جست‌وجوی استاد و پژوهشگر

Josephine Sullivanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Josephine Sullivan is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning. She is a core faculty member of Digital Futures, a cross-disciplinary research center addressing global challenges through digital technologies. Her research focuses on AI-driven environmental monitoring, disaster management, and deep learning applications in computer vision and remote sensing. She has supervised projects such as the fusion of radar and optical remote sensing for wildfire monitoring and contributes to initiatives like EO-AI4GlobalChange. Sullivan teaches advanced courses in deep learning, generative models, and machine learning at KTH, emphasizing hands-on training and research integration. Her work bridges theoretical advancements in neural networks with practical solutions for climate-related disasters, leveraging satellite imagery and time-series data. Recent projects highlight innovations in wildfire prediction datasets (Wildfirespreadts) and robust networks for disaster damage assessment (DisasterAdaptiveNet). She collaborates across disciplines at Digital Futures, contributing to labs and teams focused on AI ethics, sustainable technologies, and global change mitigation. Sullivan’s academic leadership includes roles as course examiner and responsible for multiple graduate programs in computer science and engineering.

Giulio Rossettiمشاهده پروفایل

پژوهشگر

• Complex Network Analysis
• Data Science
• Dynamic Community Discovery
• +۳ مورد دیگر

Giulio Rossetti is a permanent researcher at the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the National Research Council of Italy (CNR) in Pisa. He is a member of the Knowledge and Data Mining Laboratory (KDD Lab), a joint research group of ISTI-CNR and the University of Pisa. His academic appointments include teaching "Social Network Analysis" and "Programming for Data Science" courses within the Master Degrees in "Data Science" and "Digital Humanities" at the University of Pisa since 2018, and providing teaching support for various data science courses since 2015. His educational background includes: 2007: Bachelor Degree in Computer Science at University of Pisa 2011: Master Degree in Computer Science at University of Pisa 2015: PhD in Computer Science at University of Pisa Dr. Rossetti's research centers on algorithms for complex network analysis and data science, with a focus on modeling dynamics of and on network topologies, including dynamic community discovery and diffusion/spreading phenomena. His work also involves developing data-driven models for forecasting rare events. His research has significant applications in social network analysis, human mobility modeling, and understanding information diffusion patterns in online communities. His recent publications demonstrate a strong focus on dynamic network analysis, community detection in evolving networks, and applications to social media platforms like Reddit. His work spans theoretical algorithm development and practical applications in areas such as political polarization, music preference analysis, and epidemiological modeling. The interdisciplinary nature of his research connects computer science with social sciences, public health, and urban planning. His scientific achievements have been recognized with multiple awards: ISTI Young Researcher Award (2012, 2014, 2017, 2018, 2019) ISTI Young Open Science Award (2020) While the text doesn't explicitly mention PhD students he advises, Dr. Rossetti has been actively involved in teaching and mentoring through his roles in Master's degree programs at the University of Pisa. His research has been supported through various projects including SoBigData++, HumMingBird, SoBigData.eu, MineTheNet, and CIMPLEX, indicating substantial grant funding for his work in social mining and big data analytics. As a core member of the Knowledge and Data Mining Laboratory (KDD Lab), Dr. Rossetti collaborates with researchers from both ISTI-CNR and the University of Pisa. His work connects with international research communities, as evidenced by his visiting scholar position at the Pontifical Catholic University in Rio de Janeiro in 2013 and collaboration with Estonian researchers in 2014.

Sarah Hasanمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Natural Language Processing
• Knowledge Representation
• +۳ مورد دیگر

Sarah Hasan is a Research Fellow in the Department of Computer Science at Aalborg University, Denmark, operating within The Technical Faculty of IT and Design. She is affiliated with the Section for Distributed, Embedded and Intelligent Systems, specifically the Data Engineering, Science and Systems group, focusing on cutting-edge artificial intelligence research at Selma Lagerløfs Vej 300, 9220 Aalborg Øst. Her research expertise spans Artificial Intelligence with concentrated efforts in Natural Language Processing, Knowledge Representation, and Machine Learning. She investigates logical consistency mechanisms in Large Language Models for fact-checking applications, addressing critical challenges like inconsistent responses through knowledge graph integration and retrieval-augmented techniques to enhance model reliability and reasoning capabilities. Her current research trajectory is highlighted by a forthcoming publication at the International Conference on Learning Representations (ICLR 2025), which examines how Large Language Models maintain logical coherence during fact verification tasks. This work demonstrates significant advancements in mitigating response inconsistencies through structured knowledge integration, reflecting broader trends in trustworthy AI development. She actively contributes to the Data Engineering, Science and Systems research ecosystem at Aalborg University, collaborating on intelligent systems projects that bridge theoretical AI research with practical data science applications in distributed computing environments.

