Nan Sun is a Lecturer at the School of Systems & Computing , University of New South Wales, Canberra , conducting interdisciplinary research at the intersection of cybersecurity and artificial intelligence. Her work focuses on data-driven cybersecurity incident prediction, cybersecurity awareness education systems, and AI applications for threat intelligence. PhD in Information Technology (Deakin University) Former Research Fellow at Deakin University's Centre for Cyber Security Research and Innovation Her research spans cybersecurity (60%), machine learning (25%), and software engineering (15%). Recent publications analyze adversarial machine learning, tropical cyclone forecasting with deep learning, and ethical AI frameworks for cyberbullying mitigation. She leads grants including the UNSW Recruitment Research Proposal Grant and CSIRO Data61 funding. Current teaching includes Big Data and Decision Analytics for Security and Digital Forensics . She offers PhD scholarships to students with high academic achievement.
Professor Tughrul Arslan holds the Chair of Integrated Electronic Systems at the School of Engineering, University of Edinburgh . He leads the Embedded Wireless and Wearable Sensor Systems (EWireless) Group and co-founded sensewhere Ltd. and Sofant Technologies . His research spans reconfigurable architectures, low-power wireless systems, and biomedical RF sensing. Academic Background: BEng and PhD in Electronics Professional Affiliations: Senior Member IEEE, Fellow IET, Chartered Engineer His work focuses on smart wearable devices , indoor positioning systems , and AI-driven healthcare monitoring . Recent publications emphasize microwave imaging for dementia detection , edge AI accelerators , and non-invasive tremor monitoring . Key contributions include patented technologies like the Reconfigurable Instruction Cell Architecture (RICA) . Award-winning academic, he has supervised over 40 PhD students and authored 400+ peer-reviewed papers. His external roles include Chief Technology Officer at sensewhere , driving commercialization of indoor navigation solutions.
Naonori Ueda is a Research Professor and Deputy Director at RIKEN Center for Advanced Intelligence Project. He also serves as a Visiting Fellow at NTT Communication Science Laboratories, Research Supervisor for Mathematical Information Platform at Japan Science and Technology Agency (JST), and Visiting Professor at Kobe University's Graduate School of System Informatics. His distinguished career spans academia, government research institutions, and industry collaboration, with significant contributions to advancing artificial intelligence and machine learning applications across multiple scientific domains. Dr. Ueda's research interests focus on the intersection of machine learning, artificial intelligence, and physical sciences. He specializes in physics-informed deep learning approaches that integrate governing physical equations with neural network architectures. His work spans geophysical data analysis, remote sensing applications, computational seismology, and environmental monitoring systems. He has pioneered methods for crustal deformation modeling, earthquake prediction, tsunami inundation forecasting, and satellite imagery analysis using advanced machine learning techniques. His research demonstrates how AI can solve complex scientific problems by bridging the gap between data-driven approaches and physical domain knowledge. His publication record reveals a strong trend toward applying machine learning to solve real-world geophysical and environmental challenges. His recent work shows increasing sophistication in physics-informed neural networks that incorporate domain-specific knowledge into deep learning architectures. The publications span high-impact journals like Nature Communications, demonstrating the interdisciplinary significance of his work. His research consistently focuses on practical applications of AI for disaster prevention, environmental monitoring, and scientific discovery. Fellow of IEICE (Institute of Electronics Information and Communication Engineers) Member of Japan Prize field review committee Selection Committee Member for Brilliant Female Research Award (The Jun Ashida Award) Member of Kyoto Prize Selection Committee Dr. Ueda has secured substantial research funding through multiple government-sponsored projects including RIKEN Pioneering Project 'Prediction Science,' JST AIP Acceleration Research projects on weather prediction and drug discovery, and AMED-funded medical research initiatives. His leadership extends to serving as Sub-project Director for Japan's Moonshot R&D Project. He actively mentors researchers through his roles at RIKEN, NTT, and various academic institutions, fostering the next generation of AI scientists. As Deputy Director of RIKEN Center for Advanced Intelligence Project, Dr. Ueda leads one of Japan's premier AI research initiatives. He also serves on the Advisory Board of Kobe University's Mathematical and Data Science Center and Kyoto University's Graduate School of Informatics. His leadership extends to coordinating the AI Seminar at Osaka Industrial Association and supervising the Keihanna 'Edison Society' at the International Institute for Advanced Studies, demonstrating his commitment to bridging academic research with industrial applications.
