جست‌وجوی استاد و پژوهشگر

• Digital Innovation Platforms
• Blockchain Technology
• Fintech
• +۳ مورد دیگر

Ahmad Ghazawneh is a Senior Lecturer at the School of Information Technology , Halmstad University. His research explores the intersection of digital innovation platforms, blockchain technology, and fintech, emphasizing transformative impacts on financial systems and digital economies. Email: ahmad.ghazawneh@hh.se Research Focus: Dr. Ghazawneh investigates blockchain-based financial ecosystems, knowledge graph integration in healthcare, and platform dynamics across multiple domains including mobility systems and social media affordances. Scientific Trends: Recent publications demonstrate expertise in federated health data systems, token-based blockchain ecosystems, conversational AI for healthcare, and sustainable mobility platforms. His work bridges theoretical platform economics with practical implementations.

• Geoinformatics
• Big Data
• GIS Applications
• +۴ مورد دیگر

doc. dr Mladen Amović is an Associate Professor at the Department of Geodesy within the Faculty of Architecture, Civil Engineering and Geodesy at the University of Banja Luka. His academic career includes roles such as Visiting Assistant Professor and Senior Researcher. He specializes in geoinformatics, big data management, and spatial data systems. Education: Advanced degrees in geodesy and related fields (not explicitly detailed in text). Research Interests: Smart city data systems, remote sensing applications, GIS for cadastral systems, and geospatial policy compliance. His work focuses on spatiotemporal data analysis, big data frameworks, and geospatial infrastructure. Recent publications address smart city challenges, disease epidemiology, and agricultural monitoring systems. He has led projects like the 'Central Geospatial Database Analysis' and contributed to initiatives aligning with EU INSPIRE Directive. His research includes collaborations on flood prediction, mineral resource detection, and cadastral portal development. Notable projects include the 'Dokumentovanje arhitektonskog kulturnog nasljeđa' project funded by the BiH Ministry of Civil Affairs and the 'Decarbonization of Energy Sector' initiative.

• Software Language Engineering
• Smart Cities
• Urban Management Platforms
• +۳ مورد دیگر

Rodrigo Simões serves as a Guest Assistant at the Department of Information Science and Technology (ISTA) and as a Research Assistant at ISTAR-Iscte - Research Center in Information Sciences, Technologies and Architecture, both part of ISCTE—University Institute of Lisbon. He teaches Programming and Big Data Processing courses for the Bachelor's in Data Science program and contributes to software engineering research with practical urban applications. His academic qualifications include: PhD in Information Sciences and Technologies (expected 2028) from ISCTE—University Institute of Lisbon Master's degree in Computer Engineering (2024) from ISCTE—University Institute of Lisbon Bachelor's degree in Computer Engineering (2018) from New University of Lisbon Rodrigo's research focuses on applying Software Language Engineering to urban management platforms and smart cities. His expertise spans data visualization, geotemporal analysis, and tourism digital transformation, developing practical tools to address overtourism challenges for municipal authorities and tourism SMEs. His technical background combines academic research with over three years of industry experience in software development for Portugal's Agency for Administrative Modernization. His publication portfolio demonstrates consistent focus on tourism crowding management and visualization, with recent work examining pedestrian carrying capacity using OpenStreetMap data and developing digital transformation frameworks for tourism management. These publications appear in reputable conferences including ENTER International eTourism Conference and Joint International Conference on Digital Arts, Media and Technology, showing growing citation impact across Scopus and Google Scholar. Rodrigo actively participates in significant research initiatives including the EUROSTIT project where he develops chatbots for querying smart tourism tools, and the European RESETTING project focused on tourism digital transformation. His Master's thesis developed a geotemporal crowding visualization platform specifically designed to help tourism SMEs mitigate overtourism effects through data-driven decision making.

