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Peter LewisView profile

Associate Professor

• Artificial Intelligence
• Socio-Technical Systems
• Self-Awareness
• +7 more

Peter Lewis is an Associate Dean (Research and Graduate Studies) and Associate Professor at Ontario Tech University's Faculty of Business and Information Technology, holding a Canada Research Chair in Trustworthy Artificial Intelligence. He specializes in socio-technical systems, self-aware computing, and AI ethics. His work bridges foundational AI research with practical applications in industry, emphasizing trust, collective action, and socially intelligent systems. Education PhD in Computer Science, University of Birmingham, UK MSc in Natural Computation, University of Birmingham, UK BSc in Computer Science, University of Leicester, UK PGCert in Learning and Teaching Higher Education, Aston University, UK Research Interests His research focuses on trustworthy AI, self-aware systems, and the societal implications of AI. Key areas include explainable AI for accessibility, ethical AI governance, and modeling social institutions. He co-edited Self-Aware Computing Systems and serves on editorial boards for IEEE journals. Publications & Awards Over 75 peer-reviewed papers in journals/conferences Winner of the 2020 Aston Achievement Award for Business Engagement Best Paper Award at IEEE SASO 2018 Labs & Collaborations He leads research in the Business Analytics & AI group, collaborating on projects with industry partners. His work addresses real-world challenges in AI ethics, sustainability, and human-AI collaboration.

• Travel Behaviour
• Transport Planning
• Transport Geography
• +5 more

Dr. Jonas De Vos is an Associate Professor of Transport Planning at The Bartlett School of Planning , University College London. He holds degrees in Geography , Urbanism and Spatial Planning , and a PhD in Geography from Ghent University (Belgium). His research focuses on travel behaviour, particularly the links between travel and well-being, and travel and land use. Current role: Associate Professor at UCL (2021-present) Previous roles: Assistant Professor at UCL (2019-2021), Postdoctoral Fellow at Ghent University (2015-2019) Teaching: Coordinates BPLN0104 Transport and Well-Being , BPLN0061 International Case Studies in Transport and City Planning , and contributes to other sustainable urban development modules. Research Interests include: Travel Behaviour Transport and Well-being Transport Geography Residential Self-Selection Perceived Accessibility Sustainable Mobility Intelligent Mobility Recent Publications (2024-2025): Transport Accessibility Equity - 2024 (London EV Charging) Academic Air Travel - 2024 (Behavior-Attitude Consistency) Walkability & Aging - 2024 (London Study) Travel Satisfaction Metrics - 2025 (Ease vs. Tolerance) Intercity Travel - 2025 (Place Irreplaceability) Professional Activities : Co-editor-in-chief of Transport Reviews (2021-present) Vice-chair of World Society for Transport and Land Use Research (2021-present)

Uchitha JayawickramaView profile

Senior Lecturer

• Information Systems
• ERP Implementations
• Knowledge Management
• +5 more

Dr. Uchitha Jayawickrama is a Senior Lecturer (equivalent to Associate Professor) in Information Systems at the Centre for Information Management, Loughborough Business School, Loughborough University. He holds a PhD, MBA, BSc (Hons), PgCHPE, and is a Senior Fellow of the Higher Education Academy (SFHEA). His research focuses on ERP implementations, knowledge management, AI applications in SMEs, digital transformation, and blockchain ethics. He has over 30 peer-reviewed publications and 2 best paper awards. Before academia, he worked as an Oracle Functional Consultant and IBM Solutions Manager, with expertise in ERP systems like SAP and Oracle E-Business Suite. He leads the MSc Information Systems and Digital Innovation program and manages the SAP University Alliance. His teaching spans undergraduate and postgraduate levels, and he supervises doctoral researchers. He has organized research seminars and contributed to course development in higher education. Research highlights include ERP responsiveness via big data, AI’s role in decision-making, and blockchain ethics. His work bridges academic research with industry needs, emphasizing practical solutions for SMEs and public sector organizations. Awards include recognition for innovative contributions to information systems and digital innovation. He actively reviews for journals/conferences, serves on editorial boards, and advises on ERP selection/implementation for Midlands SMEs and city councils. His interdisciplinary approach integrates technology, management, and societal impact.