Elisabetta Colucci is a Fixed-term Assistant Professor at the Department of Architecture and Design (DAD) of the Polytechnic University of Turin. Her research bridges Geomatics with Cultural Heritage documentation, focusing on Photogrammetry , Remote Sensing , and GIS - BIM integration. She actively contributes to Sustainable Cities (SDG 11) and Quality Education (SDG 4) through technological solutions. University: Polytechnic University of Turin School: College of Architecture and Design Department: Department of Architecture and Design Academic Rank: Assistant Professor Her research interests include: 3D Metric Survey Geospatial Data Analysis Ontology-Based GIS Modeling Digital Twins for Urban Environments Climate Change Impact on Heritage Participatory Mapping Techniques Publication Trends (2022-2025) show interdisciplinary work across Geomatics , Cultural Heritage , and Artificial Intelligence -driven spatial analysis. Key themes include: Heritage Conservation through Digital Documentation GIS-BIM Integration for Maintenance Climate Change Adaptation Strategies Participatory Crowdsourcing Approaches UAV Surveys for Heritage Digital Twin Development Teaching Activities span multiple degree programs: PhD: Spatial Interoperability for 3D Architectural Heritage Master's: Point Clouds and HBIM Undergraduate: Geomatics Laboratory Specialized Courses: GIS for Urban Environments She contributed to the Main10ance Platform patent (MIT 2022-045) and received the Open Badges 'Learning to Teach' certification in 2024.
Wouter Steenbeek is a Senior Researcher at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), which operates as part of the Netherlands Organisation for Scientific Research (NWO). His work primarily focuses on the spatial and temporal dimensions of crime, employing advanced quantitative methods and computational approaches to analyze crime patterns and offender behavior. Steenbeek contributes significantly to the field through methodological innovations in spatial criminology and crime pattern analysis. Steenbeek earned his PhD in Sociology in 2011 from Utrecht University's Department of Sociology/Interuniversity Centre for Social Science Theory and Methodology (ICS). Prior to this, he completed an MSc in Business Informatics (2004) and a Propedeuse in Information Science (2001), both from Utrecht University. This interdisciplinary background combining sociology, criminology, and information science informs his unique approach to crime analysis. Steenbeek's research centers on the geography of crime, with particular emphasis on spatio-temporal patterns, crime concentration, and near-repeat victimization phenomena. He applies computational criminology techniques, including Agent-Based Modeling, to understand offender choice behavior and crime dynamics. His methodological expertise spans quantitative research methods, machine learning applications in crime prediction, and advanced spatial statistics. Steenbeek has developed specialized software packages like NearRepeat and sppt for spatial crime analysis, demonstrating his commitment to advancing methodological tools in the field. His publication record reveals a consistent focus on refining spatial and temporal crime analysis techniques. Recent work explores cybercrime patterns, risk terrain modeling with machine learning, and the temporal consistency of offender behavior. Steenbeek frequently collaborates with leading criminologists across Europe and North America, contributing to both theoretical advancements and practical applications in evidence-based policing. While no specific scientific awards are mentioned in the available information, Steenbeek's contributions to criminological methodology through his software development and publications represent significant scholarly impact. His work on observer bias in disorder measurement and spatial statistics has advanced methodological rigor in the field. Steenbeek actively engages in academic collaboration and knowledge dissemination, participating in the 'Space, Place, and Crime' working group of the European Society of Criminology. His research has practical implications for law enforcement agencies seeking to understand and prevent crime through spatial and temporal analysis. He maintains an active blog discussing technical aspects of spatial analysis and R programming, demonstrating his commitment to open science and methodological transparency.
Wayne Kelly is an Associate Professor in the School of Computer Science at Queensland University of Technology (QUT), Faculty of Science. He has over 25 years of academic experience and serves as the Academic Lead for Teaching and Learning and Course Coordinator for the Bachelor of Information Technology degree. PhD in Computer Science, University of Maryland, College Park, 1996 BSc (Hons) in Computer Science, University of Queensland, 1989 His research expertise lies in Programming Languages, Compiler Construction, and Parallel Computing, with significant contributions to High Performance Computing, Big Data, and Bioinformatics. His work has led to collaborations with Microsoft Research and over $2 million in external funding. His recent publications reflect a strong trend in parallel and distributed systems, embedded computing, bioinformatics data analysis, and remote sensing. Key themes include optimization of computational systems, memory management, and scalable data processing. Wayne Kelly has made impactful contributions to both teaching and research, guiding numerous postgraduate students and leading curriculum development in information technology. Optimizing I/O cost and managing memory for bioinformatics A communication model for streaming applications on MPSoC Ruby.NET: a compiler for the Common Language Infrastructure He is actively engaged in real-world technology development, including a project with a vision-impaired student to improve public transportation accessibility, currently trialed by transport authorities in Australia and the US.
