Dr. Ikechukwu Nkisi-Orji is a Research Fellow at Robert Gordon University's School of Computing, Engineering & Technology, where he conducts research at the intersection of artificial intelligence, semantic technologies, and case-based reasoning. He is affiliated with the university's Artificial Intelligence & Reasoning Research Group and has extensive collaboration experience with the British Geological Survey and Oil and Gas Innovation Centre. His research focuses on intelligent information retrieval systems, ontology engineering and alignment, semantic web technologies, natural language processing, and case-based reasoning applications to real-world problems. Dr. Nkisi-Orji has made significant contributions through the development of the CloodCBR platform, a cloud-based CBR framework that received an honorable mention at ICCBR 2020, and the iSee platform for personalized explainable AI experiences. His recent publication trends (2023-2025) show a strong focus on integrating case-based reasoning with modern AI techniques, particularly large language models and retrieval-augmented generation systems. His work increasingly addresses explainable AI challenges, legal question answering systems, and methods for improving the reliability of AI outputs in specialized domains. Honourable mention at ICCBR 2020 for CloodCBR platform Dr. Nkisi-Orji has active supervision availability for PhD students in Semantic Information Retrieval, Natural Language Processing, and Ontology design/alignment/application. His research is supported by collaborations with industry partners including the Oil and Gas Innovation Centre, where he has worked on extracting business intelligence from text and applying case-based reasoning to asset inventory management. As the key architect of the CloodCBR platform and contributor to the iSee XAI platform, Dr. Nkisi-Orji leads research efforts focused on making AI systems more transparent, reliable, and applicable to industrial-scale problems through the integration of traditional AI methods with contemporary approaches.
Dr. Salam Traboulsi is a Researcher at Stuttgart University of Applied Sciences (HFT Stuttgart), affiliated with the Competence Center for Digitalization in Research, Teaching & Economics since 2019. She holds a PhD in Computer Science from the University of Toulouse, France (2008). Her work bridges technology and urban innovation, with a focus on developing scalable solutions for modern cities. Research Focus: Dr. Traboulsi specializes in: Smart City ecosystems integrating IoT and 5G Precision technologies for urban positioning and navigation Data management frameworks for large-scale sensor networks Open-source IoT platforms for building efficiency and environmental monitoring Cloud and grid computing infrastructures Key Projects: She leads/contributes to: iCity 2: UDigiT4iCity – Developing urban digital twins using IoT building data and 5G sensor networks iCity 1 – Foundational research on intelligent urban infrastructure systems Publication Trends: Her recent work (2020-2024) emphasizes 5G-enabled urban solutions, including fleet management optimization, indoor positioning systems, and IoT analytics for smart buildings. Earlier research (2005-2013) focused on distributed computing, storage virtualization, and information retrieval systems, demonstrating consistent expertise in large-scale data infrastructure. Academic Engagement: She serves as a scientific reviewer for journals and conferences and teaches in the surveying study area at HFT Stuttgart.
David Chaves Fraga is an Assistant Professor in the Department of Electronics and Computing at the University of Santiago de Compostela (USC), specializing in Computer Science and Artificial Intelligence. He is also a researcher at CITIUS Research Center and maintains a collaboration with the Declarative Languages and Artificial Intelligence Group (DTAI) at KU Leuven. His research focuses on large-scale semantic data integration and its applications across various domains including DevOps, public procurement, and transportation. He is particularly recognized for his work in Knowledge Graph construction and lifecycle management. As co-chair of the W3C Knowledge Graph Construction Community Group and lead scientist for the European Public Procurement Data Space, he plays a significant role in shaping standards and practical implementations in the field. Dr. Chaves Fraga has published over 50 papers in the Semantic Web domain, with several appearing in top conferences like ISWC and ESWC. His work on 'Declarative Generation of RDF Collections and Containers from Heterogeneous Data' received the Best Paper Award at SEMANTiCS Conference. Margarita Salas Fellowship (2022) Best Paper Award at SEMANTiCS Conference (2024) He actively contributes to the academic community as a committee member for major conferences including ISWC, ESWC, WWW, SIGIR, and CIKM, and serves as a reviewer for leading journals in the Semantic Web field. Dr. Chaves Fraga is also involved in organizing two academic workshop series focused on Knowledge Graph Construction and Semantics for Transport.
Diego Calvanese serves as a Professor at the KRDB Research Centre for Knowledge and Data within the Faculty of Computer Science at the Free University of Bozen-Bolzano, where his work bridges theoretical foundations and practical applications in knowledge systems. An ACM Fellow and EurAI Fellow, he maintains active leadership in the global academic community through editorial roles including PeerJ Computer Science. His research spans knowledge representation and reasoning, ontology languages, description logics, conceptual data modeling, data integration, graph data management, data-aware process verification, and service modeling and synthesis, establishing him as a pivotal figure in formal methods for data-intensive systems. These interconnected domains drive innovations in semantic technologies and large-scale data management frameworks. Notable professional contributions include serving as Program Chair for PODS 2015 and KR 2020, General Chair for ESSLLI 2016, and a 2012-2013 visiting researcher position at the Technical University of Vienna as a Pauli Fellow of the Wolfgang Pauli Institute. His awards reflect exceptional impact: ACM Fellow EurAI Fellow Calvanese operates primarily through the KRDB Research Centre, which functions as his core academic hub for advancing knowledge representation methodologies and fostering international collaborations in data science.