• Information Systems Engineering
• Privacy Threat Modeling
• IoT Security
• +4 more

Dimitri Van Landuyt serves as an Associate Professor in the Department of Computer Science at KU Leuven , affiliated with the Information Systems Engineering Research Group (LIRIS) . He leads and co-promotes multiple high-impact research projects focused on security and privacy engineering, including initiatives on model-driven security risk analysis , privacy by design , and IoT security . His work spans GDPR compliance, synthetic data management, and threat modeling innovations. Academic Leadership : Member of the Council of FEB and Campus Council Leuven/Kortrijk Research Pillars : Privacy threat modeling, security automation, IoT systems, GDPR technical implementation His publications demonstrate expertise in privacy-enhancing technologies, with recent work analyzing LLM applications in threat modeling, developing tree-based privacy analysis frameworks, and creating adaptive trust management architectures. He explores serious games for security training, synthetic data quantification standards, and runtime threat assessment mechanisms. Dimitri contributes to educational programs through courses in ICT Service Management , Security & Privacy by Design , and Research Methodologies . He supervises student research while collaborating with industry and academia on data protection challenges.

Grzegorz J. NalepaView profile

Professor

• Artificial Intelligence
• Affective Computing
• Context-Aware Systems
• +9 more

Prof. Grzegorz J. Nalepa holds the position of Full Professor at the Faculty of Physics, Astronomy, and Applied Computer Science, Jagiellonian University, Poland. He leads the Jagiellonian Human-Centered Artificial Intelligence Lab and is involved in international projects such as the CHISTERA XPM initiative on explainable AI in predictive maintenance. His academic journey includes a PhD (2004), habilitation (2012), and a philosophy MA (2012). Research focuses on AI, affective computing, context-aware systems, and explainable AI (XAI). He has authored over 200 papers, two monographs, and edited volumes. Key projects include the BIRAFFE2 dataset for emotion-based personalization and the GEIST team's semantic wiki tools. Holds leadership roles in conferences (e.g., ECAI 2023) and serves on editorial boards of journals like Sensors and Intelligent Sensors Section . Awards include the 2018 Outstanding Monograph Prize and multiple AGH-UST Rector's Prizes for scientific achievements. Advises PhD and master students, supervising 36 MSc theses. Active in professional organizations like the Polish Alliance for AI and the IEEE Computational Collective Intelligence TC. His contributions span industrial collaborations, software tools (e.g., InXAI, LUX, KnAC), and international research networks.

• Service-Oriented Architectures
• Cloud Computing
• Data Management
• +7 more

Professor Kyriazis Dimosthenis holds a faculty position at the Department of Digital Systems, University of Piraeus. He earned his diploma in Electrical & Computer Engineering from the National Technical University of Athens (2001) and a cross-disciplinary MSc in Techno-Economic Systems (2004). His academic rank is Professor specializing in service-oriented architectures with a focus on quality of service and workflow management. He has led European projects like BigDataStack, CrowdHEALTH, and CYBELE, addressing challenges in cloud computing, edge computing, and AI-driven solutions for healthcare, finance, and industrial sectors. His research emphasizes resilient service-oriented systems, AI explainability, and human-centric digital transformation. Notable contributions include frameworks for dynamic resource allocation in hybrid cloud/edge environments, AI applications for maritime safety, and data governance solutions for cross-sector integration. He coordinates initiatives such as the Future Internet Architecture Board and Cloud QoS&SLAs, driving advancements in federated data marketplaces and sustainable computing practices. His recent work explores Large Language Model (LLM) applications in financial decision-making, conversational AI for MLOps, and neurosymbolic systems for defect detection. He also investigates XAI methodologies, including VirtualXAI, which leverages GPT-generated personas for explainability assessment. His projects often bridge technical innovation with societal impact, such as the iHELP platform for holistic health records and SmartCHANGE for behavioral change strategies in youth health. Key themes in his publications include bias mitigation in machine learning, dynamic deployment prediction in hybrid cloud settings, and energy-efficient data spaces for mobility. He has contributed to standards like the H2020-funded IRMOS and 5GTANGO, emphasizing interoperability and fault-tolerant architectures. His work frequently intersects with EU policy frameworks, particularly in data governance and ethical AI implementation.

