Find Academic Supervisors

Ralf BuschermöhleView profile

Professor

• Business Informatics
• Machine Learning
• Software Development
• +6 more

Prof. Dr. Ralf Buschermöhle is a full professor at the Faculty of Management, Culture and Technology of Osnabrück University of Applied Sciences. He leads research in Business Informatics , with a focus on Machine Learning , Digital Twins , and Process Mining . His work bridges theoretical modeling and industrial applications. Academic Affiliation: Hochschule Osnabrück (since 2016) Prior Roles: Senior Scientist at OFFIS (2001-2011), Founder of Demand2Offer (2011-2016) Research Interests: His research centers on optimizing industrial processes through machine learning integration, with recent projects like BiCoNet (cross-border data-driven business development) and EDNA (edge data management in sustainable manufacturing). He has secured funding from BMBF, BMWi, and Interreg programs. Publication Trends: His 15 most recent articles (2003-2024) demonstrate expertise in Reinforcement learning for production scheduling Neural networks in manual labor recognition Meta-modeling frameworks Formal verification of critical systems Success factors in IT projects Technology Transfer: Actively involved in third-party research and industrial collaborations, particularly with companies like ROSEN Group and Krone Business Center GmbH. Leads the Binnenforschungsschwerpunkt SPINE (Faculty of Management, Culture and Technology) and the Mittelstand 4.0 Kompetenzzentrum Lingen initiative.

Marzan Tasnim OyshiView profile

Researcher

• Immersive Visualization
• Virtual Reality
• Augmented Reality
• +12 more

Marzan Tasnim Oyshi is a Research Associate at the Leibniz Institute of Ecological Urban and Regional Development (IOER) and a Doctoral Candidate at the Chair of Computer Graphics and Visualization, Technische Universität Dresden, Germany. Her research focuses on immersive visualization, virtual reality, and data-driven decision-making for sustainability and environmental challenges. Education: Master of Science in Computer Science & Engineering, Daffodil International University, Bangladesh (2017–2019) Bachelor of Science in Computer Science & Engineering, Daffodil International University, Bangladesh (2013–2016) Erasmus Mundus Undergraduate Mobility Exchange, Information Technology, Lodz University of Technology, Poland (2015–2016) Her research interests include immersive visualization, virtual reality, IoT, machine learning, and environmental data analytics . She develops tools that support decision-making in climate resilience, flood risk, and urban sustainability using VR-based visual analytics. Her work bridges computer science with real-world environmental and societal challenges. The recent publications demonstrate a strong trend in immersive data visualization for environmental science and machine learning support . Oyshi’s work applies VR to visualize extreme weather, flood projections, and biological datasets, while also contributing to IoT-based monitoring and gamified education. The interdisciplinary nature of her research spans computer vision, sustainability, and human-computer interaction. Scientific Awards and Scholarships: 300 Army Scholarship (2017) Erasmus Mundus UG Mobility Scholarship (2015) Government District-Level Scholarship (2010) Best Debater, Transparency International Bangladesh (2010) Multiple Government Academic Scholarships (2005, 2008) Oyshi has actively contributed to academic advising, having supervised 2 Master’s theses, 2 Bachelor’s theses, 3 team projects (15 students), and 9 Hauptseminar students . She has also served as a reviewer for prestigious conferences including CHI, IEEEVIS, and ICAITA. Her research is supported by institutional affiliations with IOER and ScaDs.AI, focusing on systemic sustainability and data science. She is a key member of the Leibniz-Lab 'Systemic Sustainability' , where she contributes to developing dashboards that communicate science-society challenges related to climate change, biodiversity loss, and food security. Her projects like ExtremeWeatherVis, FloodVis, and VRCellLabeler exemplify her commitment to creating immersive, interactive tools for complex data understanding.

