Erisa Karafili is an Associate Professor in Cybersecurity at the University of Southampton. She leads Teaching Methods Innovation at the GCHQ/EPSRC Academic Centre of Excellence for Cyber Security Education (ACE-CSE) and is a Champion in Security by Design at ACE-CSR. A Fellow of the Higher Education Academy, she joined the University in 2020 after roles including a Marie Curie Fellowship at Imperial College London, where she investigated cyber-attack attribution techniques. Her research focuses on formal methods applied to security, IoT threat models, and secure data sharing frameworks. Education: PhD in non-classical logics applied to multi-agent systems security from the University of Verona. Previous positions include PostDoc at Technical University of Denmark and Researcher at Imperial College London. Research Interests: Cyber-attack attribution, IoT security, formal methods in cybersecurity, data privacy, and argumentation-based reasoning for security. Awards: Higher Education Academy Fellowship. Current PhD Students: Betul Gokkaya, Mohammed Homaid Alquliti, Peter Geoffrey Williams, Steve Johnson. Active Projects: Heterogeneous Material Integrated MEMS/NEMS-Photonics Platform for Secure Communication (collaborative with Jize Yan and others).
Vahid Yazdanpanah is an Assistant Professor (Lecturer) of Computer Science at the University of Southampton and a Visiting Lecturer at the University of Twente. He holds a PhD from the University of Twente (2019), an MSc in Artificial Intelligence from Utrecht University (2015), and an MBA from the University of Greenwich (2012). His research focuses on multiagent systems, AI responsibility, and circular economy applications. He leads the Agents, Interaction and Complexity (AIC) research group and is an RRI Champion for the UKRI CDT in AI for Sustainability (SustAI). Research interests include agent-based computing, formal logics for multiagent decision-making, and socio-technical systems. Notable awards include the Best Paper Award at PRIMA 2016 and recognition at AAMAS 2021. He teaches courses such as COMP2211 (Software Engineering) and supervises PhD students in AI and sustainability. Active projects include the ARGOS (AI Resilience Governance) and AutoTrust (Internet of Vehicles) initiatives. He has served on program committees for AAAI, IJCAI, and AAMAS, and reviews for journals like AI & Society and Annals of Operations Research. His work bridges technical AI advancements with ethical, legal, and societal challenges in responsible AI deployment.
Baishakhi Ray is an Associate Professor at Columbia University, specializing in improving software reliability and developers' productivity for both traditional and AI-driven systems. She leads the ARiSE Lab, focusing on interdisciplinary research at the intersection of software engineering and artificial intelligence. Her research interests include software testing for AI systems, adversarial robustness, automated testing of autonomous systems, and leveraging AI techniques such as neural networks for dynamic analysis and fuzzing. Notable projects include DeepTest for autonomous car testing and NEUZZ for efficient fuzzing. Awards: VMware Early Career Faculty Award (2020), IBM Faculty Award (2019), NSF CAREER Award (2019), and multiple best paper awards including EAPLS FASE (2020) and ACM Distinguished Papers (FSE 2017, MSR 2017). Grants: NSF CAREER grant (2019-2024) for deep learning testing, NSF grants for workshops and security bug detection, and collaborative grants on persistent memory and SSL/TLS implementations. Her recent work emphasizes advancing code generation with large language models (LLMs), evaluating model robustness under data contamination, and developing tools like CodeSense and CrashFixer for code semantics and kernel debugging. The ARiSE Lab also explores causal performance debugging and transfer learning for configurable systems.