Tijl De BieView profile

Professor

• Foundations of Data Science
• Human-Centric Data Science
• Exploratory Data Analysis
• +2 more

Tijl De Bie is a Senior Full Professor at the University of Ghent, specializing in machine learning, data science, and their applications in bioinformatics, computational social sciences, and HR analytics. He leads the AI and Data Analytics (AIDA) research group within IDLab-ELIS. PhD in Machine Learning (KU Leuven, 2005) Worked at U.C. Berkeley, U.C. Davis, University of Southampton, and University of Bristol His research focuses on foundational aspects of data science, including fairness in AI, network embeddings, and human-centric methodologies. Recent work explores temporal network simulation, bias mitigation, and large-scale career trajectory datasets. Notable awards include an FWO Odysseus Group I grant and three ERC grants (Consolidator, Proof of Concept, Advanced). Current projects involve ethical AI frameworks and dynamic network analysis. Scientific Awards : FWO Odysseus Group I, ERC Consolidator, ERC Proof of Concept, ERC Advanced Grant He collaborates extensively in interdisciplinary research, applying machine learning to social media analysis and financial domains. His team develops open-source tools like EvalNE and Fondue for network embedding evaluation.

• Software Engineering
• Probabilistic Programming
• Security and Penetration Testing
• +5 more

Jürgen Cito is an Associate Professor in the Department of Software Engineering at the Faculty of Informatics, TU Wien, where he leads research in probabilistic programming, security, and configuration management. His work is supported by major grants from the Austrian Science Fund (FWF), European Commission, and Meta Platforms, Inc., with active projects spanning 2022-2027. His research focuses on the intersection of software engineering and machine learning, particularly in static analysis of probabilistic programs, AI-driven penetration testing, and infrastructure security. Key contributions include identifying secret exposure in configuration files, grammar inference for ad hoc parsers, and performance prediction from source code, often combining empirical studies with tool development. Analysis of his 15 most recent publications (2020-2024) reveals three dominant trends: (1) Security vulnerabilities in configuration management systems, especially secret leakage in dotfiles; (2) Application of large language models to offensive security testing; and (3) Machine learning techniques for performance prediction and AutoML optimization in software contexts. Cito has supervised 22 Master's students on cutting-edge topics including AI security, infrastructure as code, and program analysis. His current research portfolio includes: Types4Strings (FWF, 2024-2027): Type systems for string processing Cloud Open Source Research Mobility Network (EU, 2023-2026): Open-source cloud infrastructure Software Assistants for Probabilistic Programming (Meta, 2022-2026): AI tools for probabilistic code He is embedded in TU Wien's Institute of Software Technology and Interactive Systems (E194), collaborating on cross-institutional projects focused on software security and developer tooling, with particular emphasis on empirical validation of security practices and configuration management systems.

• Virtual Reality (VR)
• Human-Computer Interaction (HCI)
• Accessibility in Technology
• +3 more

John Quarles is a Professor at the University of Texas at San Antonio, specializing in Virtual Reality (VR) and Human-Computer Interaction. His research focuses on accessibility in immersive technologies, cybersickness mitigation, and inclusive design. He has contributed over 100 publications across top venues like IEEE VR, ISMAR, and IEEE Transactions on Visualization and Computer Graphics. His work addresses challenges faced by users with disabilities, such as balance impairments and mobility limitations, through innovative feedback systems and adaptive algorithms. Quarles has co-authored influential papers on cybersickness prediction, VR accessibility for persons with Multiple Sclerosis, and disability simulations to reduce societal bias. He has held leadership roles including Program Chair for IEEE VR 2023, demonstrating his influence in academic and industrial VR communities. His research spans interdisciplinary applications in healthcare, education, and rehabilitation, with notable collaborations on datasets like 'Mazed and Confused' and frameworks like SmoothRide. Key themes include multimodal feedback methods, user-centric design principles, and leveraging AI for personalized VR experiences.

Zoë WaltersView profile

Associate Professor

• Epigenetics and Genomics
• Cancer Biology
• Target Prioritisation and Validation
• +3 more

Dr. Zoë Walters is an Associate Professor in Translational Epigenomics at the University of Southampton, affiliated with the Faculty of Medicine and Cancer Sciences department. She leads the MSc Genomics program's Genomics Guided Treatment and Dissertation modules and contributes to BMedSci teaching. Her research focuses on epigenetic mechanisms in cancer and developmental disorders, particularly targeting therapies for pediatric cancers like neuroblastoma and rhabdomyosarcoma. She collaborates on projects addressing therapy resistance, tumor microenvironment dynamics, and precision medicine approaches. Current research includes investigating EZH2 inhibitors in combination therapies, leveraging omic data for sarcoma treatment, and understanding mechanisms of therapeutic resistance. She has received awards such as the Norman Williams Prize (2024) and Young Investigator Award (2023). Her lab also explores AI applications in clinical decision-making for oesophageal cancer. Zoë has supervised numerous PhD students and contributed to over 10 peer-reviewed publications. She serves on editorial boards (e.g., Frontiers in Cell and Developmental Biology) and reviews for journals like Nature Communications and Clinical Epigenetics. Her work spans interdisciplinary collaborations, including with engineering and computer science teams for AI-driven oncology solutions.