• Cybersecurity
• Privacy-Preserving Machine Learning
• Secure Multi-Party Computation
• +۳ مورد دیگر

Xiaoning (Maggie) Liu is a Lecturer (equivalent to US Assistant Professor) at the School of Computing Technologies, RMIT University, where she earned her Ph.D. in Computer Science. Her research focuses on secure systems to address data privacy and security challenges in machine learning, particularly through secure multi-party computation (MPC) and privacy-preserving machine learning (PPML). She applies these techniques to practical domains like medical diagnostics and mobile image classification. Her design philosophy emphasizes lightweight, fundamental protocols leveraging advanced cryptographic methods, such as function secret sharing and zero-knowledge proofs. Research Interests: Secure multi-party computation and its applications in PPML Privacy-preserving medical diagnostics (e.g., MediSC , CryptMed , OblivGNN ) Secure cloud and edge computing systems ( Sonic , EncSIM , Leia ) Cross-domain encryption and collaborative computation Advising & Supervision: Xiaoning supervises Masters and PhD students in research projects such as intrusion detection systems for healthcare IoT devices, defenses against backdoor attacks in neural networks, and privacy-preserving fairness verification. She emphasizes recruiting self-motivated researchers passionate about impactful security work. Interdisciplinary Contributions: Her work bridges computer systems, cryptography, and machine learning to address real-world challenges in areas like MLaaS, medical data collaboration, and mobile-edge inference. Collaborative frameworks like MediSC and Leia

Luis de la Cruz Pirisمشاهده پروفایل

استادیار

• Network Optimization
• Cybersecurity
• Intelligent Transportation Systems
• +۳ مورد دیگر

Luis de la Cruz Piris is an Assistant Professor in the Department of Telematics Engineering at Universidad de Alcalá. His research focuses on network optimization, cybersecurity, and intelligent transportation systems. He is part of the NetIS research group (Networks and Intelligent Systems). He earned his Ph.D. in 2019 with a thesis on multi-objective optimization strategies for vehicle coordination at urban intersections, supervised by Dr. Iván Marsá Maestre and Dr. Miguel Ángel López Carmona. His research interests include wireless network optimization (e.g., Wi-Fi channel assignment), IoT security frameworks, and AI-driven threat mitigation. Notable projects include the EnvAdapt-CRO-SL algorithm for dynamic channel management and CloudWall, a resilient healthcare IT infrastructure framework. Recent work explores unsupervised learning for cybersecurity, cooperative approaches to Wi-Fi performance, and distributed multi-agent systems for network resilience. His publications span topics like smart traffic light management, OAuth token configuration in IoT, and fuzzy ontology-based driver behavior analysis.

• Blockchain Technology
• Data Analytics
• Cybersecurity
• +۷ مورد دیگر

Salvador Sánchez Alonso is a Professor at the Department of Computer Languages and Systems, Universidad de Alcalá. He holds a PhD from Universidad Politécnica de Madrid (2005) under Dr. Miguel Ángel Sicilia Urbán. His research focuses on blockchain technology, cybersecurity, linked data, healthcare informatics, and educational technology. He actively contributes to the I4 research group (Intelligence, Innovation, Internet, and Information), exploring topics like privacy-preserving systems, social network analysis, and open data principles. His work bridges theoretical advancements with practical applications, such as predictive modeling for healthcare innovation, quality assessment of educational resources, and decentralized identity systems. Recent publications highlight innovations in quantum software measurement, KlimaDAO analysis, and ethical AI traceability. He has also explored the impact of political events on social networks and hospital resource management through predictive analytics. Key contributions include frameworks for metadata quality in digital repositories, interlinking educational data with the Web of Data, and applying blockchain for secure metadata deployment. His research often intersects interdisciplinary domains, emphasizing social data visualization and user-centric repository design.

