Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.
Behrouz Far is a Professor at the University of Calgary’s Schulich School of Engineering, Department of Electrical and Software Engineering. He holds a PhD in Artificial Intelligence from Chiba University, Japan (1990) and degrees from the University of Teheran including a B.S. in Electrical Engineering (1983) and M.S. in Electrical Engineering (1986). His research focuses on AI applications in medical imaging, software engineering, transportation systems, and data mining. He has contributed to advancements in fundus image analysis, deep learning models for disease detection, and intelligent traffic management systems. Dr. Far has received notable awards such as the 2017 SSE Achievement Award and the AITF-AMA Tier-2 Chair in Smart Multimodal Transportation Systems (2013). His work bridges theoretical AI with practical healthcare solutions, including tools like LETTA for traffic management systems and methodologies for detecting ocular lesions using CNNs. He teaches courses on software testing, reliability engineering, and agent-based systems. His publications highlight contributions to medical diagnostics (e.g., choroidal nevi classification), transportation optimization (e.g., real-time traffic signal control), and machine learning explainability. Collaborative research includes projects on biopotentiostat biosensors for SARS-CoV-2 detection and data mining for cancer patient stratification.
Pietro Barbiero is a Researcher at the Faculty of Informatics, Università della Svizzera italiana (USI) in Lugano, Switzerland. His work focuses on interpretable AI, explainable AI theory, AI-assisted mathematics, and neural-symbolic reasoning, with applications in precision medicine. He holds a PhD in Computer Science from the University of Cambridge (2020-2023), an M.Eng. and B.Eng. in Mathematics and Computer Science from Politecnico di Torino, Italy. Research Interests: - Development of transparent AI systems - Integration of symbolic reasoning with neural networks - Applications in healthcare and precision medicine Lab Affiliation: Member of the People-Centered Computing Group (led by Profs. Silvia Santini and Marc Langheinrich) at USI, involved in projects like the Innosuisse-funded 'XAI-FinCrime' targeting explainable AI for financial crime detection.
Salvatore Ruggieri is a Full Professor at the Department of Computer Science, University of Pisa, Italy. He coordinates the National PhD Program in Artificial Intelligence for Society and teaches in the Master Program in Data Science and Business Informatics. His research focuses on algorithmic fairness, explainable AI, causality, and discrimination discovery, with contributions to tools like YaDT, X-SPELLS, and SCube. He co-chaired FAT*2020 and contributed to projects such as NoBias and HumanE-AI. His work bridges theoretical foundations with practical applications in fairness, privacy, and societal impact. Education: Ph.D. in Computer Science (1999), University of Pisa. Research Interests: Algorithmic Fairness and Non-Discrimination Explainable AI (XAI) Causal Inference Methods Decision Tree Algorithms Social Network Analysis Key Contributions: Developer of YaDT (decision tree tool) and SCube (segregation discovery). Advocacy for ethical AI through policy frameworks and GDPR-compliant explanations. Scientific Awards: Recipient of the Best Ph.D. Thesis in Theoretical Computer Science (EATCS, 1999).
Associate Professor Ke Deng is affiliated with the School of Computing Technologies at RMIT University. His research focuses on urban computing, spatiotemporal data analysis, and social networks, with expertise in data mining and artificial intelligence. He holds a PhD in Computer Science from The University of Queensland (2007), a Master's in Information and Communication Technology (2001), and a Bachelor's in Electrical Engineering (1994). Previously, he was a postdoctoral researcher at CSIRO ICT Centre and a researcher at Huawei Noah's Ark Lab. His work emphasizes practical applications of AI, such as fairness-aware recommendation systems, traffic scenario modeling, and energy-efficient protocols for IoT. Dr. Deng's career includes roles as an acting lecturer at The University of Queensland and co-supervisor of a PhD student. He has supervised numerous research projects, including advancements in quantum annealing for recommenders, fake news mitigation via reinforcement learning, and smart human sensing using millimeter-wave radar. His research outputs span journals like ACM Transactions on Information Systems and IEEE Transactions on Knowledge and Data Engineering. Education: PhD in Computer Science, The University of Queensland (2007) MSc in Information and Communication Technology (2001) BEng in Electrical Engineering (1994) Key Research Themes: Urban computing and smart cities Mechanisms for fair AI systems Energy-efficient IoT protocols Spatiotemporal data mining Grants and Collaborations: ARC Australian Postdoctoral Fellowship (2010–2012) Research at Huawei Noah's Ark Lab (2013) His recent projects highlight interdisciplinary innovation, such as quantum computing for feature selection and self-supervised networks for traffic scenario clustering. Dr. Deng is actively involved in mentoring PhD/Masters students and contributes to RMIT’s research initiatives in AI and urban informatics.