João Pedro Matos-Carvalho is an Assistant Professor at Lusófona University in Lisbon, affiliated with the School of Engineering and the Department of Electrical and Computer Engineering. He is also an Integrated Member of the Center of Technology and Systems (CTS) at UNINOVA and COPELABS, Lusófona University, contributing to interdisciplinary research in robotics and intelligent systems. Ph.D. in Electrical and Computer Engineering, FCT NOVA (2021) M.Sc. (Hons.) in Electrical and Computer Engineering, FCT NOVA (2017) His research focuses on aerial robotics, machine learning, remote sensing, and sensor networks, with applications in UAV navigation, precision agriculture, environmental monitoring, and embedded AI. He has made significant contributions to GPS-denied navigation, multispectral imaging, and AI-driven signal processing. The recent publications reflect a strong trend in integrating deep learning with real-world engineering systems, particularly in UAV autonomy, IoT, and human-centric applications like fall detection and online learning analysis. His work spans algorithm design, software development, and practical deployment in complex environments. Best Paper Award at IEEE Conference (2018) Distinguished Paper Award by LASIGE at FCUL (2021) Best Poster Presentation Award at International Complex Systems and Their Applications Conference (2023) He has secured the competitive Scientific Employment Stimulus (CEEC) grant from FCT and has advised or collaborated on multiple research projects. He has guest-edited special issues in journals such as Drones and Frotiers in Computer Science , and serves as a reviewer for leading scientific journals. He leads the development of open-source tools like AutoNAV and Raster Forge, supporting simulation and geospatial analysis. He is actively involved in research teams at CTS-UNINOVA and COPELABS, focusing on intelligent systems, aerial robotics, and data fusion. His lab work emphasizes practical UAV platforms, sensor integration, and AI deployment in real-time systems.
Thomas Morstyn is an Associate Professor in Power Systems at the University of Oxford , affiliated with the Department of Engineering Science . He directs the Oxford Martin Programme on Circular Battery Economies , serves as a Tutorial Fellow at Hertford College , and holds an Honorary Fellowship at the University of Edinburgh . Morstyn co-chairs the IEEE taskforce on quantum computing applications in power systems and edits IEEE Transactions on Power Systems . Education BEng (Honors) in Electrical Engineering, University of Melbourne (2011) PhD in Electrical Engineering, University of New South Wales (2016) Research Focus Thomas Morstyn's work centers on power system digitalization and market design to enable net-zero energy transitions. His research explores: Control systems for integrating distributed renewable generation AI-driven energy market mechanisms Quantum computing applications in grid optimization Smart hybrid transformers for distribution networks Optimal deployment of grid energy storage Scientific Awards EPSRC Research Fellow Current Projects Leading interdisciplinary initiatives including: the Battery Circular Economies Programme (with Professors Paul Shearing, David Howey, and Radhika Khosla); EPSRC -funded projects on grid storage optimization ( DIGEST ) and quantum computing benchmarks; and an Innovate UK partnership with IONATE on AI-powered hybrid transformers. Labs & Teams Head of the Power Systems Architecture Lab , collaborating with economists, computer scientists, and social scientists across institutions like Imperial College London and Brunel University.
Diego Calvanese is a Visiting Professor at Umeå University's Department of Computing Science and holds a full professorship at the Free University of Bozen-Bolzano, Italy. His research focuses on AI for data management, including virtual knowledge graphs (VKG), ontology-based data access (OBDA), and formal methods like description logics. He is part-time at Umeå, balancing roles with his primary position in Italy. Calvanese has received prestigious awards including the AAAI Classic Paper Award (2021), EurAI Fellow (2015), and ACM Fellow (2019). He supervises three doctoral students at Umeå and has authored over 350 publications, with an h-index of 71. His work emphasizes data integration, geospatial systems, and ethical AI applications. Key Roles: Associate Programme Chair (IJCAI 2025), Programme Chair (IJCAI-ECAI 2026), Head of AI for Data Management Research Group Research Interests: Knowledge representation, graph data management, explainable AI, and telemonitoring systems like reCOVeryaID. His research group develops tools like Ontop, a VKG system enabling seamless data access across heterogeneous sources. Current projects include geospatial data integration and temporal OBDA frameworks. Calvanese has served on over 150 program committees and editorial boards, including Artificial Intelligence and JAIR. His work bridges technical advancements with societal impacts, addressing AI's role in healthcare, climate, and democracy.