• Computer Science
• Software Engineering
• Data Management
• +۷ مورد دیگر

Miguel Ángel Sicilia Urbán is a Full Professor in the Department of Computer Science at Universidad de Alcalá, Spain. His research focuses on software engineering, data management, and fuzzy systems, with recent work extending into blockchain technology, cryptocurrency, and semantic web applications. He holds a Ph.D. from Universidad Carlos III de Madrid (2003), where his thesis explored adaptive hypermedia models with imperfect information support. Research Interests His expertise spans collaborative filtering algorithms, software cost estimation, and the application of fuzzy logic in database systems. Current projects include metadata traceability, privacy-preserving computing, and the analysis of cryptocurrency markets. Sicilia is also active in semantic web technologies, including linked data integration and knowledge graph development. Publications & Collaborations With over 20 years of research output, Sicilia has authored/co-authored 11 indexed publications since 2001, including foundational work on OWA-based collaborative filtering and Choquet integral aggregation. Notable collaborations include projects with researchers like Elena García on usability criteria modeling and Juan J. Cuadrado-Gallego on software estimation models. Awards & Recognition While no specific awards were explicitly mentioned, his contributions to software engineering and data management have been recognized through multiple co-edited conference proceedings and citations in interdisciplinary fields like medical informatics and environmental economics. Contact Email: msicilia@uah.es

Lee How Chinhمشاهده پروفایل

مدرس ارشد

• Predictive Modelling
• Machine Learning
• Statistical Business Analysis
• +۶ مورد دیگر

Dr Lee How Chinh is a Senior Lecturer (Practice) in the Department of Econometrics and Business Statistics at the School of Business, Monash University Malaysia. He is actively involved in teaching, research, and industry collaboration, particularly in data science and analytics. Education: PhD in Statistics, Universiti Sains Malaysia Master of Applied Statistics, Universiti Putra Malaysia Master of Science in Mathematics, Universiti Kebangsaan Malaysia Bachelor of Science (Hons) in Mathematical Sciences, Universiti Kebangsaan Malaysia Dr Lee's research centers on applying statistical and machine learning methods to business and industrial problems. His key interests include predictive modeling, statistical business analysis, and anomaly detection in operational data. He emphasizes practical applications and bridges academia with industry through training and consultancy. The recent publications reflect a strong trend in statistical process control, AI-driven environmental modeling, and educational technology. His work combines theoretical rigor with real-world impact, particularly in quality control and data-driven decision-making. Scientific Awards and Recognitions: Hadiah Sanjungan from PERSAMA for PhD thesis Professional Technologist (MBOT) SAS Certified Professional: AI and Machine Learning Dr Lee actively supervises students and contributes to academic service as a peer reviewer for journals like IEEE Access and PLoS ONE. He is a chief investigator in an active research project focused on building data-driven organizations using AI and data warehousing. He also collaborates with SAS Institute Malaysia as an accredited trainer, delivering data science programs to industry and government. He is involved in professional communities and serves as a technical reviewer and judge for data science competitions and conferences, contributing to the advancement of the field in Southeast Asia.

Kalman Graffiمشاهده پروفایل

استاد

• Cybersecurity
• Peer-to-Peer Networks
• Opportunistic Networks
• +۴ مورد دیگر

Prof. Kalman Graffi is a Professor of Networks, Communication Systems, and Cybersecurity at Bingen University of Applied Sciences. He holds a Dr.-Ing. from TU Darmstadt and has held academic roles at institutions like the University of Düsseldorf and University of Paderborn. Previously, he was a Principal Scientist at Honda Research Institute Europe GmbH (2019–2022). His research focuses on secure, scalable networks (e.g., overlay networks, robot swarms) and privacy-preserving computing. Notable contributions include work on peer-to-peer frameworks (LibreSocial), threat modeling, and fog computing optimizations for IoT. Awards include the 'Young Scientist of the Year 2014' (Academics.de) and memberships in AGYA and Germany's Young Academy of Sciences. He has authored over 90 publications and supervised 175+ student theses. Key projects include BMBF-funded initiatives on decentralized communication platforms and DFG-supported research on opportunistic networks. His work also bridges technical innovation with societal impact, including collaborations with activists in the Arab world.

Uwe Egbert Wolterمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Category Theory
• Algebraic Specification
• +۱۲ مورد دیگر

Uwe Egbert Wolter is an Associate Professor in the Department of Informatics at the University of Bergen, Faculty of Mathematics and Natural Sciences. He is actively engaged in research at the intersection of theoretical computer science, category theory, and formal methods in software engineering. His research interests include: Theoretical Computer Science Category Theory Algebraic Specification Graph Transformations Formal Methods Coalgebras Diagrammatic Modeling Model-Driven Engineering Multilevel Modeling Logic in Computer Science Wolter’s recent publications focus on diagrammatic specification frameworks, generalized sketches, and coalgebraic modeling. His work explores the use of categorical and algebraic structures to formalize software models, knowledge representation, and logical systems. He frequently collaborates with researchers such as Harald König, Adrian Rutle, Zinovy Diskin, and Alfio Martini. The research trends show a consistent focus on unifying logical and structural aspects of systems using category-theoretic tools. He has no listed scientific awards in the provided text. Wolter has advised or collaborated with several researchers, though formal students are not explicitly listed. His work has been supported through academic collaborations and research projects, including funding from the Research Council of Norway (e.g., project 194521). He has contributed to workshops and conferences in formal methods, algebraic development techniques, and model-driven engineering. He is involved in research groups and collaborative efforts centered on formal modeling, particularly through the Diagram Predicate Framework and multilevel typed graph transformations. His work contributes to foundational aspects of software specification and knowledge representation.