Professor Coral Dando is a Professor of Psychology at the University of Westminster, leading research in forensic cognition and investigative interviewing. With a background as a London Police Officer, she completed a BSc (Hons) Psychology and PhD in Applied Forensic Cognition. She is a Chartered Psychologist, National Teaching Fellow, and Registered Forensic Psychologist. Her research focuses on eyewitness memory, deception detection, and cross-cultural interviewing, with a particular emphasis on improving investigative methods in security and forensic contexts. Her academic roles include teaching forensic psychology modules (e.g., detecting deception, investigative interviewing) and supervising PhD students researching topics like virtual environments in interviews and neurodivergent credibility. She has secured significant research funding from entities like the Home Office, FBI, and CPNI, totaling over £2M across projects addressing insider threats, county lines exploitation, and aviation security. Key Research Themes: Eyewitness reliability, cognitive interviewing techniques, cross-cultural persuasion, and insider threat detection. Grants: Includes a 2024 €97,000 COST grant for implementing the Mendez Principles and a 2016 $469,000 FBI-funded study on intelligence interviewing. Professor Dando’s work bridges academia and practice, training professionals from police forces, security agencies, and NGOs. She contributes to policy development and has authored over 100 peer-reviewed publications, including seminal works on the cognitive interview and WAFA (Witness-Aimed First Account) for neurodivergent individuals. Her research emphasizes ethical, context-aware methodologies to enhance justice outcomes.
Rongxing Lu is an Adjunct Professor at the Faculty of Computer Science, University of New Brunswick (UNB), Canada, since August 2016. Previously, he held positions at Nanyang Technological University (NTU), Singapore (2012–2016) and the University of Waterloo, Canada (PhD in 2012). His research focuses on applied cryptography, privacy enhancing technologies, and IoT-big data security. He has over 7,500 citations and received prestigious awards like the Governor General’s Gold Medal (2012) and the IEEE ComSoc Asia Pacific Outstanding Young Researcher Award (2013). He is an IEEE senior member and serves on editorial boards of journals like IEEE Network. **Education**: PhD in Electrical & Computer Engineering, University of Waterloo (2012), awarded Governor General’s Gold Medal Postdoctoral Fellow at University of Waterloo (2012–2013) **Research Interests**: Developing cryptographic protocols for IoT and big data systems Privacy-preserving techniques for distributed systems Secure communication in 5G/6G networks and vehicular systems **Awards and Recognition**: Recipient of multiple best paper awards in IEEE conferences 2016–2017 Excellence in Teaching Award at UNB **Editorial and Leadership Roles**: Symposium co-chair at IEEE Globecom’16 Secretary of IEEE ComSoc CIS-TC Organized special issues on fog computing security (Elsevier) and big data security (IEEE IoT Journal) **Key Contributions**: Pioneered privacy-aware data reporting schemes for vehicular networks Designed lightweight IoT authentication protocols Advanced secure machine learning frameworks with privacy guarantees
Joseph Alejandro Gallego Mejia is an Assistant Teaching Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He holds a PhD with meritorious distinction in Systems and Computing Engineering from the National University of Colombia, along with a Master’s and dual Bachelor’s degrees in Systems and Computing Engineering and Industrial Engineering. PhD in Systems and Computing Engineering, National University of Colombia (Meritorious Distinction) Master of Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Industrial Engineering, National University of Colombia His research focuses on artificial intelligence, machine learning, computer vision, quantum machine learning, natural language processing, and cybersecurity. He explores robustness estimation, anomaly detection, incremental learning, and scalable software architectures for AI systems. His work bridges theoretical foundations and practical applications in health, remote sensing, and edge computing. The recent publications reflect a strong trend in interdisciplinary AI research, combining machine learning with quantum computing, cybersecurity, and natural language understanding. His work spans domains such as satellite imagery analysis, medical diagnostics, IoT security, and conversational AI, demonstrating a commitment to scalable and robust intelligent systems. Keywords across publications include Computer Science, Machine Learning, Quantum Computing, and Cybersecurity, with subfields ranging from adversarial robustness to hybrid quantum-classical models. Scientific distinctions include: PhD with meritorious distinction, National University of Colombia Postdoctoral fellow, Frontier Development Lab (Trillium), supported by NASA and ESA He has served as a reviewer for top-tier journals and conferences including Neurocomputing, IEEE Access, Radioscience, NeurIPS, and NLDL. Though no formal grants are listed, his postdoc was funded by NASA and ESA, indicating significant external support. He teaches courses in programming, data science, machine learning, deep learning, NLP, and software engineering. He founded the tech company Sammu and mentors students through instruction and research supervision. He is actively involved in research and teaching, contributing to innovative programs in AI and computing education. His lab and team affiliations are not explicitly stated, but his work suggests collaboration with AI, quantum computing, and cybersecurity research groups.
