Nicolò Cesa-Bianchi is a Professor of Computer Science at the University of Milan, Department of Computer Science (Dipartimento di Informatica), and affiliated with the DEIB Department at Politecnico di Milano. His research focuses on foundational aspects of machine learning, particularly online learning, multi-armed bandits, reinforcement learning, and graph analytics. He is an ELLIS Fellow and a corresponding member of the Accademia Nazionale dei Lincei. Research interests include the design and analysis of algorithms for prediction, clustering, and online decision-making, with applications to digital markets, social networks, and bioinformatics. Notable contributions span cooperative online learning, multitask learning, and bandit algorithms. He co-authored the influential book Prediction, Learning, and Games (2006). Professional roles include Board member of ELLIS, co-director of the Milan ELLIS unit, and involvement in EU initiatives like ELSA (Secure & Safe AI) and ELIAS (AI for Sustainability). He teaches graduate courses on statistical methods, machine learning, and reinforcement learning, with a focus on theoretical foundations. Key awards: ELLIS Fellowship (2020), Corresponding Member of the Accademia Nazionale dei Lincei (Italian National Academy of Sciences). His work bridges theory and practice, addressing challenges in adaptive systems, market design, and algorithmic fairness. Current projects explore distributed learning, regret minimization in adversarial environments, and interpretable models.
Dr. Milena Dobreva is a Senior Lecturer in Information Behavior at the Department of Computer and Information Sciences, University of Strathclyde, Scotland. She holds roles in the Faculty of Science and is affiliated with the Strathclyde iSchool. Her work focuses on innovation diffusion in cultural and scientific heritage sectors, media literacy, and combating misinformation. She has led projects funded by the European Commission, including the Bulgarian Romanian Observatory of Digital Media and the LEARN & EXCHANGE initiative to develop international MOOCs on digital transformation. Her research explores user needs in digital heritage, open innovation tools for cultural institutions, and digital skills training in Sub-Saharan Africa. Professional Activities Principal Investigator on projects addressing digital divide, GLAM innovation labs, and disinformation policy. Member of the Scientific Board of DARIAH and the Europeana Network Association. Peer reviewer for journals like Transformations: A DARIAH Journal and conferences such as ACM/IEEE-CS JCDL. Research Interests Innovation labs for GLAM sectors (Galleries, Libraries, Archives, Museums). Data spaces and digital transformation frameworks for cultural heritage. Information behavior in the context of disinformation and media literacy. Open science infrastructures and policy implementation. Grants & Projects Bulgarian Romanian Observatory of Digital Media (2022–2025): Combats disinformation through regional collaboration. LEARN & EXCHANGE (2022–2024): Develops multilingual MOOCs for digital transformation skills. eCHOIng (2021–2024): Uses open innovation to revitalize cultural heritage institutions. Labs & Collaborations Co-leads GLAM innovation labs research, focusing on digital divide mitigation and heritage digitization. Active in the CEDCHE (Common European Data Space for Cultural Heritage) initiative.
Paul Franzon is the Cirrus Logic Distinguished Professor and Associate Department Head for Graduate Affairs at the Department of Electrical and Computer Engineering, North Carolina State University. He holds a PhD and Bachelor's in Electrical Engineering and a Bachelor's in Physics/Mathematics from the University of Adelaide, Australia. His research focuses on quantum information science, machine learning-driven hardware design, 3D integration, and high-speed systems. Education: PhD in Electrical Engineering, University of Adelaide (1988) Bachelor's in Electrical Engineering, University of Adelaide (1984) Bachelor's in Physics and Mathematics, University of Adelaide (1982) Research Interests: Quantum computing and algorithm optimization AI-driven design automation for 3D integrated circuits High-speed communication systems Hardware security and FPGA acceleration Awards & Honors: IEEE Fellow (2006) Alcoa Foundation Distinguished Engineering Research Award (2005) NC State Alumni Distinguished Undergraduate Professor Award (2003) NSW Australia Expatriate Scientist Award (2003) Advising & Grants: Advised PhD student Priyank Kashyap (2023 graduate) Recipient of NSF Young Investigators Award (1993) Labs & Collaborations: Center for Advanced Electronics Through Machine Learning (CAEML) IEEE EPS Society (Associate Editor)
Emanuele Crisostomi is Associate Professor of Electrical Systems at the University of Pisa's Department of Energy, Systems, Territory and Constructions Engineering. He holds PhD and graduate degrees in Automation Engineering from the University of Pisa, with research visits to University of Cambridge, University of Leicester, and Hamilton Institute. His research bridges control theory, energy systems, and smart mobility. His research integrates: Smart grid control and renewable energy integration Electric vehicle charging infrastructure and routing algorithms Urban traffic modeling and optimization Nonlinear system dynamics and Lyapunov stabilization Machine learning applications in energy systems Crisostomi's publications demonstrate consistent innovation in electric vehicle-grid integration, with recent focus on predictive charging control, highway EV circulation patterns, and hydropower monitoring. His work shows increasing application of deep reinforcement learning for multi-regional power systems and hybrid network models for infrastructure monitoring. He leads research on IoT-enabled energy efficiency in buildings and contributes to epidemic modeling through network science. Current projects examine battery sizing optimization, charging station scheduling under uncertainty, and synergetic building management systems integrating environmental monitoring with energy conservation.