Stefano Teso is an Assistant Professor at the University of Trento (UNITN), actively engaged in research related to interpretable and trustworthy machine learning. His work focuses on integrating human explanations into the learning process and improving model transparency. His research interests center around explainable AI (XAI) and interactive machine learning, particularly through frameworks that incorporate explanatory supervision. Projects such as awesome-explanatory-supervision , caipi , and calimocho highlight his focus on building models that provide understandable reasoning, turning local explanations (e.g., LIME) into globally consistent and trustworthy predictors using self-explaining neural networks. The absence of listed publications prevents detailed trend analysis, but the thematic consistency across repositories indicates a strong, focused research agenda on making AI systems more transparent, reliable, and aligned with human reasoning. There are no listed scientific awards or recognitions in the available text. There is no information available about student advising or research grants. Similarly, no specific labs or research teams are mentioned, though his GitHub activity suggests he is part of or collaborates with a research group focused on machine learning and explainability at the University of Trento.
Shamik Sengupta is the Ralph E. and Rose A. Hoeper Professor at the University of Nevada, Reno (UNR) , where he serves as Professor in the Department of Computer Science & Engineering and Executive Director of the Cybersecurity Center . He holds a PhD in Computer Science from the University of Central Florida (2007) and a BE in Computer Science from Jadavpur University (2002). IEEE Senior Member Director, UNR Cybersecurity Center NSF CAREER Award Recipient
Catia Pesquita is an Associate Professor in Computer Science at the Faculty of Sciences of the University of Lisbon , where she is also a Senior Researcher at LASIGE and leads the Health and Biomedical Informatics Research Line . With a multidisciplinary background in Biology and Computer Science, she focuses on Artificial Intelligence and Data Science applications in life and health sciences . Her research spans Semantic Web , Biomedical Ontologies , Knowledge Graphs , and Explainable AI , with significant contributions to ontology matching and semantic similarity . Education: PhD in Computer Science - Bioinformatics (2012) MSc in Bioinformatics (2008) Degree in Cell Biology and Biotechnology (2005) Current Projects: KATY (2021-2024): AI-Empowered Personalized Medicine for cancer treatments. BRAINTEASER (2021-2024): AI for ALS and MS disease progression models. Research Outputs: Developed tools like AgreementMakerLight (AML) , KGsim-benchmark , and the Epidemiology Ontology . Over 133 publications with significant citations (32,909 reads, 3,889 citations). Teaching: Lectures advanced topics in Databases , Data Integration , Bioinformatics , and Big Data . Advocacy: Vice-president of Biodata.pt , promoting biological data valorization in Portugal. Actively involved in initiatives to promote computer science careers to young women .
Konstantinos Gryllias is a Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Sciences. He leads research in the Mechatronic System Dynamics (LMSD) unit at the Arenberg campus. His academic affiliations extend across multiple KU Leuven institutes including Leuven.AI, Leuven.AM (Additive Manufacturing), and the Gravitation Institute. He serves on important governance bodies as a member of the Faculty Council of Engineering Sciences, Faculty Doctoral Committee of Engineering Sciences, and Departmental Council of Mechanical Engineering. Dr. Gryllias specializes in signal processing, fault detection and diagnosis of rotating machinery, condition monitoring, and machine learning applications in structural health monitoring. His research spans linear and nonlinear vibrations, anomaly detection, rotordynamics, and pattern recognition. His work bridges theoretical signal processing with practical engineering applications in wind turbines, marine propulsion systems, and industrial machinery. His recent publications demonstrate strong focus on deep learning approaches for wind turbine anomaly detection, bearing diagnostics, stern bearing lubrication optimization, and structural health monitoring using advanced signal processing techniques. The research shows increasing integration of explainable AI methods with traditional vibration analysis. Dr. Gryllias teaches advanced courses including Monitoring & Prognostics, Structural Dynamics, Smart Sensing Technologies, and Applied AI perspectives. His teaching portfolio reflects the interdisciplinary nature of his research, connecting mechanical engineering fundamentals with cutting-edge AI methodologies. He currently leads multiple research projects through 2025-2029, primarily as Promotor, focusing on fault detection in gears using fiber optic sensors, multi-sensor monitoring of drivelines, physics-inspired machine learning for condition monitoring, and digital twin applications for wind turbine efficiency improvement.
