Dr. Francisco J. Aparicio Navarro serves as Associate Professor in Cyber Security and Programme Leader for BSc/MSc Digital Technology Solutions Degree Apprenticeships at De Montfort University's School of Computer Science and Informatics. An expert in Computer Network Security and Intrusion Detection Systems, his research develops novel unsupervised detection frameworks integrating Contextual Information and Situational Awareness for cyber-attack identification in networked environments. His research spans Network Security, Machine Learning, and Cyber Situational Awareness with specialized expertise in Penetration Testing, Cyber Threat Intelligence, and Incident Response. Key contributions include statistical approaches for multi-stage attack detection, data fusion techniques for anomaly identification, and security solutions for Connected Autonomous Vehicles developed in partnership with Airbus. His work bridges theoretical advances in cryptographic protocols with practical defense mechanisms for critical infrastructure. Recent publications demonstrate evolving focus from foundational wireless intrusion detection (2017-2018) toward sophisticated APT prediction using machine learning correlation analysis (2018-2019), vehicle cybersecurity (2019-2020), and emerging areas like medical imaging watermarking (2023) and lightweight cryptographic solutions (2024-2025). Cross-cutting themes include contextual information integration, IoT privacy compliance, and distributed authentication mechanisms. Scientific recognition includes: Fellowship of the Higher Education Academy (FHEA) He currently supervises PhD candidates Tareq Ahmad Alhajahjeh (Cyber Threats Intelligence using BRNN-LSTM) and Ademola Elijah Owaraye (Zero-Trust Security in Fog Computing), following successful completion of Dr. Raphael Martin Juergen Riebl's thesis on Vehicular AdHoc Networks. Research funding includes Innovate UK projects SACRED (Co-Investigator), INSURE Phases 1-2 (Principal Investigator), and FLOURISH (Co-Investigator with Airbus), alongside EPSRC/Dstl collaborations on networked battlespace security. As Director of the Cyber Security Academic Startup Accelerator Programme, he fosters industry-academia partnerships while maintaining active consultancy in penetration testing and incident response for commercial clients.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۷۲۱-۷۳۵ از ۳٬۵۳۸ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Alagan Anpalagan is a Full Professor in the ELCE Department at Toronto Metropolitan University, previously Ryerson University. He holds a PhD in Electrical Engineering from the University of Toronto. His research focuses on radio resource management (RRM), green communication, IoT networks, and wireless communication systems. He directs the WINCORE Lab, specializing in radio access & networking (RAN) and cross-layer design. Dr. Anpalagan has authored/co-edited multiple books and holds IEEE Fellow status (2024). He has received awards including the Ryerson Sarwan Sahota Distinguished Scholar Award (2022) and the IEEE Canada Outstanding Engineering Educator Medal (2018). He has served in editorial roles for IEEE Communications Surveys & Tutorials and led industry collaborations with companies like Bell Mobility and IBM. Education: B.A.Sc. in Electrical Engineering, University of Toronto M.A.Sc. in Electrical Engineering, University of Toronto Ph.D. in Electrical Engineering, University of Toronto Research Interests: His work spans RRM, energy-efficient networks, cognitive communication, and smart grid technologies. Recent projects include digital twin applications in IoT and 6G-based user localization for emergencies. Publications: His 15 most recent articles include studies on network routing protocols, IoT scheduling with digital twins, and AI-driven disaster response systems. These reflect his focus on integrating AI and edge computing into next-gen communication systems. Awards: Recognized for both research and teaching excellence, including IEEE Fellowships and institutional awards for graduate education and service. Labs/Teams: Lead researcher at the WINCORE Lab, collaborating with academia and industry on wireless resource management and heterogeneous networks. The lab’s alumni network includes over 100 researchers.