Chun Ouyang is a Professor at Queensland University of Technology (QUT) in the School of Computer Science within the Faculty of Science. With an extensive publication record spanning over two decades from 2002 to 2025, Professor Ouyang has established themselves as a leading researcher in Business Process Management, Process Mining, and Explainable AI. Their work bridges theoretical foundations with practical applications across healthcare, finance, and industrial sectors. Professor Ouyang's research interests primarily focus on Business Process Management systems, Process Mining techniques, Explainable Artificial Intelligence, and Healthcare Process Analysis. Their work has evolved from foundational BPMN/BPEL translation research in the early 2000s to sophisticated process mining approaches in the 2010s, and most recently to cutting-edge Explainable AI applications in clinical and business contexts. They have developed novel methodologies for process querying, predictive process analytics, and XAI evaluation frameworks that have significantly advanced the field. Their research consistently emphasizes practical applicability while maintaining strong theoretical foundations, with publications in top-tier journals and conferences including IEEE Transactions, Springer journals, and major BPM conferences. Analysis of Professor Ouyang's recent publications (2023-2025) reveals a strategic research trajectory that integrates traditional process mining with modern AI techniques, particularly focusing on explainability and trustworthiness. Their work demonstrates a consistent pattern of addressing real-world challenges through rigorous methodological development, with increasing emphasis on healthcare applications, clinical decision support systems, and the ethical implications of AI deployment. The publications show strong interdisciplinary collaboration patterns, particularly with medical researchers and industry partners. Professor Ouyang has mentored numerous PhD students and early-career researchers who have gone on to establish themselves in the BPM and AI communities. Their research group at QUT has secured multiple competitive grants supporting innovative work in process analytics and AI. They maintain active collaborations with leading researchers globally, including Catarina Pinto Moreira, Arthur ter Hofstede, and Moe Wynn. Professor Ouyang leads the Process Analytics Research Group at QUT, which focuses on developing advanced techniques for business process analysis, prediction, and optimization. The group maintains strong industry connections with healthcare providers, financial institutions, and government agencies, ensuring their research has practical impact. Current projects include developing trustworthy AI systems for clinical decision support, cross-organizational process analysis frameworks, and next-generation process mining techniques for complex, distributed systems.
Peter Karsmakers serves as Associate Professor at KU Leuven's Department of Computer Science within the Faculty of Engineering Technology, based at the Geel Campus. He coordinates the Declarative Languages and Artificial Intelligence (DTAI) research group and holds leadership roles including coordinator of Research and Education for Computer Science across Geel and Diepenbeek Campuses. Karsmakers earned his PhD in Engineering Science in May 2010, focusing on kernel-based learning algorithms for sparse modeling and efficient predictions from large datasets. His doctoral work established foundations for his current research trajectory in resource-constrained machine learning systems. His research integrates machine learning with signal processing for real-time sensor data interpretation, specializing in anomaly detection from acoustic, radar, and accelerometer signals on embedded devices. Current projects address industrial condition monitoring, elderly care systems, and livestock facility monitoring through three main tracks: acoustic monitoring (e.g., SINS, WATCHDOG), radar-based systems (e.g., FARADAY, NextPerception), and smart electronics for power converters. Recent publications demonstrate strong trends in constraint-guided deep learning architectures for industrial applications, cross-environment robustness in sensor systems, and domain-knowledge integration to reduce data requirements. His work consistently bridges theoretical machine learning with practical implementations in resource-constrained environments. No scientific awards or fellowships were mentioned in the provided materials. Karsmakers supervises over 10 master's theses annually and coordinates a research team of 10 PhD students and a post-doc within DTAI-ADVISE. He has secured approximately 2.3 million euros in funding through VLAIO, EU-ECSEL, and bilateral industry contracts, including 10 active projects such as AutoEdgeML (2024-2028) and Fault Tolerant Neural Networks for Space Applications (2024-2027). He leads the DTAI-ADVISE research group focused on developing software that attaches semantics to sensor data on resource-constrained devices. The team operates across multiple campuses with specialized labs for acoustic monitoring (Geel), radar-based systems (in collaboration with ESAT-TELEMIC), and smart electronics (with Electrical Engineering department), maintaining strong industry partnerships with companies in healthcare, manufacturing, and agriculture sectors.