• Machine Learning
• Cloud Computing
• Fault Diagnosis
• +۳ مورد دیگر

Chao Yin is a researcher at Shanghai University , Department of Computer Engineering and Science. His work spans multiple domains including Machine Learning, Cloud Computing, Fault Diagnosis, and Supply Chain Optimization. Key research areas: Machine Learning , Cloud Computing , Quantum Computing , Supply Chain Systems , Network Security Scientific Contributions (2024-2025): Developed heterogeneous graph neural networks for automotive supply chain analysis Created MSDF-VAE cloud-edge fault diagnosis framework using transfer learning Proposed quantum metrology methods with Heisenberg-limited precision Designed LARP pseudonym protocol for V2X communication Optimized fog computing resource scheduling with hybrid metaheuristics Prior Work (2012-2023): Contributed to fluid animation feature preservation from single images Developed label distribution learning for facial age estimation Designed erasure coding storage systems for big data Created multi-agent manufacturing networks in cloud environments

Cindy Chenمشاهده پروفایل

دانشیار

• Spatio-temporal Databases
• Data Mining
• Cloud Computing
• +۴ مورد دیگر

Cindy Chen serves as Department Chair and Associate Professor at the Miner School of Computer and Information Sciences within the Kennedy College of Sciences at the University of Massachusetts Lowell. Her academic journey includes a Ph.D. in Computer Science with specialization in Database and Knowledge Base Management from UCLA (2001), an MS in Computer Science from UCLA, and a BS in Space Physics from Peking University. Ph.D.: Computer Science (Database and Knowledge Base Management), University of California at Los Angeles (2001) MS: Computer Science, University of California at Los Angeles BS: Space Physics, Peking University - Beijing, China Her research spans spatio-temporal databases, data mining, cloud computing, and social network analysis. Early work focused on spatio-temporal data models and query languages , evolving into cloud data warehouse optimization and social network mining (particularly online dating behavior), then advancing to high-dimensional indexing , knowledge graph querying , and recent innovations in temporal database granulation using Allan Variance techniques for CAV friction modeling. Current work integrates database systems with autonomous vehicle infrastructure and real-time urban analytics. Her publication timeline reveals a transition from foundational spatio-temporal database systems (1998-2008) to cloud/social applications (2008-2015), then to granulation techniques and CAV systems (2015-present). Key domains include XML databases, skyline queries, social trend discovery, and friction data modeling for autonomous vehicles. Dean's Fellowship (1998) Dean's Fellowship (1999) Dr. Chen has secured significant research funding including the UMASS Transportation Center grant for an Interactive TMC Decision Support Tool (2008) and a UML Healey Grant Award for Spatio-Temporal Knowledge Discovery in Nuclear Physics (2008). Her collaborative projects span transportation systems, nuclear physics applications, and autonomous vehicle infrastructure. She actively advises on database curriculum development and serves as department chair overseeing academic operations.

Pascal Belouinمشاهده پروفایل

پژوهشگر

• Digital Humanities
• Knowledge Modeling
• Network Analysis
• +۶ مورد دیگر

Pascal Belouin serves as a Digital Humanities Scholar at the Max Planck Institute for the History of Science (MPIWG) in Berlin, part of the Max Planck Society. His role focuses on providing specialized technical support to historians implementing digital methodologies in their research. Belouin holds a Bachelor's degree in Software Engineering from the University of La Rochelle, a Master of Research in Human Computer Interaction from Lancaster University, and a Master of Arts in Social Sciences from the Open University (completed in 2010). His research interests span multiple domains within Digital Humanities, with particular expertise in knowledge modeling, distant reading techniques, network analysis, and data visualization. He is actively exploring innovative approaches to research data preservation through decentralized storage solutions, reflecting his background in software engineering and computer science. Belouin's recent publications demonstrate a consistent focus on research infrastructure development, particularly in the areas of data interoperability, semantic web technologies, and sustainable preservation of research data. His work shows a progression from API development for textual resources toward more comprehensive solutions for long-term data preservation using decentralized approaches. He is actively involved in multiple significant projects at the MPIWG including the Central Knowledge Graph (CKG), CORAL: Commoning Oral Histories of Knowledge, Heavens Map, VoH Image Database, Commoning Biomedicine, Measuring the Earth, and RISE and SHINE: Research Infrastructure for the Study of Eurasia. Before joining the MPIWG, Belouin had significant professional experience in the private sector and held a leadership role with the Medical Schools Council Assessment Alliance, where he spearheaded the design and implementation of the MSCAA Item Bank—a comprehensive web-based platform enhancing collaboration across the UK's medical assessment community.