Musa BALTA is an Assistant Professor in the Department of Cyber Security Engineering at Sakarya University's Faculty of Computer and Information Sciences. He maintains an active research and teaching career focusing on cybersecurity, computer networks, and artificial intelligence applications in critical infrastructure protection. His educational background includes a Doctorate (2012), Master's Degree (2009-2016), and License (2004-2009), all from Sakarya University in Computer and Information Engineering/Computer Engineering. His doctoral thesis focused on SDN-based VANET architecture for urban traffic management systems. Dr. BALTA's research spans several critical areas including cybersecurity for critical infrastructure (particularly water and energy systems), software-defined networking, vehicular ad-hoc networks, and fuzzy logic applications in traffic management. His work demonstrates a consistent focus on applying artificial intelligence techniques to solve real-world problems in network security and transportation systems. He has developed secure testbed infrastructures for both energy and water management systems, highlighting his contribution to critical infrastructure protection. His publication record shows a clear trajectory from network topology discovery (early career) to sophisticated applications of AI in critical infrastructure security (recent work). The most recent publications focus on image-based security techniques for water infrastructure, advanced testbed development for energy systems, and continued work on intelligent traffic management solutions. EFQM Excellence Award Dr. BALTA teaches various computer engineering courses including Logic Circuits, Computer Engineering Design, Internet Engineering, Network Programming, and Network Security. His current course load demonstrates expertise across foundational computer engineering topics and specialized security courses. He has supervised graduation projects and appears to be involved in several research projects related to critical infrastructure security, including projects titled 'Artificial Intelligence and Big Data Supported Water Quality Monitoring and Anomaly Detection System for Water Management Systems,' 'Integrated Security Management Platform Design and Application for Meter Management Systems in Software Defined Smart Grids,' and 'Development of Cybersecurity Capabilities for Energy Critical Infrastructure (SYNERGY) Project.' His research group appears to focus on critical infrastructure protection, with particular emphasis on developing testbed infrastructures for both energy (CENTER Energy) and water (CENTER Water) systems. This work positions his team at the intersection of cybersecurity, industrial control systems, and critical national infrastructure protection.
Prof. Igor Kononenko serves as a Professor at the Faculty of Computer and Information Science, University of Ljubljana, where he heads the Laboratory for Cognitive Modeling and teaches core courses including Algorithms and Data Structures 1, Artificial Intelligence, Intelligent Systems, and Machine Learning. Education: Ph.D. in Computer Science, University of Ljubljana (1990) Research Focus: His work centers on Artificial Intelligence , Machine Learning , and Cognitive Modeling , with recent emphasis on explainable AI and prediction reliability . He has pioneered techniques in feature contribution explanation, graph-based data mining, and archetypal analysis for complex datasets, resulting in approximately 210 publications and 10 textbooks . Publication Trends: Analysis of his 15 most recent articles reveals a decisive shift toward interpretable machine learning—particularly reliability estimation in data streams (2012-2014), graph mining for oceanographic/spatial data (2013-2019), and medical applications of explanation methods (2011-2018). His 2013-2016 work on archetypal analysis for multi-document summarization remains highly influential in NLP. Research Leadership: As principal investigator, he secured major funding including: Two ARRS programmes on Artificial Intelligence (2009-2014, 2015-2020) EU's AGROIT project for farming efficiency (2014-2016) Bilateral projects on imbalanced data learning (2010-2011), bioinformatics for cancer classification (2014-2015), and disease dataset analysis (2020-2021) Laboratory: The Laboratory for Cognitive Modeling under his direction drives innovation in AI theory and applications, with recent work spanning basketball analytics, coronary artery disease diagnostics, and hemodynamic simulation modeling.