Prof. Dr. Enkelejda Kasneci is a Distinguished Professor at the Technical University of Munich (TUM), leading the Chair of Human-Centered Technologies for Learning. She holds dual affiliations within TUM School of Social Sciences and Technology and TUM School of Computation, Information and Technology. Her research integrates AI, eye-tracking, and immersive technologies to advance educational paradigms. She directs the TUM Center for Educational Technologies and chairs the MSc program 'AI in Society.' Education: PhD in Computer Science from University of Tübingen (2013), M.Sc. from University of Stuttgart (2007). Earlier roles include Assistant Professor and Dean of Studies at University of Tübingen. Research Focus: Human-centered AI applications in education, multimodal interaction design, and privacy-preserving eye-tracking. Her work bridges technology and pedagogy through projects like AI tutor PEER, VR Classroom, and Privacy-Preserving Eye-tracking. Key Projects: Leads EU-funded projects VIVA (€1.125M), DigiProMIN (€163K), and SARA Kids (€244.8K). Active in policy initiatives like Europe’s AI Imperative. Awards: TUM Heinz Maier-Leibnitz Medal (2024), Liesel Beckmann Distinguished Professorship (2022), and Südwestmetall Research Prize (2014). Grants & Advising: Over €5M in secured funding across 12+ projects. Supervises 14+ PhD researchers and mentors postdocs in AI education and HCI. Labs & Teams: IT-Stiftung EdTech Lab houses advanced VR/eye-tracking setups. Research group includes 20+ members spanning AI, HCI, and educational technology.
Nguyen Dang is a Lecturer at the School of Computer Science, University of St Andrews, actively supervising PhD students and teaching AI-related modules including Artificial Intelligence (CS3105), Artificial Intelligence Practice (CS5011), Machine Learning (CS5014), and Uncertainty in Artificial Intelligence (CS5016). He leads the Centre for Interdisciplinary Research in Computational Algebra and maintains an active research profile with numerous publications in top conferences. University of St Andrews, School of Computer Science Lecturer (equivalent to assistant professor) Supervising PhD students including Tai Nguyen Teaching multiple AI and Machine Learning courses Dr. Dang's research focuses on the intersection of machine learning and optimization, particularly automated algorithm configuration and design. His work centers on leveraging machine learning techniques to automate the development of optimization algorithms, with special emphasis on deep reinforcement learning for Dynamic Algorithm Configuration and integrating machine learning into constraint programming. His research has significant applications across various domains, especially in automated constraint modeling. The publications reflect strong activity in combinatorial optimization, algorithm selection, and benchmark instance generation. His recent publications demonstrate consistent output in top venues including Artificial Intelligence Journal, GECCO, FOGA, and CP conferences, with notable achievements including Best Paper Awards at GECCO'2025 and GECCO'2022. The research spans theoretical foundations of parameter control, practical applications in constraint programming, and innovative approaches to algorithm configuration. Best paper award at GECCO'2025 Best paper award at GECCO'2022 Nomination for best paper award at FOGA'2023 Best paper award at GECCO'2017 Dr. Dang holds a Leverhulme Early Career Fellowship (2020-2023) worth £90,000 for his project on constraint-based automated generation of synthetic benchmark instances. He has secured additional funding including EPSRC High Performance Computing grants totaling over 2.2 million CPU hours and a COST Action grant. His research group actively develops tools and frameworks for automated algorithm configuration and benchmark instance generation, with several open-source datasets available on GitHub. He is involved with multiple research groups including the Centre for Interdisciplinary Research in Computational Algebra and collaborates extensively with researchers at University of St Andrews and internationally, including at Université de Paris I Panthéon-Sorbonne where he conducted visiting research.