Fabian UnruhView profile

Academic

• Nuclear Disarmament Verification
• Machine Learning
• Nuclear Archaeology
• +3 more

Fabian Unruh is a Doctoral Researcher at the Peace Research Institute Frankfurt (PRIF) , affiliated with the Cluster for Natural and Technical Science Arms Control Research (CNTR) . His work focuses on nuclear disarmament verification and machine learning applications in nuclear archaeology for reconstructing fissile material production histories. He previously held a postgraduate assistant role at RWTH Aachen University’s Nuclear Verification and Disarmament group (2023–2024), following academic achievements including a Master of Physics (2021–2023) and Bachelor of Physics (2018–2021) from RWTH Aachen. Research Interests include: Nuclear archaeology and verification methodologies AI/ML integration in arms control contexts Nonproliferation and nuclear security frameworks Policy implications of emerging technologies He actively participates in interdisciplinary initiatives such as the AMC Cross-Disciplinary Conference 2025 and the Princeton School on Science and Global Security , collaborating with networks like the PEACE Network (COST Action) and the Science for Nuclear Diplomacy research group. Fabian also contributes to public outreach, including the Book A Scientist event organized by the Leibniz Association.

• Graph Neural Networks
• Time Series Analysis
• Anomaly Detection
• +5 more

Cesare Alippi is a Professor at Politecnico di Milano in the Department of Electronics, Information and Bioengineering within the School of Industrial and Information Engineering. With an extensive publication record spanning over three decades from 1991 to 2025, he has established himself as a leading researcher in machine learning, particularly in graph neural networks, time series analysis, and anomaly detection. His research interests focus on developing advanced machine learning methodologies for time series analysis, graph-based learning, and anomaly detection systems. Professor Alippi's work bridges theoretical foundations with practical applications across various domains including smart systems, healthcare, and cybersecurity. His recent publications demonstrate a strong emphasis on graph-based approaches to time series forecasting, structural anomaly detection, and spatiotemporal modeling. His publication trends from 2023-2025 reveal a concentration on graph neural networks for time series applications, with significant contributions to forecasting, anomaly detection, and representation learning. These works often address challenges in spatiotemporal data, missing values, and structural pattern recognition, demonstrating both theoretical depth and practical utility. Professor Alippi has mentored numerous researchers who have become prominent collaborators, including Andrea Cini, Daniele Zambon, and Lorenzo Livi, among others. His collaborative network extends across multiple institutions and research domains.

• Social Network Analysis
• Trust Modeling
• Criminal Network Analysis
• +7 more

Pasquale De Meo is a Professor at the Department of Computer Engineering, Modeling, Electronics and Systems at Mediterranea University of Reggio Calabria, Italy. With over 178 publications spanning from 2003 to 2025, he has established himself as a leading researcher in social network analysis, trust modeling, and complex systems. His work frequently appears in top-tier journals including IEEE Transactions, Expert Systems with Applications, and Knowledge-Based Systems. De Meo's research focuses on social network analysis, trust modeling, criminal network analysis (particularly Sicilian Mafia operations), graph theory, and machine learning applications to networks. His work combines theoretical network science with practical applications in security, recommendation systems, and data mining. He has pioneered approaches for identifying key nodes in criminal networks, developing trust prediction models, and creating robust community detection algorithms. His recent publications (2023-2025) demonstrate a strong trend toward integrating quantum-inspired methods with traditional network analysis, developing advanced recommendation systems that address cold-start problems, and applying deep learning techniques to complex network structures. His work bridges theoretical computer science with practical security applications, particularly in fraud detection and criminal network disruption. De Meo has collaborated extensively with researchers including Giacomo Fiumara (61 papers), Domenico Ursino (57 papers), Giovanni Quattrone (40 papers), and Emilio Ferrara (37 papers), forming a productive research network focused on complex systems and network science.

• Blockchain Technology
• Sustainable Energy Systems
• Data Management
• +7 more

Jorge Marx Gómez is affiliated with the Carl von Ossietzky University of Oldenburg , Germany. As a researcher, he contributes to interdisciplinary domains spanning blockchain technology , sustainable systems , artificial intelligence , and smart agriculture . His research explores blockchain applications for regulatory compliance (e.g., electronic bills of lading, GDPR-enforcing systems), machine learning for internal auditing and environmental data analysis, and computer vision in precision livestock farming. Recent work includes 5G-enabled smart farming dashboards and environmental information portals with citizen participation. Analysis of his 15 most recent publications reveals a focus on technology-driven sustainability , combining NLP , data lakes , and multimodal models to optimize resource efficiency, transportation safety, and agricultural practices. Trends emphasize cross-sectoral integration of emerging technologies. His collaborations span institutions in Germany and Latin America, with co-authors like Hauke Precht , Felix Kruse , and Manuel Mora Tavarez . Publications appear in venues such as EnviroInfo , HICSS , and CENTERIS .