• Formal Methods
• Graph Databases
• Property Graph Schema
• +۵ مورد دیگر

Ştefania-Gabriela Dumbravă is an Associate Professor at Institut Polytechnique de Paris (ENSIIE/Samovar Laboratory). Her research focuses on formal methods for graph database algorithms , including schema design, query processing, and verification. She contributes to international standards through co-chairing VLDB 2026 Demonstration Track and organizing workshops like VLDB Summer School 2025 . Dumbravă serves on editorial boards (e.g., ACM Transactions on Database Systems ) and program committees for top-tier conferences ( SIGMOD , ICDE ). Research Interests include formal verification of graph databases , property graph schema constraints , certified inference engines , and distributed graph systems . She integrates proof assistants (Coq, Isabelle) with practical database development, emphasizing reliability and mechanical correctness . Her work spans theoretical foundations (e.g., category theory , type theory ) and real-world applications (e.g., transit accessibility analysis , SDN controllers ). Selected Scientific Awards : EASST Best Software Science Paper Award (ICGT 2025) SIGMOD Best Paper Award (2023) VLDB Best Regular Paper Runner-Up (2022) D4Gen Hackathon 3rd Prize Teaching includes Graph Databases , Functional Programming , and Formal Languages at ENSIIE, with additional roles at Université Paris-Saclay, ENS Lyon, and Université Rennes 1. She supervises research internships and participates in gender parity initiatives (e.g., Paris-Saclay Women & Science Mentorship Program).

• Big Data
• Social Network Mining
• Stream Processing
• +۱۲ مورد دیگر

Dr. Thanh Tam Nguyen is a Lecturer at the School of Information and Communication Technology, Griffith University, Gold Coast Campus. He holds a PhD in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and is a Fellow of the Higher Education Academy (FHEA). His academic and research profile is centered on advancing Big Data and Smart technologies through efficient and trustworthy AI systems. His educational background includes a Doctor of Science and a Master of Science, both from EPFL. He is actively involved in high-impact research and has published over 65 papers in top-tier venues such as SIGMOD, VLDB, SIGIR, ICDE, IJCAI, VLDBJ, TKDE, and Pattern Recognition, with over 35 in CORE A* journals and conferences. His work has attracted more than 4,200 citations (h-index 35+). Dr. Nguyen's research focuses on Big Data Analytics, Social Network Mining, Stream Processing, Privacy-Preserving Machine Learning, Recommender Systems, Explainable AI, and Graph Neural Networks . He aims to bridge human insights with data models to ensure transparency and trust in data-driven decisions. His recent work explores misinformation management, machine unlearning, and federated learning, with applications in social networks, healthcare, and sustainable agriculture. The 15 most recent publications reflect a strong trend in privacy-preserving AI, explainability, federated learning, and graph-based modeling . Key themes include machine unlearning, adversarial robustness in recommender systems, heterogeneous graph representations, and trustworthy AI deployment. His work is frequently published in ACM and IEEE venues, indicating sustained excellence in computer science and AI research. Fellow of the Higher Education Academy (FHEA) Dr. Nguyen has secured over $1 million in research funding from government (DFAT, A4I, AKF), industry (CSIRO, Ubitech, Johnson & Johnson), and international bodies (NAFOSTED, ETRI, KARI). He serves as a guest editor for IEEE Journal of Biomedical and Health Informatics, area chair for ACL and EMNLP, and reviewer for top journals like TKDE, JVLDB, and CSUR. He supervises multiple PhD students and collaborates with leading international researchers such as Prof. Karl Aberer (EPFL), Prof. Björn Schuller (Imperial College), and Prof. Hongzhi Yin (UQ). He is a key contributor to the Responsible Big Data Lab at Griffith University and leads projects on misinformation management, AI safety, and sustainable agriculture through AI-powered traceability. His impact extends to policy and industry, particularly in cybersecurity and trustworthy AI adoption.