Wolfgang Klas is a Professor at the Faculty of Computer Science, leading the Research Group Multimedia Information Systems. His research focuses on multimedia systems, data management, and information retrieval, with significant contributions to multimedia content analysis and database systems. He has been actively involved in multiple research projects, including TP2 PRECIOUS (2013-2016), SciLink (2011-2014), and OptFI (2010-2013). His work intersects with emerging technologies like blockchain, as seen in his public engagements and talks on topics such as 'From Blockchain to Web' and IT4S Forum presentations. His research interests span multimedia systems, database design, and the application of declarative programming for security and data integrity. Recent projects emphasize fake review detection and sentiment analysis using advanced algorithms and neural models. He has collaborated internationally, contributing to workshops like SMAP 2020 and publishing in journals like Algorithms and Applied Sciences . Grants/Projects: TP2 PRECIOUS (2013-2016), SciLink (2011-2014), OptFI (2010-2013) Activities: Speaker at IT4S Forum (2024), Blockchain-related talks (2020–present) Labs/Teams: Research Group Multimedia Information Systems
James A. Millward is a Professor of Inter-societal History at Georgetown University's Edmund A. Walsh School of Foreign Service and Department of History. He holds a Ph.D. in History from Stanford University (1993), an MA from the School of Oriental and African Studies (University of London, 1985), and a BA in East Asian Languages and Civilizations from Harvard (1983). Millward specializes in Qing imperial history, the Silk Road, Xinjiang studies, and cross-cultural musical exchanges across Eurasia. Research Interests: His work critically examines Sinicization narratives, the Qing tribute system, and the impact of colonial historiography on contemporary China's ethnic policies. He also explores musical instrument diffusion along the Silk Road, comparing lutes like the pipa and oud. Publications: Notable works include Eurasian Crossroads: A History of Xinjiang (2007, 2021), The Silk Road: A Very Short Introduction (2013), and chapters in edited volumes analyzing Qing multiculturalism and cultural assimilation frameworks. Academic Leadership: As former president of the Central Eurasian Studies Society (2010) and contributor to the International Studies Association award-winning Culture and Order in World Politics (2019), Millward bridges historical analysis with modern political discourse. Media Engagement: A frequent contributor to The New York Times , Washington Post , and international media, he provides expert commentary on Xinjiang, Uyghur issues, and China-West relations. His current projects include a study of lute cultural exchange and the book Decolonizing History in China , challenging exceptionalist narratives in mainland historiography.
Dr Kun Wei is a Lecturer in Computer Science at the University of the West of England (UWE), joining in 2015. Previously, he worked as a Research Associate at the University of York, focusing on timing properties of complex systems and formal development of safety-critical applications. His research spans formal verification, security protocols, concurrent programming, and model-checking tools. BSc in Computer Science MSc in Internet Computing PhD in Computer Science from the University of Surrey His expertise includes the analysis of timing properties in social-technical systems and cyber-physical systems using the timebands model, as well as formal verification of security protocols. He specializes in object-oriented software development , concurrent programming , and semantics of programming languages , with applications in safety-critical and dependable systems . His work integrates theorem-proving and model-checking tools for system development and verification.