• Human-Computer Interaction
• Wearable Computing
• Sensor Technology
• +3 more

Kazuya Murao is an active researcher in human-computer interaction , wearable computing , and sensor technology , affiliated with Osaka University's Department of Information and Computer Science . His work focuses on leveraging PPG sensors , accelerometers , and acoustic sensing for biometric authentication , gesture recognition , and physiological signal processing . Recent publications highlight his exploration of smart ring authentication (2025), PPG-to-EMG conversion (2023), and non-invasive sensor attacks (2023). His research combines signal processing , machine learning , and user perception manipulation through sEMG feedback and tactile stimuli (2022). Key subfields : Biometric authentication, Sensor data analysis, Wearable interface design, Human activity sensing, Gesture recognition, Physiological modeling

• Network Management
• Cognitive Radio Networks
• Internet of Things (IoT) Security
• +7 more

Francine Krief is a Professor with extensive research in network management , QoS optimization , and context-aware security across cognitive radio networks, IoT, and vehicular systems. Her work integrates blockchain and fuzzy logic for secure, efficient resource allocation. Key Research Areas : Network Security, Cognitive Radio, IoT, Edge Computing Collaborations : Active partnerships with researchers like Mohamed Aymen Chalouf, Badr Benmammar, and Tidiane Sylla Publication Trends : Over the past decade, Krief has focused on blockchain applications in IoT security (2021), energy-efficient cognitive radio (2019), and SDN-based vehicular networks (2019). Recent work (2025) addresses user-centric IoT service placement in edge infrastructures.

• Software Engineering
• Requirements Engineering
• Functional Size Measurement
• +5 more

Carmine Gravino is a Professor in the Department of Computer Science at the University of Salerno, Italy, with an extensive publication record spanning over two decades from 2001 to 2025. His academic career demonstrates consistent research productivity with numerous publications in top software engineering venues including IEEE Transactions on Software Engineering, Empirical Software Engineering, and Information and Software Technology. Gravino's research spans multiple domains within software engineering and its intersection with emerging technologies. His primary research interests include software measurement (particularly COSMIC functional size measurement), requirements engineering, code quality analysis, and the application of artificial intelligence to software engineering problems. In recent years, he has expanded his research into educational technology, focusing on the development of educational metaverses and the integration of generative AI in learning environments. His work also extends to healthcare applications, particularly in diabetes prediction using machine learning techniques. The trajectory of Gravino's recent publications reveals a strategic evolution from traditional software engineering topics toward the integration of cutting-edge AI technologies. His 2023-2025 works prominently feature large language models applied to functional size measurement, requirements engineering, and code analysis. Simultaneously, he has developed a significant research stream in educational technology, creating frameworks for educational metaverses that incorporate ethical considerations for emotion recognition and AI integration. His healthcare-related work demonstrates interdisciplinary collaboration, applying software engineering rigor to medical prediction problems. Gravino maintains an active research group with frequent collaborations, particularly with Filomena Ferrucci (69 joint publications), Genny Tortora (33), Giuseppe Scanniello (30), Vincenzo Deufemia (29), and Michele Risi (29). His work appears in both journal and conference venues, with a strong emphasis on empirical validation through controlled experiments and case studies. His research has practical applications in software project management, requirements engineering processes, and educational technology development.