Yubai Yuan is an Assistant Professor of Statistics at the Pennsylvania State University, affiliated with the Department of Statistics within the Eberly College of Science. He holds a PhD from the University of Illinois Urbana-Champaign (2020) and completed postdoctoral research at UC Irvine. His research focuses on network science, causal inference, and statistical machine learning, with applications to neuroscience and social systems. Notable awards include the 2022 NSF-Simons Center Fellow Award and the 2019 ASA Student Paper Award. Education: PhD in Statistics (UIUC, 2020), MS in Statistics (Sun Yat-sen University, 2016), BS in Mathematics (Shandong University, 2012). Research interests span complex network analysis, optimal transport, active learning, and mediation analysis. Current projects include de-confounding causal inference and hypergraph modeling. Teaching includes courses on probability theory and statistical modeling at Penn State. Advises PhD students Yuanchen Wu (active learning on graphs) and Siyu Huang (latent network structures). Collaborates with the Center for Social Data Analytics and organizes workshops in statistical network science. Publications emphasize methodological advancements in network analysis, causal pathways, and data integration. Recent work addresses disaster response via social media data and neuronal activity analysis using optimal transport frameworks.
Dr. Takahiro Yabe is an Assistant Professor at the Department of Technology Management and Innovation (TMI) and the Center for Urban Science + Progress (CUSP) within New York University's Tandon School of Engineering. His research focuses on computational social science and network science approaches to model urban resilience against disasters, pandemics, and technological disruptions. He holds a Ph.D. from Purdue University (2021) and degrees from the University of Tokyo (BS 2015, MS 2017). Previously, he was a Postdoctoral Associate at MIT's IDSS and Media Lab under Sandy Pentland and Esteban Moro. Research interests include urban resilience, human mobility, socioeconomic networks, inequality, and computational modeling. He leads the Resilient Urban Networks (RUN) Lab, an interdisciplinary group developing data-driven tools for urban systems analysis. His work has been published in top journals like Nature Human Behaviour, PNAS, and Nature Machine Intelligence. Recent grants include NSF funding for EV charging infrastructure planning (SAI 2024) and post-disaster mobility governance (HDBE 2024). Notable awards include the NICE STEP Researchers recognition (2024). His lab collaborates with urban planners, policymakers, and global institutions to advance equitable urban resilience strategies. Current projects involve open mobility data standards, disaster recovery policy assessment, and AI-driven urban modeling. Students supervised include PhD candidates Vaidehi Raipat (urban policy), Callie Clark (mobility equity), and DongHak Lee (socioeconomic networks). The lab also hosts visiting scholars like Mavin De Silva (EV charging systems) and supports master's students in mobility analysis and disaster response.
Simon J. Puglisi is a Professor in the Department of Computer Science at the University of Helsinki. His research focuses on algorithms, data structures, pattern matching, and data compression, with applications in bioinformatics and genomics. He has collaborated extensively with researchers in the field, including Travis Gagie, Juha Kärkkäinen, and Andrew Turpin. His work spans theoretical computer science and practical applications in genomic data processing and efficient indexing techniques. Key research interests include genome sequencing algorithms, efficient compression methods (e.g., Lempel-Ziv and relative Lempel-Ziv), and the development of succinct data structures for handling large genomic datasets. His contributions to suffix arrays, wavelet trees, and de Bruijn graphs have advanced computational methods in bioinformatics and information retrieval. Puglisi's articles frequently address challenges in text indexing, error correction in short-read sequencing, and optimizing algorithms for scalability. He is known for his work on self-indexing techniques and the SHREC error correction method for genomic data. His research bridges theoretical algorithm design with real-world applications in high-throughput sequencing and large-scale data management.