Professor Asif Gill is Head of Discipline for Software Engineering at the School of Computer Science, University of Technology Sydney (UTS), where he was promoted to Professor of Computer Science in January 2024. He also serves as Director of the DigiSAS Research and Innovation Lab and is actively involved in the Global Big Data Technologies Centre at UTS. As a founder of both the DigiSAS Lab and the Future Generation Enterprise Architecture Community of Practice (FGEA CoP), he has established integrated teaching-research-engagement frameworks that translate academic research into practical applications while enhancing graduate employment opportunities. Professor Gill's research interests span Adaptive Enterprise Architecture , Agile Software Development , and Design Science Research & Innovation , with a particular focus on architecting large-scale data-intensive enterprise software systems. His work addresses challenges across academia, industry, government, and society, with significant contributions to AI systems architecture, digital identity management, and enterprise knowledge graphs. His applied research has resulted in numerous collaborations with organizations including the Reserve Bank of Australia, Revenue NSW, Capsifi, Data Zoo, and the NSW Department of Planning, Industry and Environment. His publication record includes 3 books and over 190 articles in major academic journals such as IEEE Transactions on Professional Communication, Information and Management, and Information Systems. His recent work demonstrates a consistent focus on cutting-edge topics in enterprise architecture, AI systems, and digital identity, with multiple publications appearing in 2024-2025. His research trajectory shows a clear evolution from foundational work in agile software development toward more sophisticated integration of AI, enterprise architecture, and data governance. Fellow of the Australian Computer Society (ACS) Fellow of DSE (ESCP Center for Design Science in Entrepreneurship) Senior Member IEEE Associate Editor, IEEE Transactions on Technology & Society Associate Editor, Springer Nature Discover Data journals Member, Data Sharing Committee, IFIP Technical Committee 8.1 Member, Standards Australia Software and Systems Engineering Committee IT-015 Professor Gill has successfully secured numerous research grants from 2019-2026, totaling significant funding for projects related to digital identity, enterprise architecture, and AI systems. His approach emphasizes industry-academia collaboration, with many projects involving direct partnerships with government agencies and industry organizations. He has supervised multiple PhD and Master's students through industry-sponsored scholarships and maintains active collaborations with researchers across multiple institutions. Leading the DigiSAS Research and Innovation Lab, Professor Gill has created an environment that bridges theoretical research with practical implementation. The lab focuses on developing frameworks and tools for adaptive enterprise architecture, with particular emphasis on AI-enabled systems, data governance, and digital identity solutions. His work on the Data Satellite Architecture represents a significant contribution to combating data pollution in federated digital ecosystems.
Assoc Prof Henry Nguyen is an Associate Professor at Griffith University's School of Information and Communication Technology, with expertise in data integration, data quality, recommender systems, and big data visualization. He directs the Responsible Big Data Lab and has secured over $3.5M in funding since 2015 from ARC, DFAT, and industry partners. PhD & Master's from EPFL, Switzerland ARC DECRA Award (2020) His research focuses on privacy-preserving AI for social data , IoT , and satellite analytics , with over 200 publications in top venues like SIGMOD, KDD, and IEEE TKDE. Recent work spans federated learning , graph neural networks , and secure AI systems . Article trends highlight 2024-2025 publications on: Federated recommendation security On-device AI optimization Privacy-preserving explainable AI Graph condensation techniques LLM-powered risk analysis Cloud-edge collaboration Scientific contributions include ARC DECRA Award 2020 Multiple senior PC roles in A* conferences Citations in International AI Safety Report 2025 Henry Nguyen supervises 12 active PhD/MSc students and has directed 8 completed doctoral theses . His funded projects include collaborations with Ubitech , KARI , and CSIRO , focusing on Australia-Korea partnerships and responsible AI development.