- Network Analysis
- Graph Drawing
- Data Visualization
- +۵ مورد دیگر
Beppe Liotta is a Full Professor at the Department of Engineering, University of Perugia. He serves as Rector Delegate for ICT and Digital Agenda. His research spans network discovery, graph drawing, algorithm engineering, and computational geometry . Laurea in Electrical Engineering (1990), Ph.D. in Computer Engineering (1995), both from University of Rome 'La Sapienza' Post-doc at Brown University (1995-1996) Current teaching: Information Visualization and Database Management Systems Liotta has authored over 170 papers and led projects like VisFAN (financial crime detection), VHyXY (large graph visualization), COWA (web traffic analysis), and WhatsOnWeb (web clustering). His work focuses on hybrid visualizations and network robustness . Recent articles highlight his expertise in biological networks , financial activity networks , and one-to-many matched graph visualizations . He has contributed to journals like IEEE Transactions on Visualization and Computer Graphics and conferences including PacificVis and Graph Drawing . Liotta actively participates in scientific service, including editorial roles for the Journal of Graph Algorithms and Applications and program committees for IEEE PVIS 2019.
Shaun West is a Professor at the Lucerne School of Engineering and Architecture (HSLU), specializing in Product-Service Systems and Servitization. He holds roles as lecturer and researcher within the Institute of Innovation and Technology Management (IIT). His work bridges theory and practice in industrial services, focusing on digital transformation, lifecycle management, and value co-creation in manufacturing ecosystems. Education: Holds a background from Imperial College London (1994), with extensive industry experience at AEA Technology/ETSU and National Power before academia. Research Interests: Servitization strategies, IoT-enabled services, digital twins, outcome-based contracts, lifecycle management of PSS, and ecosystem innovation. His work emphasizes translating theory into practical solutions for manufacturing firms. Key Projects: Leads initiatives like Horizon Europe's WILSON project, ReRe circular sourcing research, and Data-to-Action frameworks. Notable contributions include co-creating value propositions via digital twins and optimizing field service operations through data analytics. Awards: Recognized with 'Best new business model' (2015), finalist in 'Most Innovative Supply Chain Strategy' (2017), and contributions to top innovative firms awards. His work on smart services and servitization frameworks has gained industry acclaim. Grants & Collaborations: Involved in EU-funded projects (e.g., Horizon Europe) and industry partnerships. His research often involves collaborative efforts with firms like Sulzer Turbo Services, ABB, and SMEs to implement PSS solutions. Labs/Teams: Active in HSLU's innovation labs focusing on digital servitization, leading teams in service design thinking and ecosystem mapping methodologies. Contributes to global conferences like the Smart Services Summit and servitization conferences.
Sheng Liمشاهده پروفایل
استادیار مدعو
Sheng Li is an Adjunct Assistant Professor in the School of Computing at the University of Georgia (UGA). He holds roles such as Graduate Program Faculty and Courtesy Faculty in the Institute of Bioinformatics. Previously, he served as an Assistant Professor at UGA from August 2018 to July 2022. His research focuses on Machine Learning, Computer Vision, Data Mining, Natural Language Processing, Causal Inference, and User Modeling. Li earned his Ph.D. in Computer Engineering from Northeastern University in 2017, following degrees from Nanjing Institute of Post and Telecommunications in China. His work bridges theoretical advancements and practical applications, including AI solutions for healthcare (e.g., medication adherence monitoring), computer vision for wildlife tracking (fish re-identification), and causal inference methodologies. He has received notable awards like the Fred C. Davison Early Career Scholar Award (2022) and the Aharon Katzir Young Investigator Award (2020). His research is funded by grants from agencies such as the US Department of Defense and NIH, supporting projects like Knowledge-Guided Scene Graph Generation and Reasoning for Visual Understanding. Li’s publications span top venues in AI and computer science, addressing challenges in domain adaptation, trustworthy AI, and multimodal learning. He has collaborated on interdisciplinary projects, including bioinformatics and agricultural NLP. His contributions emphasize both technical innovation and societal impact.