Matthes Fleck serves as Professor and Director of the Institute for Communication and Marketing (IKM) at Lucerne University of Applied Sciences and Arts, School of Business, leading research initiatives since 2010 with expertise spanning digital communication, social media strategy, and AI-driven marketing analytics. His leadership drives industry-relevant projects examining Switzerland's sharing economy landscape and intelligent transportation systems. Academic credentials include a Doctorate in Business Administration (Dr. oec.) from University of St. Gallen (2011) and Master of Arts in Journalism and Business Administration from Freie Universität Berlin (2006), establishing an interdisciplinary foundation for his research. This dual expertise enables rigorous analysis of corporate communication through both business strategy and media theory lenses. Research focuses on digital transformation's impact on business communication, with three interconnected pillars: social media dynamics in corporate contexts (examining CSR blogging and stakeholder engagement), sharing economy strategic frameworks (particularly through projects like Sharecity), and artificial intelligence applications for marketing analytics. Methodologically, Fleck pioneers natural language processing techniques to analyze economic news coverage, corporate culture statements, and chatbot efficacy—evidenced by his 2022 agenda-setting study of pandemic-related economic reporting and 2021 assessment of AI mental health solutions for elderly populations. Recent publication trends (2020-2022) reveal intensified focus on pandemic-era digital adaptations, with 60% of recent work applying computational linguistics to crisis communication and health tech. Cross-cutting themes include ethical considerations in AI deployment, data-driven branding for SMEs, and social media's evolving role in stakeholder capitalism—demonstrating consistent alignment between technical methodology and strategic business application. No scientific awards or major honors were documented in source materials. Research leadership manifests through continuous project funding including "Mobile als Innovator in Marketing" (2017), "B2B Social Media" (2014), and current AI/mobility initiatives, though specific grant amounts remain undisclosed. As IKM Director, Fleck manages a multidisciplinary team conducting industry-partnered research in mobile marketing analytics and social media impact measurement, positioning the institute as Switzerland's hub for applied communication science in digital business transformation.
Qing Huang is an Associate Professor in the School of Computer and Information Engineering at Jiangxi Normal University in Nanchang, China. His academic career focuses on bridging software engineering with artificial intelligence, particularly through the application of large language models to enhance software development processes. His research interests span multiple interconnected domains: Software Engineering Knowledge Graphs Human-Computer Interaction Programming Languages Artificial Intelligence applications in software development Dr. Huang's recent work demonstrates a strong focus on leveraging large language models (LLMs) to address fundamental challenges in software engineering. His research explores how AI can enhance code generation, API understanding, software testing, and knowledge representation in programming contexts. A significant portion of his work investigates the intersection of knowledge graphs and LLMs to create more intelligent software development tools. His publications reveal a consistent pattern of innovation in applying cutting-edge AI techniques to practical software engineering problems, with particular emphasis on improving developer productivity through better tooling and knowledge management. Dr. Huang has made notable contributions to the field of prompt engineering for software development tasks, exploring how natural language interfaces can serve as "APIs" for human-AI interaction. His work on AI Chains represents a novel approach to connecting human developers with LLM capabilities through structured knowledge representations. Additionally, he has conducted significant research on smart contract analysis, code reuse, and type inference in partial code contexts. His scientific contributions have been recognized through publications at major software engineering conferences including ASE and ICSE, with multiple papers accepted across different tracks (Research Papers, Journal-First, Tool Demonstrations). Dr. Huang serves as a Program Committee member for ASE 2025 in the Research Papers track, demonstrating his standing in the software engineering research community. Dr. Huang collaborates extensively with researchers from various institutions, including Data61 (Australia), Nanyang Technological University, and other Chinese universities. His work demonstrates strong interdisciplinary connections between traditional software engineering and emerging AI technologies.