Wilfried Gansterer is a Professor at the Faculty of Computer Science, University of Vienna, leading the Theory and Applications of Algorithms research group. His work focuses on numerical algorithms, distributed computing, and machine learning, with notable contributions to graph neural networks and fault-tolerant systems. Active in projects such as Algorithmic Data Science for Computational Drug Discovery (2020–2028) and REPEAL (Resilience vs. Performance in Numerical Linear Algebra, 2016–2020). Research Interests: Dr. Gansterer’s expertise spans graph neural networks, matrix compression, adversarial defense mechanisms, and high-performance computing. His work addresses challenges in efficient computation, resilience against node failures, and optimizing distributed systems. Projects : Algorithmic Data Science for Computational Drug Discovery (2020–2028) REPEAL: Resilience vs. Performance in Numerical Linear Algebra (2016–2020) Verteiltes Rechnen (Distributed Computing, 2007–2014) Awards : 2023 Best Paper Award for work on Crossfire: An Elastic Defense Framework for Graph Neural Networks. Labs/Teams : Directs the Theory and Applications of Algorithms group, focusing on algorithmic innovation in distributed and high-performance computing environments.
Sotirios K. Goudos is a Professor at the Department of Physics, Aristotle University of Thessaloniki (AUTH), Greece, and Director of the ELEDIA@AUTH lab within the ELEDIA Research Center Network. His research focuses on antenna design, evolutionary algorithms, wireless communications, machine learning, and IoT applications. He holds a B.Sc. in Physics (1991), M.Sc. in Electronics (1994), Ph.D. in Physics (2001), and additional qualifications in Information Systems and Electrical Engineering. Prof. Goudos is a Senior Member of IEEE and serves as Editor-in-Chief of the Telecom open access journal (MDPI) and Associate Editor for IEEE Transactions on Antennas and Propagation, IEEE Access, and IEEE Open Journal of the Communication Society. He has organized multiple special issues in journals like EURASIP Journal on Wireless Communications and Networking and has authored/edited books on antennas and AI in networks. His awards include multiple IEEE Access Outstanding Associate Editor recognitions (2019–2023) and inclusion in Stanford University's top 2% scientists list (2020–2024). He teaches courses on telecommunications, Java programming, and microwave systems, and has supervised over two dozen master's students since 2009. His work spans antenna optimization, AI-driven communications, and IoT security, with contributions to 5G/6G, RIS systems, and smart agriculture. Prof. Goudos actively contributes to IEEE Greece Section leadership roles, including Secretary (2022) and Vice-Chair (2023–2024). His labs and teams focus on ELEDIA's research in electromagnetics, optimization, and AI applications.
Nitisha Jain is a Postdoctoral Researcher at King's College London's Department of Informatics, part of the Faculty of Natural, Mathematical & Engineering Sciences. She holds a PhD in Knowledge Graphs from the Hasso Plattner Institute (University of Potsdam) and a Master's in Research from the Indian Institute of Science (IISc). Her research focuses on neuro-symbolic AI, ethical AI standards, multimodal knowledge graphs, and knowledge engineering using large language models. Her work includes contributions to the Croissant metadata standard for machine-readable datasets and the development of interpretable embeddings aligned with semantic aspects. She has published extensively in venues like NeurIPS, ACL, and ISWC, and serves on program committees for conferences such as ESWC and DEEM. Key achievements include a spotlight paper at NeurIPS 2024 and a best paper award at DEEM 2024. She supervises students in areas like knowledge graph embeddings and neuro-symbolic methods, and has taught courses on network data analysis and knowledge engineering at both bachelor’s and master’s levels.