Charalambos Papadakis is an Assistant Professor at the Department of Electrical and Computer Engineering (E.M.E.) of the Hellenic Mediterranean University (EL.ME.PA). His research focuses on Distributed Services and Networks. He holds a PhD from the University of Crete's Department of Computer Science, with a thesis titled "Improving Routing in Unstructured Peer-to-Peer Systems". He earned his B.A. in Computer Science (University of Crete, 2000) and M.A. in Computer Engineering (University of Patras, 2004). Teaching responsibilities include undergraduate courses like "Computer Networks II", "Distributed Systems and Clouds", and "Big Data", as well as postgraduate courses such as "Distributed Systems and Applications". He collaborates with the MNCLab (Multimedia, Networks and Communications Laboratory) at the University of Athens, focusing on Network and Distributed Systems research. Papadakis has authored over 30 publications in international journals/conferences, achieving an h-index of 8 and i10-index of 7. His research spans blockchain systems, graph analysis, and peer-to-peer networks. He actively reviews for academic conferences and journals and has led numerous EU/National R&D projects.
Professor Kyriazis Dimosthenis holds a faculty position at the Department of Digital Systems, University of Piraeus. He earned his diploma in Electrical & Computer Engineering from the National Technical University of Athens (2001) and a cross-disciplinary MSc in Techno-Economic Systems (2004). His academic rank is Professor specializing in service-oriented architectures with a focus on quality of service and workflow management. He has led European projects like BigDataStack, CrowdHEALTH, and CYBELE, addressing challenges in cloud computing, edge computing, and AI-driven solutions for healthcare, finance, and industrial sectors. His research emphasizes resilient service-oriented systems, AI explainability, and human-centric digital transformation. Notable contributions include frameworks for dynamic resource allocation in hybrid cloud/edge environments, AI applications for maritime safety, and data governance solutions for cross-sector integration. He coordinates initiatives such as the Future Internet Architecture Board and Cloud QoS&SLAs, driving advancements in federated data marketplaces and sustainable computing practices. His recent work explores Large Language Model (LLM) applications in financial decision-making, conversational AI for MLOps, and neurosymbolic systems for defect detection. He also investigates XAI methodologies, including VirtualXAI, which leverages GPT-generated personas for explainability assessment. His projects often bridge technical innovation with societal impact, such as the iHELP platform for holistic health records and SmartCHANGE for behavioral change strategies in youth health. Key themes in his publications include bias mitigation in machine learning, dynamic deployment prediction in hybrid cloud settings, and energy-efficient data spaces for mobility. He has contributed to standards like the H2020-funded IRMOS and 5GTANGO, emphasizing interoperability and fault-tolerant architectures. His work frequently intersects with EU policy frameworks, particularly in data governance and ethical AI implementation.
Tijl De Bie is a Senior Full Professor at the University of Ghent, specializing in machine learning, data science, and their applications in bioinformatics, computational social sciences, and HR analytics. He leads the AI and Data Analytics (AIDA) research group within IDLab-ELIS. PhD in Machine Learning (KU Leuven, 2005) Worked at U.C. Berkeley, U.C. Davis, University of Southampton, and University of Bristol His research focuses on foundational aspects of data science, including fairness in AI, network embeddings, and human-centric methodologies. Recent work explores temporal network simulation, bias mitigation, and large-scale career trajectory datasets. Notable awards include an FWO Odysseus Group I grant and three ERC grants (Consolidator, Proof of Concept, Advanced). Current projects involve ethical AI frameworks and dynamic network analysis. Scientific Awards : FWO Odysseus Group I, ERC Consolidator, ERC Proof of Concept, ERC Advanced Grant He collaborates extensively in interdisciplinary research, applying machine learning to social media analysis and financial domains. His team develops open-source tools like EvalNE and Fondue for network embedding evaluation.