Kede Ma is an Associate Professor in the Department of Computer Science at City University of Hong Kong (CityUHK). He received his B.E. from the University of Science and Technology of China (USTC) in 2012, and MASc and Ph.D. degrees from the University of Waterloo in 2014 and 2017, respectively. From 2018 to 2019, he was a Research Associate with the Howard Hughes Medical Institute and New York University. Prof. Ma has been named to the Highly Cited Researchers list by Clarivate Analytics in 2024 and currently serves on the editorial boards of IEEE Transactions on Image Processing, IEEE Transactions on Information Forensics and Security, and IEEE Signal Processing Letters. Prof. Ma leads the Multimedia Analytics (MA) Laboratory, an interdisciplinary research group focused on computational vision, computational modeling of human visual perception, perceptual multimedia signal processing, quality assessment, and multimedia forensics. His research spans computational photography, high dynamic range imaging and rendering, omnidirectional video analysis, camera processing pipeline design, and artificial intelligence safety in multimedia systems. His work integrates machine learning techniques including reinforcement learning, generative modeling, self-supervised learning, and continual learning for multimedia signal processing applications. His recent publications demonstrate a strong focus on image quality assessment, deep learning for multimedia processing, and multimedia forensics. His work bridges theoretical computer vision principles with practical applications in multimedia systems. The research trends show increasing integration of foundation models with specialized multimedia processing tasks, particularly in quality assessment and security applications. Highly Cited Researchers list by Clarivate Analytics (2024) Best Paper Award at IEEE International Conference on Virtual Reality and Visualization (2021) Best Paper Runner-Up at International Joint Conference on Artificial Intelligence Workshop (2021) Top 10% Award at IEEE International Conference on Image Processing (2015) Finalist for the Governor General's Gold Medal, University of Waterloo (2017) Spotlight presentation at NeurIPS (2022) Highlight paper at ICCV (2025) Oral presentation at ICLR (2025) Prof. Ma advises numerous PhD students and postdoctoral fellows in the MA Laboratory. His research is supported by various grants enabling work in multimedia analytics, image processing, and computer vision. The laboratory maintains active collaborations with researchers at institutions including SUSTech, ZJU, and HIT. Current projects focus on advancing image quality assessment methodologies, developing more robust deep learning techniques for multimedia forensics, and exploring new approaches to HDR imaging and omnidirectional video processing. The Multimedia Analytics Laboratory maintains a strong focus on both theoretical foundations and practical applications of multimedia processing. Current research directions include integrating large language models with image quality assessment, developing more robust deepfake detection methods, and advancing techniques for continual learning in multimedia applications. The lab emphasizes rigorous evaluation methodologies and maintains multiple datasets for multimedia quality assessment research.
David Blakesley is Professor of Rhetorics, Communication, and Information Design and Campbell Chair in Technical Communication at Clemson University's College of Arts and Humanities, Department of Interdisciplinary Studies. He currently serves as President of the Faculty Senate for 2023-2024. His academic career spans over three decades with significant contributions to rhetorical theory, digital publishing, and technical communication. Blakesley earned his PhD in Rhetoric, Linguistics, and Literature from the University of Southern California (1990), an MA in English from San Diego State University (1986), and a BA in English from the same institution (1983). His research spans multiple interconnected fields including rhetorical theory, history of rhetoric, digital and visual rhetorics, print and digital publishing, film theory and production, technical communication, and digital humanities. He has focused particularly on Kenneth Burke's rhetorical theory and its applications across various media. His scholarly work demonstrates a consistent trajectory from traditional rhetorical theory toward digital applications and publishing innovations. Over the past decade, his research has increasingly focused on the intersection of digital technologies with rhetorical practices, particularly examining how digital platforms transform scholarly communication and publishing. His 2022 TUGboat article on production versus distribution in publishing exemplifies his ongoing interest in how technological shifts impact academic and professional communication. Fellow, Rhetoric Society of America George Yoos Distinguished Service Award (RSA) Charles Moran Award for Distinguished Contributions to the Field of Computers and Composition Blakesley has secured significant funding including the $11 million Clemson-Adobe Partnership for Campus-Wide Creative Cloud implementation. He serves as Editor of KB Journal, Co-Editor of WAC Journal, and General Editor of The Writing Instructor. His editorial work has been instrumental in shaping scholarly communication in rhetoric and composition studies, particularly through his leadership in digital publishing initiatives.