Jonathan Hersh is an Associate Professor at Chapman University's George L. Argyros College of Business and Economics, specializing in Economics and Management Science. His research bridges artificial intelligence, machine learning, and economics to address business, labor, and societal challenges through diverse data sources like satellite imagery and economic records. Education: University of Chicago (BA), University of Pennsylvania (MS), Boston University (PhD) Research focuses on AI's societal impact, including digital platform strategy, online piracy, and development economics. He has pioneered methods for poverty mapping using satellite data and war destruction analysis with AI. Recent publications explore AI skills gaps in financial institutions, API-driven economic growth, and satellite-based poverty estimation. His work has appeared in Management Science , MIS Quarterly , PNAS , and NeurIPS . Awards: BBVA Foundation Frontiers of Knowledge Award (2023) Previously worked as a data scientist for startups and the World Bank. Teaches AI, machine learning, and development economics to undergraduate and MBA students.
Dr Mahir Arzoky is a Lecturer in the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. He holds a PhD from Brunel University London (2015) and has extensive research experience in artificial intelligence and software engineering. His research focuses on: Artificial Intelligence and Intelligent Data Analysis Search Based Software Engineering (SBSE) Clustering algorithms and heuristic search methods Software refactoring and quality assessment Data mining applications in healthcare and education Analysis of his 15 most recent publications (2018-2022) reveals strong interdisciplinary work bridging computer science with healthcare (diabetes patient modeling, medical imaging) and education (chatbot design, algorithm visualization). His technical focus centers on clustering optimization, refactoring impact analysis, and explainable AI, with frequent use of empirical validation methods. Key collaborations include researchers like Stephen Swift, Steve Counsell, and Giuseppe Destefanis. Dr Arzoky has secured significant research funding through EPSRC grants including: AQUATIC project (EP/M024083/1): Assessing Test Suite Quality in Industrial Code FIAR-NET (EP/N011627/1): Fault Analyses in Industry and Academic Research Network His professional network includes active collaborations across computer science, healthcare informatics, and educational technology domains, with recent work extending into transformer models for healthcare SQL conversion and graph partitioning for software modularization.
Mohit Kumar is an außerplanmäßiger Professor of Computational Intelligence in Automation at the Institute of Automation Technology, University of Rostock. He concurrently serves as a Key Researcher in Data Science at the Software Competence Center Hagenberg, Austria, and as a Visiting Professor at the Georg-August-Universität Göttingen. His research centers on Trustworthy Artificial Intelligence frameworks, specifically developing Explainable AI, Privacy-Preserving AI, and Transferrable AI methodologies. He pioneers fuzzy logic applications in machine intelligence and creates AI-driven analytical systems for complex data, signals, and image processing. This work is rigorously grounded in probability theory, statistical modeling, estimation theory, and robust adaptive filtering techniques. At the Software Competence Center Hagenberg, he leads digitalization solution development through theoretically sound approaches and extensive real-world experimentation to solve critical industrial and societal challenges.
Mohammad Shojafar (M'17-SM'19) is an Associate Professor at the Institute for Communication Systems within the Faculty of Engineering and Physical Sciences at the University of Surrey , UK. He has secured over £1.9M in research funding as Principal Investigator for projects like ORAN-TWIN (EPSRC), PRISENODE (MSCA-IF), TRACE-V2X (MSCA-SE), and D-XPERT (Innovate UK), among others. Previously held positions include Senior Researcher at University of Toronto and Toronto Metropolitan University, Senior Researcher at Italian universities (Telecom Italia Mobile), and Postdoc at University of Padua Key affiliations: Associate Editor for IEEE Transactions on Network and Service Management, Intelligent Transportation Systems, Green Communications and Networking, and Consumer Electronics Magazine Research Specialism: 5G/6G Security and Privacy Open-RAN Security Green Networking Adversarial Machine Learning Applied Cryptography Publication Trends: Focus on Open RAN security challenges (bearer context migration poisoning, KPI poisoning attacks), IoT/Fog security (GAN-based attacks, distributed intrusion detection), Lightweight Cryptography (multi-signature protocols, authentication schemes), and AI-driven Network Optimization (federated learning, reinforcement learning applications). Recent work addresses security in vehicular networks, smart grids, and video streaming frameworks. Scientific Recognition: Marie Curie Individual Fellowship (MSCA-GF-IF) Intel Innovator ACM Professional Member Sustainability Fellow at Institute for Sustainability IEEE Senior Member Supervision: Currently supervising 6 PhD students and has graduated 5 PhD/MSc students since 2021. Active in 5G/Open RAN security research with over 20 related publications since 2022.