Mehwish Alamمشاهده پروفایل
دانشیار
- Machine Learning
- Deep Learning
- Language Models
- +۳ مورد دیگر
Mehwish Alam is an Associate Professor of Language, Knowledge, and Artificial Intelligence at Télécom Paris, part of the Institut Polytechnique de Paris. She leads the Data, Intelligence, and Graphs (DIG) team at the Laboratoire Traitement et Communication de l'Information (LTCI). Her research focuses on machine/deep learning, language models, knowledge graphs, and natural language processing. She is actively involved in organizing workshops such as DL4KG and chairs roles at major conferences like EKAW 2024 and ECAI 2025. Education includes a Ph.D. in informatics from LORIA, INRIA, Nancy Grand-Est, France, and an Erasmus Mundus MSc in Language, Communication & Technology. She has held postdoctoral roles at institutions including Karlsruhe Institute of Technology, CNR Rome, and University of Bologna. Research interests span neurosymbolic AI, knowledge graph embeddings, and applications in cultural heritage and materials science. She supervises multiple PhD students and has mentored interns across institutions like BNP Paribas and Nokia Bell Labs. Her work bridges deep learning and symbolic methods, emphasizing interdisciplinary collaborations.
Jens Ulrik Hansen is an Associate Professor at Roskilde University's Department of People and Technology. His work bridges artificial intelligence, data science, and social epistemology. He holds a PhD and MSc, and his research focuses on explainable AI, machine learning applications in healthcare, social network dynamics, and logical frameworks for multi-agent systems. Hansen participates in interdisciplinary projects like #echopol (studying social media polarization) and ROROGREEN (digital innovation in maritime shipping). He has published widely on topics including AI ethics, expert knowledge integration in ML systems, and opinion dynamics in social networks. Research Interests: Artificial Intelligence & Machine Learning Data Science & Big Data Analysis Explainable AI Social Network Analysis Formal Epistemology Key Projects: #echopol: Echo chambers and polarization dynamics ROROGREEN: Green RORO shipping innovations FeedbackBox: Collaborative workshop methodologies Awards: No specific prizes listed, though his work has received media attention for innovations in AI research and healthcare applications.
- Computer Science
- Database Systems
- Process Mining
- +۴ مورد دیگر
Andreas Manfred Pointner is an Assistant Professor at FH Hagenberg , specializing in interdisciplinary research at the intersection of computer science, healthcare informatics, and software engineering. He leads research in graph databases, process mining, and attribute grammars with applications in healthcare IT and automated data systems. His affiliations include the Web Intelligence and Innovation Laboratory , AIST Center of Excellence , and Medical Engineering/TIMed Center . He has contributed to projects such as RiskAI (risk management in enterprises), PASS (plan analysis automation), and REPO (radiology e-health platforms). Research Interests: Graph database optimization, interoperability in healthcare systems (HL7 standards), fuzzing techniques for software testing, and process mining for audit event analysis. His work bridges theoretical formal methods with practical applications in clinical workflows and automated data cleansing. Notable Contribution: Developed a graph transformation framework for complex data structures Recipient of the Best Paper Award 2022 for contributions to intelligent systems Collaborative Projects: Focus on AI-driven solutions for enterprise risk management and healthcare interoperability He actively participates in international conferences and has supervised projects involving 3D model analysis, contour extraction, and global disease monitoring systems.
Zaiwen Fengمشاهده پروفایل
پژوهشگر
- Data Governance
- Semantic Modeling
- Knowledge Graphs
- +۵ مورد دیگر
Zaiwen Feng is a researcher actively contributing to data governance, semantic modeling, and causal inference. His work focuses on knowledge graphs, graph-based methods, and service-oriented architectures through collaborations with institutions like the University of Queensland and universities in China. Research Focus : Graph Differential Dependencies, Entity Resolution, Causal Effect Estimation, and Ontology Alignment Methodologies : Machine Learning, Variational Autoencoders, Prompt Engineering, and Semantic Retrieval Application Areas : Biomedical Data, Property Graph Recommendation, and Process Model Repositories Key trends in his publications include automated semantic modeling , neural approaches for entity resolution , and causal inference with graph structures . He frequently collaborates with researchers like Keqing He, Wolfgang Mayer, and Selasi Kwashie across conferences such as HPCC, BIBM, and WISE.