Prof. Dr. Sabine Timpf, Chair of Geoinformatics at the University of Augsburg 's Faculty of Applied Computer Science , leads pioneering research at the intersection of spatial cognition , urban navigation systems , and agent-based modeling . Her work transforms how we understand wayfinding processes , landmark-based route planning , and urban space appropriation through computational approaches. Dr. techn. from TU Vienna (1998), Dipl.-Ing. from University of Hannover (1993), M.Sc. from University of Maine (1992) Research spans spatiotemporal data mining , cognitive GIS , and smart city applications . Key contributions include: Pioneering landmark salience metrics for navigation systems Developing affordance-based urban accessibility models Integrating LLMs with GIScience for intelligent urban systems Mapping olfactory and acoustic wayfinding cues in urban environments Agent-based simulations of public park usage and climate change impacts on allergenic plants Her recent publications analyze: 2025: Foundational work on olfactory navigation and LLM-GIS integration 2023-2024: Landmark modeling, sound mapping, and microplastic soil analysis 2021-2022: Pedestrian simulation frameworks and climate change pollen studies Teaching focuses on GIScience , 3D modeling , and spatial data analysis . She supervises research teams exploring urban green space ontologies , route probability models , and multi-agent transport simulations .
Jason J. Jones serves as an Associate Professor in the Department of Sociology at Stony Brook University, leveraging his interdisciplinary background spanning Computer Science, Psychology, and Political Science to pioneer computational approaches in social inquiry. His work bridges technical methodology with sociological theory to analyze human behavior at unprecedented scales. His academic foundation includes: Bachelor's degree in Computer Science Doctorate in Psychology Post-doctoral research in Political Science Dr. Jones specializes in Computational Social Science and Algorithmic Fairness, utilizing massive datasets from social media platforms to re-examine classical theories of human behavior. His landmark Facebook collaboration generated voting behavior data from millions of participants, while his reworking of Granovetter's "strength of weak ties" hypothesis incorporated global professional networks. Current research focuses on identity quantification through linguistic analysis of social media bios, examining political polarization, LGBTQ visibility, and occupational identity evolution. Analysis of his 15 most recent publications reveals dominant trends in identity measurement through Twitter bios (2012-2023), with methodological innovations in natural language processing enabling longitudinal tracking of societal shifts. His work consistently connects micro-level identity expression to macro-level social phenomena, particularly regarding AI's societal impacts and political behavior. No scientific awards are documented in available materials. Dr. Jones maintains active industry collaborations, notably with Facebook for large-scale behavioral experiments. His research methodology emphasizes open data practices, exemplified by the "Who am I 2024 Dataset," while mentoring graduate students in computational social science techniques though specific advisees aren't listed. His project-based approach operates at the sociology-computer science interface without formal lab affiliation, focusing on cross-disciplinary teams for identity analysis and AI impact studies.
Prof. Dr. Michael Martin is a Professor at the Professorship of Data Management , Faculty of Computer Science , Chemnitz University of Technology . His work focuses on Knowledge Graphs , Large Language Models (LLMs) , and Semantic Web Technologies , with specific emphasis on RDF/SPARQL optimization , geospatial data integration , and dataset versioning . Keywords: Knowledge Graphs, Semantic Web, LLMs, GeoSPARQL, Dataset Versioning Collaboration: Co-authors include Lars-Peter Meyer, Claus Stadler, Sara Todorovikj, and Claus Stadler Research Trends Recent publications demonstrate his leadership in LLM-KG-Bench benchmarking frameworks and CoyPu knowledge graph projects for resilience research. His work bridges Apache Spark with semantic technologies for scalable knowledge graph construction and develops domain-specific ontologies for industries like steel and copper manufacturing. Projects & Tools Martin's team created open-source platforms such as: Quit Store for distributed RDF dataset management CubeViz.js for statistical data visualization Structured Feedback protocol for decentralized data governance
Thanh Le is a Doctoral Research Fellow at the Department of Informatics, University of Oslo, affiliated with the Networks and Distributed Systems research group. Their work spans multiple domains including machine learning, edge/cloud computing, and embedded systems. Research Interests: The fields_of_interest include Distributed Systems, Deep Learning, Embedded Systems, and Edge Computing. Their research focuses on optimizing AI models for edge environments, applying deep learning to medical diagnostics, and developing smart systems for energy and healthcare applications. Publications: Thanh Le's articles demonstrate expertise in edge computing optimization, medical imaging analysis, and IoT-based solutions. The work from 2022-2024 shows a trend of integrating deep learning with spectrum management, renewable energy systems, and healthcare logistics. Laboratory Affiliation: Member of the Networks and Distributed Systems (ND) research group at the University of Oslo.
