmental change, and privacy-preserving data computations. Education: Ph.D., Stevens Institute of Technology (2016) Bachelor of Science, Dalian University of Technology (2011) Research interests emphasize cybersecurity (e.g., intrusion detection, privacy-preserving techniques) and big data analytics (e.g., anomaly detection, causal inference). Notable contributions include secure data outsourcing frameworks, verified deep learning systems, and patent applications on intrusion detection. Awards include Best Paper Awards at IRI 2017 and KDD Workshop 2019, and the NEC Excellent Invention Award 2017. Recent projects include NSF-funded REU Site grants for cybersecurity education. His work spans 47+ publications across journals like IEEE Transactions and conferences like Big Data, ECML-PKDD, and ICDM.

• Computer Science
• Formal Methods
• Programming Languages
• +3 more

Florian Mihola is a PreDoc Researcher at TU Wien's Institute of Computer Engineering, part of the Faculty of Informatics. His work focuses on formal methods applied to low-level programming, including type systems for assembly languages. He teaches the 'Operating Systems' course (191.002) as a VU lecture. Education: Holds a Dipl.-Ing. (engineering diploma) and BSc degree. His research emphasizes rigorous verification techniques for systems software, with a recent publication on bit-level type systems in assembly languages. Research interests include formal methods, programming language design, and their application to cyber-physical systems. No notable scientific awards have been listed. Currently affiliated with the Cyber-Physical Systems group at TU Wien, contributing to foundational research in systems software verification.

• Formal Methods
• Cyber-Physical Systems
• Temporal Logic
• +3 more

Eleonora Nesterini is a PreDoc Researcher at TU Wien's Faculty of Informatics, specializing in Cyber-Physical Systems. She is affiliated with the E191-01 department and involved in the ProbInG project (2020–2025). Her research focuses on formal methods for system verification, particularly mining temporal logic specifications and applying them to safety-critical systems. She holds an ORCID identifier: 0000-0002-1229-5331 . Her work bridges theoretical computer science with practical applications in predictive safety monitoring and multi-class classification systems. Recent projects include developing tools like ShapeIt for automated specification generation and analyzing hyperproperties in cyber-physical systems. Eleonora's research has been published in venues such as ACM Transactions on Embedded Computing Systems and Runtime Verification conferences. She actively contributes to advancing formal methods through interdisciplinary approaches combining machine learning and temporal logic frameworks.

• Cybersecurity
• Information Security
• Software Engineering
• +3 more

Aakanksha Saha is a PreDoc Researcher at the Department of Security and Privacy, Faculty of Informatics, Technical University of Vienna. Her research focuses on cybersecurity, with emphasis on malicious document analysis, advanced persistent threat (APT) attribution, and source code security. She contributes to projects like IoTIO (2020–2025), exploring security challenges in interconnected systems. Education: Bachelor of Science Master of Science Research Interests: Saha investigates methods to systematically detect and analyze malicious documents, automate APT campaign attribution using heterogeneous data sources, and reduce false positives in secret detection within source code. Her work integrates machine learning and software engineering principles to address real-world cybersecurity vulnerabilities. Publications: Focus on cutting-edge topics like APT analysis, secret exposure in repositories, and automated threat detection systems. Recent work emphasizes practical solutions for enterprise security challenges. Awards/Grants: No awards explicitly listed, but contributes to ongoing IoTIO project (2020–2025). Teams/Labs: Engaged with TU Wien’s Security and Privacy research group, collaborating on projects involving malware analysis and defensive strategies.

• Formal Methods
• Machine Learning Verification
• Natural Language Processing
• +1 more

Martin Tappler is a Postdoctoral Researcher in the Cyber-Physical Systems group at TU Wien's Faculty of Informatics. His work focuses on formal methods, scalable testing for AI systems, and NLP model interpretability. Publications include studies on generative automata models, importance-driven testing for reinforcement learning, and semantic analysis of RNNs. He contributes to the TAIGER project (2023-2027) on AI-driven systems.

• Cybersecurity
• Blockchain
• Internet of Things (IoT)
• +3 more

Kotzanikolaou Panayiotis is an Associate Professor at the Department of Informatics , University of Piraeus, where he serves as Director of the Laboratory of Security. His work focuses on cybersecurity, blockchain, IoT, and smart grids. Academic Rank: Associate Professor Department: Informatics University: University of Piraeus His research spans cybersecurity , blockchain , and IoT security , with over 70 publications and 1700+ citations (h-index=23). He has participated in EU projects and contributed to critical infrastructure protection methodologies. Recent publications highlight trends in Wi-Fi security , UAV vulnerabilities , blockchain protocols , and smart grid resilience , reflecting his expertise in securing next-generation networks. He holds certifications such as CISSP and ISO 27001 Lead Auditor , and has served as guest editor for cybersecurity journals.

Pantelis FrangoudisView profile

Researcher

• Mobile/Wireless Networking
• Edge Computing
• Network Softwarization
• +4 more

Pantelis Frangoudis is a PostDoctoral Researcher at the Distributed Systems Group of the Vienna University of Technology since 2019. His academic journey includes a Ph.D. in Computer Science (2012), M.Sc. (2005), and B.Sc. (2003) from the Department of Informatics, Athens University of Economics and Business (AUEB) , Greece. His research spans Edge Computing , IoT , Network Softwarization , Federated Learning , and Wireless Networking , focusing on adaptive systems and security. Education B.Sc. in Computer Science (2003), AUEB M.Sc. in Computer Science (2005), AUEB Ph.D. in Computer Science (2012), AUEB His research explores Edge Computing Continuum systems, with emphasis on QoS-aware orchestration , serverless edge deployment , and self-adaptive IoT services . Recent work addresses federated learning for 6G , WebAssembly for edge serverless , and dynamic resource allocation in heterogeneous environments. Key projects include AloTwin (2023–2025), INTEND (2024–2026), and EDENSPACE (2019–2022). His publications (2025–2020) cover machine learning for 5G/6G , MEC application orchestration , and IoT security , with a focus on distributed systems and edge-cloud collaboration . Scientific Awards ERCIM Alain Bensoussan Post-Doctoral Fellowship He has supervised multiple diploma theses at TU Vienna, including topics like serverless edge computing , traffic light AI optimization , and vertical farming IoT systems . His collaborations with institutions like EURECOM (France) and IRISA/INRIA (France) reflect a global academic footprint.