• Cultural Algorithms
• Evolutionary Computation
• Artificial Intelligence
• +5 more

Robert G. Reynolds is a Professor of Computer Science at Wayne State University's College of Engineering, where he leads pioneering research in Cultural Algorithms and evolutionary computation. With over 197 publications spanning from 1982 to 2024, he has established himself as a leading authority in computational intelligence and socially-inspired optimization techniques. His work bridges computer science, social modeling, and practical applications across diverse domains including education, healthcare, and archaeology. Reynolds' research interests center around Cultural Algorithms, which he developed as a framework for modeling how cultures solve problems through social learning and knowledge transmission. His work extends to evolutionary computation, swarm intelligence, and agent-based modeling, with applications ranging from virtual reality educational systems to medical imaging analysis. He has published the influential book 'Culture on the Edge of Chaos - Cultural Algorithms and the Foundations of Social Intelligence' (2018) that formalized his theoretical contributions. His recent publications (2022-2024) demonstrate continued innovation in applying Cultural Algorithms to diverse challenges including STEM education through virtual reality environments, medical image analysis, economic modeling, and climate change impact assessment. These works consistently show his approach of integrating social dynamics into computational problem-solving frameworks. Among his notable scientific contributions is the development of the Cultural Algorithms framework as a comprehensive approach to optimization and problem-solving that incorporates social dimensions often missing in traditional evolutionary algorithms. His work has influenced numerous researchers in the field of computational intelligence. Professor Reynolds has mentored numerous students who have become frequent collaborators, including Mostafa Z. Ali, Thomas Palazzolo, Sarah Saad, and Chencheng Zhang. His research has been supported by various grants that have enabled the development of projects like the Deep Dive Virtual Reality system for exploring ancient submerged civilizations and educational applications of AI. His laboratory work focuses on developing the Cultural Algorithms Toolkit (CAT) and applying it to real-world problems through virtual environments that simulate social and cultural dynamics. Current projects include extending Cultural Algorithms for trustable AI systems and developing more sophisticated models of cultural knowledge transmission.

• Network Security
• IoT Security
• Machine Learning for Security
• +5 more

Michele Nogueira Lima is a Professor at the Federal University of Parana, Curitiba, Brazil, with an extensive publication record in network security, IoT security, and healthcare networks. Her research spans from 2008 to 2025 with over 200 publications, demonstrating her sustained contribution to the field of computer networking and security. Her research interests focus on network security with particular emphasis on Distributed Denial of Service (DDoS) attack detection and prediction, IoT security, privacy protection in smart environments, molecular communications, and healthcare network security. She employs advanced techniques including machine learning, federated learning, and deep learning to address security challenges in modern networked systems. Analysis of her recent publications (2023-2025) reveals a strong trend toward applying cutting-edge AI techniques to network security problems, particularly transformer models for DDoS detection, federated learning approaches for privacy-preserving security, and zero trust architectures for next-generation networking. Her work bridges theoretical security concepts with practical implementations in healthcare and IoT contexts. Michele has collaborated extensively with researchers across Brazil and internationally, with Aldri Luiz dos Santos being her most frequent collaborator (82 joint publications). Her work appears in top-tier venues including IEEE journals, GLOBECOM, and LATINCOM conferences. She has contributed significantly to both theoretical frameworks and practical implementations in network security, with applications spanning from molecular communications to large-scale IoT networks. Her research demonstrates a consistent focus on developing autonomous, intelligent security systems that can adapt to evolving threats.

• Database Systems
• Data Stream Processing
• Uncertain Data Management
• +4 more

Tingjian Ge is an academic researcher with a focus on database systems, graph stream processing, and uncertain data management. His work spans theoretical foundations and practical implementations in data science, with a career trajectory showing increasing specialization in real-time network analytics and secure data processing. PhD from Brown University (2009) Active in top-tier venues: ICDE, KDD, VLDB, WWW Research Interests include: Approximate query processing under resource constraints Graph stream modeling and edge shedding techniques Security and privacy in data systems Temporal network analysis and predictive modeling Optimization of multi-core and cloud-based query execution Scientific Contributions demonstrate expertise in: Developing sketch data structures for graph streams Creating fairness-aware prediction frameworks Advancing differential privacy mechanisms Designing efficient temporal subgraph algorithms