Kijung Shin is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology), holding dual appointments in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering (Computer Division). He leads the Data Mining Lab and teaches multiple courses including Graph Mining and Social Network Analysis, Data Mining and Search, and other foundational courses in electrical engineering and AI. Education Ph.D. in Computer Science, Carnegie Mellon University (February 2019) M.S. in Computer Science, Carnegie Mellon University (December 2017) B.S. in Computer Science and Engineering, Seoul National University (August 2015) B.A. in Economics (Double Major), Seoul National University (August 2015) Research Interests Professor Shin's research primarily focuses on data mining, graph algorithms, and network science, with particular expertise in hypergraph analysis, tensor decomposition, and graph neural networks. His work bridges theoretical foundations with practical applications, developing algorithms that can efficiently analyze complex real-world networks. His recent research has expanded into multimodal learning, integration of large language models with graph neural networks, and applications in recommendation systems, satellite imagery analysis, and biological data analysis. His approach combines rigorous mathematical analysis with practical implementation, resulting in numerous open-source software tools that have been widely adopted in both academia and industry. His research has significant implications for social network analysis, fraud detection, recommendation systems, and scientific discovery in various domains. Research Trends Professor Shin's recent publications show a clear trajectory toward more complex network structures, particularly hypergraphs that capture higher-order interactions beyond simple pairwise relationships. His work increasingly integrates traditional graph algorithms with deep learning approaches, especially focusing on how graph neural networks can be improved and made more interpretable. There's also a growing emphasis on practical applications in areas like satellite imagery analysis, medical data, and recommendation systems that address real-world challenges. Scientific Awards Received the PAKDD Best Survey Paper Award for 'Multi-Behavior Recommender Systems: A Survey' (2025) Selected as one of the best short paper candidates of ACM RecSys 2024 (top 7) for 'Revisiting LightGCN' (2024) Selected for oral presentation (2.6% of accepted papers) at AAAI 2024 for 'VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation' (2024) Received the IEEE ICDM Best Student Paper Runner-up Award for 'TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions' (2023) Received the SIGKDD Best Research Paper Award and CogX Award for Best Student Paper in AI for 'FRAUDAR: Bounding Graph Fraud in the Face of Camouflage' (2016) Received the Best Senior Thesis Award from Seoul National University (2015) Received the Samsung Humantech Paper Award (1st in Computer Science) (2015) Teaching and Mentoring Professor Shin has taught multiple graduate and undergraduate courses at KAIST since 2019, including Graph Mining and Social Network Analysis, Data Mining and Search, and foundational courses in electrical engineering. He has also co-organized tutorials at major conferences including AAAI, KDD, ICDM, and CIKM on advanced topics in hypergraph neural networks and real-world hypergraph analysis. As the leader of the Data Mining Lab, he mentors numerous graduate students and postdoctoral researchers, fostering a collaborative research environment that has produced significant contributions to the field of data mining and network analysis. Research Leadership Professor Shin leads the Data Mining Lab at KAIST, which focuses on developing novel algorithms for analyzing complex networks and high-dimensional data. The lab has produced numerous influential software tools including D-Cube, M-Zoom, CoreScope, and DenseAlert, which are widely used in both academic research and industry applications. His research group maintains active collaborations with institutions worldwide and has received funding from various sources to support their innovative work in data mining and network analysis.
Marek Mutwil serves as an Associate Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen, specializing in plant biochemistry with research spanning genomics, systems biology, and computational approaches to plant science. His work bridges experimental biology and data-intensive methodologies to address fundamental questions in plant environmental responses. His research program focuses on deciphering regulatory networks in plant stress adaptation, particularly through cross-species analyses of abiotic stress mechanisms in hydroponic systems. A significant portion of his recent work involves developing computational infrastructure for plant science, exemplified by the PlantConnectome knowledge graph that integrates literature-derived biological relationships across 71,000+ plant research articles. This dual emphasis on experimental stress physiology and bioinformatics resource development positions his work at the intersection of molecular plant biology and data science. Analysis of his 2025 publications reveals a consistent trajectory toward integrative plant systems biology, combining hydroponic crop stress experiments with large-scale knowledge graph applications. These works demonstrate growing emphasis on translational bioinformatics tools that convert fragmented plant science literature into structured, queryable biological networks. Scientific Awards: No awards were documented in the provided text. Advising and Grants: The source material contains no references to graduate students, postdoctoral trainees, or research funding sources. Labs and Teams: While affiliated with the Section for Plant Biochemistry, no specific laboratory structure, team members, or collaborative consortia are described in the available information.
June Zhang is an Associate Professor in the Electrical Engineering department at the University of Hawaii at Manoa, a position she has held since 2024 after serving as Assistant Professor from 2017. Her research focuses on network science and system dynamics, with applications spanning bioinformatics and social processes. Her educational background includes: B.S. with Highest Honor in Electrical and Computer Engineering from Georgia Institute of Technology M.S. in Electrical Engineering from Stanford University Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University (2015) Dr. Zhang investigates how individual adaptations and interactions shape large-scale system behaviors, specializing in graph signal processing, complex systems, and statistical machine learning. Her work integrates nonlinear dynamical systems with biological applications like population genetics modeling and phylodynamics, alongside social phenomena such as information diffusion. Her scientific recognitions include: Georgia Hope Scholarship NSF Graduate Research Fellowship Microsoft Azure Research Award (2015-2016) As co-Principal Investigator for the NSF AI Institute in Dynamic Systems, she leads cutting-edge research while maintaining prior interdisciplinary experience from her ORISE Research Fellowship at the CDC's Viral Hepatitis Division (2016-2017).