Mohammad Kamrul Hasan is an Associate Professor and Head of the Network and Communication Technology Research Lab at the Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM). He holds a Ph.D. in Electrical and Communication Engineering from the International Islamic University Malaysia (IIUM) and has over a decade of prior industry experience in communication systems and network design. He has held academic positions at Universiti Malaysia Sarawak and IIUM, and is currently active in research and leadership at UKM. Ph.D. in Engineering (Electrical and Computer Engineering), International Islamic University Malaysia, 2016 M.Sc. in Communication Engineering, International Islamic University Malaysia, 2012 His research focuses on cutting-edge areas in network and communication technologies. Key interests include Wireless Communication and Network Security , Industrial Internet of Things (IIoT) , Cyber-Physical Systems , 5G and Beyond (6G) Networks , Smart Grids , and AI-driven security . He explores machine learning, federated learning, blockchain, and optimization algorithms to enhance network resilience, privacy, and efficiency in critical infrastructure and consumer electronics. His recent publications (2023–2025) demonstrate a strong trend toward intelligent and secure next-generation networks. Topics include intrusion detection in IIoT, passwordless authentication, federated learning for healthcare IoT, 6G security, and digital twins for SCADA systems. His work is frequently published in high-impact IEEE and Springer journals, reflecting a consistent and influential research output. Gold Medal for research excellence Young Scientist Award Fulbright Scholarship (Ministry of Higher Education Malaysia) Senior Member, IEEE (since 2013) Member, Institution of Engineering and Technology (IET) Member, Internet Society Dr. Hasan has served as an editorial member for prestigious journals including IEEE, IET, and Elsevier. He has led funded research projects such as the design of a two-way wireless communication system for medium-voltage electrical networks at Universiti Malaysia Sarawak. He has mentored students and collaborated widely, with co-authors from Malaysia and international institutions. He has also contributed to professional service as Chairperson of the IEEE IIUM Student Branch and as a peer reviewer for over 13 journals including Computer Networks , Internet of Things , and Soft Computing . He leads the Network and Communication Technology Research Lab at UKM, focusing on secure, intelligent, and scalable communication systems for smart cities, industry, and healthcare. His team works on AI-powered intrusion detection, blockchain for critical infrastructure, and privacy-preserving data fusion in IoT environments.
Dr. Malcolm Heywood is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the Network Information Management and Security (NIMS) Lab and is actively involved in research on genetic programming, coevolution, reinforcement learning, and big data analytics. His research interests span: Genetic Programming and Evolutionary Computation Coevolution and Competitive Learning Problem Decomposition and Hierarchical Models Streaming Data Analysis and Anomaly Detection Network Security and Insider Threat Detection Reinforcement Learning in Games (Atari, ViZDoom, Dota 2) Dr. Heywood's recent publications focus on emergent behaviors in reinforcement learning using Tangled Program Graphs (TPG), benchmarking genetic programming for streaming data, and applications in cybersecurity and computational finance. His work demonstrates a strong trend toward scalable, efficient evolutionary models for complex, real-world problems. His scientific awards include: Silver placed at Human-Competitive (Humies) Competition (2018) Best Paper at EuroGP (2017) Best Paper at DETA track, ACM GECCO (2017) Best Paper at RWA track, ACM GECCO (2018) Nomination for Best Paper at DETA track, ACM GECCO (2019) He has supervised numerous graduate students, including PhD and Master's candidates, many of whom have continued research in evolutionary computation. His lab has developed open-source code distributions for Tangled Program Graphs and Symbiotic Bid-Based GP. Dr. Heywood teaches courses in Computer Organization, Introduction to AI with Gaming Applications, and Genetic Algorithms and Programming.
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 .
Dr. Valentina Tamma is a Lecturer in the Department of Computer Science at the University of Liverpool. Her research focuses on ontologies in open and distributed environments, including Semantic Web, Multi-Agent Systems, and Knowledge Graphs. She leads the Knowledge-Based Agents research group and serves as Area Editor for the Transactions on Graph Data and Knowledge journal. Her recent work explores the intersection of Generative AI and Semantic Web technologies, including competency question engineering, ontology alignment, and knowledge evaluation frameworks. She has co-authored 15+ publications (2023-2025) on topics like ontology reuse, question difficulty prediction, and autonomous agent governance. Dr. Tamma has held significant editorial and organisational roles, including Programme Co-Chair for ISWC 2020 and co-chair of the Alan Turing Institute's Knowledge Graphs Interest Group. She teaches modules such as Ontologies and Semantic Web (COMP598) and Planning Your Career (COMP221). Scientific contributions include: Ontology modularization techniques Negotiation protocols for semantic alignment Knowledge evaluation frameworks AI-driven competency question generation Knowledge Graph applications in life sciences
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.