Mortaza Shoae BarghView profile

Professor

• Privacy
• Cybersecurity
• Data Governance
• +3 more

Mortaza Shoae Bargh is a Professor in Privacy & Cybersecurity at Rotterdam University of Applied Sciences (Rotterdam UAS), where he leads research at the intersection of data governance, machine learning, and regulatory compliance. His work addresses critical challenges in secure data handling within public institutions, smart cities, and emerging technologies like IoT. His research spans: Privacy-preserving technologies for GDPR compliance and personal data minimization Explainable AI systems for security applications and bias mitigation Statistical disclosure control methods for open data initiatives Zero-trust architectures in specialized domains like smart shipping Human-centric approaches to cybersecurity dashboard design Recent publications reveal a strong trend toward practical implementations of privacy-by-design frameworks, with increasing focus on societal impacts of AI (e.g., ChatGPT stereotypes) and cross-domain applications of machine learning in judicial systems and healthcare. His work consistently bridges technical innovation with regulatory requirements, emphasizing data utility preservation alongside security. Professor Shoae Bargh actively contributes to cybersecurity discourse through publications spanning journals, conferences, and book chapters, with notable emphasis on real-world applicability in governmental contexts. His research outputs demonstrate sustained engagement with evolving challenges in data ethics and secure system design since 2012.

Henning TrsekView profile

Professor

• Cybersecurity
• Industrial Automation
• Internet of Things
• +3 more

Prof. Henning Trsek serves as Director of the Institute Industrial IT (inIT) at OWL University of Applied Sciences since 2024, leading research in Interconnected Automation Systems. He concurrently contributes to the Cybersecurity Training Lab, a Fraunhofer IOSB-INA collaboration, and teaches graduate courses in Network Security and Industrial Communication. His academic journey spans over two decades with deep industry integration through rt-solutions.de GmbH. Educational Background: PhD in Information Technology (2015, Otto-von-Guericke-Universität Magdeburg) MSc in Information Technology (2003-2005, Halmstad University/Aalborg University) Diploma in Electrical Engineering (2000-2003, Fachhochschule Lippe und Höxter) Trsek's research centers on industrial cybersecurity and intelligent automation , with seminal work in isochronous wireless networks and OT security frameworks . His current focus integrates large language models for automated risk assessment and TSN-based security architectures , addressing critical gaps in Industry 4.0 security validation. Recent publications demonstrate methodological evolution from protocol-specific analysis (2016-2020) toward holistic security automation (2023-2025). Analysis of his 15 most recent publications reveals dominant themes: automated security risk assessment (60% of works), industrial network security (25%), and emerging threat landscapes (15%). The research trajectory shows increasing emphasis on AI-driven security validation and modular security frameworks for flexible production systems. Scientific Recognition: Best Paper Award at ETFA 2012 Trsek actively supervises graduate theses through inIT's research projects while securing industry partnerships for applied cybersecurity initiatives. His committee leadership includes IEEE Industrial Electronics Society roles and technical program coordination for ETFA (2020-2025) and INDIN conferences. Current grants focus on AI-enhanced OT security validation through the Cybersecurity Training Lab collaboration. He directs the Interconnected Automation Systems research group at inIT, operating from Lemgo's Campusallee facility. The team maintains close ties with Fraunhofer IOSB-INA for industrial testbed development and participates in Germany's Allianz für Cybersicherheit for national security standardization.

• Case-Based Reasoning
• Artificial Intelligence
• Knowledge Management
• +5 more

Professor Klaus-Dieter Althoff is a faculty member at the University of Hildesheim, working within the Intelligent Information Systems Division of the Mathematics, Natural Sciences, Economics & Computer Science school. His research focuses on applying artificial intelligence techniques, particularly case-based reasoning (CBR), to architectural design support systems and knowledge management applications. His research interests center on Case-Based Reasoning methodologies , AI-assisted architectural design , semantic building information modeling , and knowledge representation systems . Althoff's work bridges computer science with practical applications in architecture, developing systems that automate and enhance early-stage design processes through machine learning and case-based approaches. Analysis of his recent publications (2021-2024) reveals a strong trend toward integrating deep learning with traditional case-based reasoning for architectural applications. His research increasingly focuses on autocompletion of architectural spatial configurations , BIM (Building Information Modeling) enhancement , and explainable AI for design support . The work spans theoretical CBR methodology development and practical implementations in architectural design tools. Althoff maintains active research collaborations across disciplines, with publications spanning architecture, computer science, and cybersecurity applications of case-based reasoning. His work demonstrates consistent methodological development in CBR frameworks like FLEA and SEASALT, while expanding into new application domains including network security and fitness training systems.