Jun Liu is a Professor in the Department of Statistics at Harvard University, renowned for his contributions to computational statistics, bioinformatics, and Bayesian methods. He leads research in statistical genetics, genomic data analysis, and algorithm development for biological systems. His work integrates advanced statistical theory with computational tools, such as the Gibbs Motif Sampler and Bayesian Aligner, widely used in bioinformatics. Research interests include Monte Carlo methods, statistical genetics, and machine learning applications in biology. He has developed influential software tools like BPPS, MDScan, and CLIC, addressing problems in motif discovery, genomic sequence analysis, and pathway expansion. Liu’s interdisciplinary approach bridges statistics and computational biology, with applications in cancer genomics, immune repertoire analysis, and evolutionary biology. Notable recognition includes fellowships from the American Statistical Association, Institute of Mathematical Statistics, and International Society for Bayesian Analysis. He advises numerous Ph.D. students and postdoctoral researchers, many of whom hold academic and industry positions globally. His lab collaborates internationally, organizing workshops on Monte Carlo methods and statistical forums in China. Liu’s publications span statistical methodology, computational biology, and genetics, with recent work on genomic element evolution, immune cell profiling, and algorithmic advancements in high-dimensional data analysis. He emphasizes inverse modeling and Bayesian approaches to tackle complex biological questions.
Tao Yang is a Professor in the Department of Computer Science at the University of California, Santa Barbara, where he has been a faculty member since 1993. His research spans web search and mining, database and information systems, machine learning and data mining, parallel and distributed systems, and cloud computing. He serves as an active educator, teaching courses including CS170 Operating Systems (Spring 2024), CS291A Neural Information Retrieval (Fall 2024), and CS140 Parallel Computing (Winter 2025). PhD in Computer Science, Rutgers University ME in Artificial Intelligence, Zhejiang University MS in Computer Science, Rutgers University BS in Computer Science, Zhejiang University Professor Yang's research focuses on advancing the field of information retrieval with particular emphasis on neural approaches to search and ranking. His recent work explores neural document ranking, privacy-aware search systems, and versioned data search. He has led significant projects including Neptune clustering infrastructure, Sorrento self-organizing storage cluster, and TMPI for MPI execution optimization. His research bridges theoretical advances with practical implementations, particularly in scaling search architectures to handle billions of documents while maintaining relevancy, performance, and freshness. His publication record shows a clear evolution from foundational work in parallel and distributed systems toward contemporary research in neural information retrieval. Recent publications demonstrate expertise in optimizing both sparse and dense retrieval methods, with particular focus on efficiency improvements for multi-vector representations. His work consistently addresses real-world challenges in search scalability and privacy preservation. Faculty Research Award, Google Research Research Initiation Award, NSF (1994) UC Regents' Junior Faculty Award (1994) Computer Science Faculty Teacher Award (1995) CAREER Award, NSF (1997) Noble Jeeviant Award, AskJeeves (2002) Professor Yang has supervised numerous graduate students, many of whom have gone on to prominent positions at companies like Google, Apple, and Coursera, or academic positions at universities worldwide. His industry experience as Chief Scientist for Ask.com (2001-2010) and founding Chief Scientist for Teoma (2000-2001) has informed his research direction and provided valuable practical context for his academic work. He has served on program committees for major conferences including WWW, SIGIR, KDD, WSDM, CIKM, ECIR, and EMNLP. His research group maintains active projects in neural information retrieval, privacy-aware search, similarity computing, and parallel computing systems. The group collaborates closely with industry partners, particularly in the search technology space, and has developed systems that power major search engines serving over 100 million users.
- Computer Science
- Machine Learning
- Data Science
- +۳ مورد دیگر
Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.
فقط ۷۵۰ نتیجه اول نمایش داده میشود. برای رسیدن به نتایج دقیقتر، جستوجو را با فیلترها محدودتر کنید.