Jatinder SinghView profile

Professor

• Accountability in AI Systems
• Responsible AI Development
• Data Governance and Stewardship
• +7 more

Jatinder Singh is a Professor at the RC Trust and Principal Research Associate (equivalent to Research Professor) at the Department of Computer Science & Technology, University of Cambridge. He is primarily affiliated with the University of Duisburg-Essen, Germany, where he leads the Compliant and Accountable Systems research group within the Law department. His work operates at the critical intersection of computer science, legal frameworks, and societal impact, focusing on practical implementations that align technology with regulatory requirements while addressing user and community concerns. Research interests center on accountability mechanisms for AI systems, responsible development practices, data governance, and privacy/security in emerging technologies. He examines governance, agency, trustworthiness, and transparency gaps in algorithmic systems through interdisciplinary socio-technical lenses. Current work addresses bias in LLMs, stakeholder participation frameworks, and human rights implications in domains like healthcare, maritime enforcement, and consumer IoT, emphasizing contextual awareness and real-world applicability. His 15 most recent publications (2025-2024) reveal dominant trends in AI transparency, fairness proxy development, and legal-compliance engineering. Key focus areas include stakeholder involvement in AI governance, bias mitigation in language models, data justice applications for vulnerable populations, and operationalizing human-centered AI in clinical settings. The work consistently bridges technical implementation with regulatory frameworks like the EU Cyber Resilience Act and GDPR. Scientific Awards: No awards or fellowships were mentioned in the provided text. Advising and Grants: The text does not specify PhD/Master's students or grant details. As leader of an active research group publishing high-impact work on EU regulations and human rights, he likely directs funded projects and mentors early-career researchers, though concrete evidence is absent in the source material. His position suggests involvement in interdisciplinary grant consortia addressing socio-technical challenges. Labs and Teams: Singh leads the Compliant and Accountable Systems research group at University of Duisburg-Essen, which collaborates across university-wide clusters including Artificial Intelligence and Society, Human-AI Interaction, Trustworthy Human Language Technologies, and Verification of Machine Learning. The group develops frameworks for legal compliance in AI, focusing on demonstrable accountability through tools for transparency, bias auditing, and stakeholder engagement in real-world deployments.

• Machine Learning
• Case-Based Reasoning
• Natural Language Processing
• +3 more

Nirmalie Wiratunga is a Professor in Intelligent Systems at the School of Computing , Robert Gordon University, and serves as the Associate Dean for Research . She is also an Adjunct Professor at the Norwegian University of Science and Technology (IDUN program). Her academic excellence spans over two decades in Artificial Intelligence and Machine Learning , with a focus on Explainable AI (XAI) , Case-Based Reasoning (CBR) , and Natural Language Processing (NLP) . Her research explores innovative methodologies for knowledge-rich representations to automate decision-making through CBR for Retrieval-Augmented Q&A systems and human-centered AI platforms . She co-founded Attendr.app , a spinout for student and conference attendance tracking, and leads the Artificial Intelligence & Reasoning Research Group at RGU. Recent publications (2024–2025) highlight her work on LLM hallucination detection , counterfactual explanations in finance , cross-lingual biomedical review automation , and multi-query resolution in legal domains . Themes span AI explainability , NLP , CBR , and domain-specific knowledge integration across healthcare, law, and education. Her leadership extends to organizing international workshops on XAI , digital health , and Deep Learning , and co-chairing the ICCBR 2021 and 2022 conferences. She actively contributes to program committees for ECCBR , ECML/PKDD , and IJCAI .

Gyunam ParkView profile

Researcher

• Process Mining
• Action-Oriented Process Mining
• Object-Centric Process Analysis
• +12 more

Gyunam Park is a Research Group Lead and Process and Data Scientist at Fraunhofer FIT and a Scientific Assistant at the Chair of Process and Data Science at RWTH Aachen University, a leading institution in computer science and engineering. He is actively involved in both research and teaching, contributing to the advancement of process mining, data science, and artificial intelligence. His work bridges academic research and industrial applications, particularly in SAP ERP systems and digital twins of organizations. Research Interests: Gyunam Park’s research focuses on Action-Oriented Process Mining (AOPM) , Object-Centric Process Analysis , and Responsible Machine Learning . He aims to transform process mining insights into actionable management decisions, ensuring transparency, fairness, and compliance. His work enables organizations to monitor operational constraints, generate corrective actions, and assess their impact using data-driven methods. Publication Trends: His recent publications emphasize object-centric approaches to process mining, predictive monitoring, constraint checking, and integration with AI planning. There is a strong trend toward preserving structural information in event logs, improving machine learning performance, and applying these techniques to real-world systems like SAP ERP and after-sales service processes. Scientific Awards: No awards are explicitly mentioned in the provided text. Advising and Grants: While no formal students are listed, Gyunam Park leads research projects and collaborates with industry partners such as Samsung Electronics and SAP. His projects involve root cause analysis, resource optimization, and educational data mining. He has developed open-source tools like ProAct and OCPA , indicating active grant or institutional support for software development and research dissemination. Labs and Teams: He is a core member of the Process and Data Science (PADS) group led by Prof. Wil van der Aalst at RWTH Aachen University and leads a research group at Fraunhofer FIT. These teams focus on cutting-edge research in process mining, data science, and AI, with strong industry collaborations and regular contributions to top conferences and journals.

Enayat RajabiView profile

Associate Professor

• Machine Learning
• Knowledge Graphs
• Data Analytics
• +3 more

Dr. Enayat Rajabi is an Associate Professor of Business Analytics at the Shannon School of Business, Cape Breton University. Holding a PhD in Information and Knowledge Engineering from the University of Alcala (Spain) and a postdoctoral fellowship from Dalhousie University, his research focuses on the intersection of Machine Learning, Knowledge Graphs, and Data Analytics. He actively applies these technologies in healthcare, smart cities, and social media crisis response contexts. Education PhD in Information and Knowledge Engineering, University of Alcala (Spain) Postdoctoral Fellowship, Dalhousie University Research Interests His work bridges Knowledge Graphs with Machine Learning, emphasizing explainability and practical applications. Key areas include: Explainable AI for clinical decision-making Knowledge Graph applications in healthcare systems Social media analytics for emergency response Smart city data integration Generative modeling for tabular data Recent Publications Trends Recent articles highlight: Explainable AI in healthcare settings Industrial breakdown prediction systems Social media influencer detection Smart city infrastructure modeling Advanced data synthesis techniques Continued focus on Knowledge Graph applications

• Explainable Artificial Intelligence
• Type-2 Fuzzy Logic Systems
• Computational Intelligence
• +12 more

Hani Hagras is a Professor in the School of Computer Science and Electronic Engineering at the University of Essex, UK , where he leads pioneering research in type-2 fuzzy logic systems and explainable artificial intelligence (XAI) . His work bridges theoretical advances with real-world applications in smart infrastructure, healthcare, telecommunications, and ambient intelligence. His research interests include explainable AI, type-2 fuzzy logic, computational intelligence, machine learning, human-computer interaction, and intelligent systems. He investigates how fuzzy systems can enhance the transparency, robustness, and adaptability of AI models, particularly in uncertain and dynamic environments. His recent work explores applications in genomics, predictive maintenance, and smart grid optimization. The publication trends show a strong focus on integrating fuzzy logic with deep learning and optimization techniques to create interpretable models. His work frequently appears in top-tier journals such as IEEE Transactions on Fuzzy Systems , IEEE Transactions on Artificial Intelligence , and Neurocomputing . He emphasizes real-world applicability, often collaborating with industry partners like BT. ORCID: 0000-0002-2818-5292 He has advised numerous PhD and Master’s students, including Mehrin Kiani, Ashish Bhatia, and Hugo Leon-Garza, many of whose research projects focus on workforce optimization, healthcare AI, and intelligent systems. His research has been supported by major grants, particularly in smart environments and telecommunications. Hani Hagras is actively involved in leading research labs and teams at the University of Essex, including work on augmented reality for field service support, georeferenced data visualization , and ambient-assisted living systems. His team develops fuzzy logic frameworks for real-time decision-making in complex environments.