Harry Kai-Ho Chanمشاهده پروفایل

مدرس

• Data Mining
• Data Science
• Big Data
• +۷ مورد دیگر

Dr. Harry Kai-Ho Chan is a Lecturer in Data Science at the University of Sheffield's School of Information, Journalism and Communication. He holds a BEng, MPhil, and PhD from The Hong Kong University of Science and Technology (HKUST). His research focuses on data mining, big data analytics, and spatial data management, with particular emphasis on spatio-textual data, indoor location-based services, and applying machine learning in databases. Before joining Sheffield in 2022, he was a Postdoc researcher on the "Data Management for Indoor Location-based Services" project at Roskilde University (2020-2022). He currently serves as the Interim Deputy Programme Coordinator for the BSc Data Science program and the Department Employability Lead for Data Science. His work addresses efficient query processing for spatial data, co-location pattern mining, and uncertainty-aware systems in indoor environments. He has contributed to over 15 peer-reviewed publications in top venues like IEEE TKDE and VLDB. Teaching responsibilities include courses like INF114 (Communicating Data), INF217 (Databases and beyond), and INF4000 (Data Visualization). He actively reviews for leading conferences (e.g., SIGKDD, ICDE) and journals (e.g., IEEE TKDE, GeoInformatica).

• Linked Data
• Statistical Data Integration
• Data Mashups
• +۴ مورد دیگر

Tuan Dat Trinh is a Researcher at Vienna University of Technology's Network Lab, holding a Dr.techn. degree in technical sciences. His work bridges academic research and practical data integration solutions within the university's engineering ecosystem. His primary research focuses on Linked Data processing , statistical data integration , and widget-based mashup platforms . Key contributions include the Linked Widgets framework for open data exploration and StatSpace for statistical data analysis, emphasizing user-friendly interfaces that lower technical barriers for non-experts. His methodology combines semantic web technologies with innovative interaction paradigms like drag-and-block interfaces and autocomplete annotation systems. Analysis of his 15 most recent publications (2014-2017) reveals three dominant research trajectories: (1) environmental data stream integration for real-time monitoring systems, (2) statistical data cube exploration through widget composition, and (3) collaborative mashup approaches for data journalism. His work consistently addresses the tension between data complexity and user accessibility, particularly in open government and public sector contexts. As a core member of the Network Lab, Trinh contributes to developing next-generation data integration infrastructure where semantic technologies meet practical usability requirements. His collaborative projects with researchers like Wetz, Do, and Tjoa demonstrate strong interdisciplinary engagement across computer science and domain-specific applications.

• Geospatial Data Management
• Database Systems
• Cluster Computing
• +۵ مورد دیگر

Jia Yu is a researcher affiliated with Arizona State University , Tempe, AZ, USA. Their work focuses on geospatial data management, database systems, and cluster computing frameworks like Apache Spark. They have collaborated extensively with Mohamed Sarwat and other researchers on projects such as GeoSpark , GeoSparkViz , and GeoSparkSim , contributing to scalable spatial data processing and visualization systems. Key research areas include Learned indexing mechanisms (e.g., GLIN) Microscopic traffic simulation Parallel and distributed data processing Interactive geospatial dashboards Column correlation exploitation for database efficiency Integration of visualization with backend data systems Recent publications (2014-2024) demonstrate expertise in geospatial analytics, database indexing, software testing, and Apache Spark-based systems. Notable projects include Turbocharging Visualization Dashboards , HERMIT Indexing , and Spindra Knowledge Graph Management . Work emphasizes both theoretical innovation and practical implementation for handling massive-scale spatial data.

• Data Management
• Database Systems
• Data Discovery
• +۶ مورد دیگر

Raul Castro Fernandez is a prominent researcher in data management and database systems, with a focus on data discovery, integration, and marketplaces. He has collaborated extensively with leading institutions and researchers, contributing to projects like Data Station and Nexus for secure data sharing. His work bridges theoretical innovation with practical implementations in cloud optimization, differential privacy, and LLM-driven data tools. Key Contributions : Data market frameworks, LLM applications in databases, differential privacy platforms Collaborators : Yue Gong, Samuel Madden, Michael Stonebraker, Eugene Wu, Kyle Chard Research Themes Fernandez explores automated metadata management for data catalogs, spatiotemporal data sharing with privacy guarantees, and LLM-based data discovery . His work on stateful stream processing (e.g., SABER system) and cost optimization in cloud analytics shows technical depth. Recent Trends 2023-2025 publications highlight his pivot toward LLM applications in data management, including tabular data representation and hypothesis assessment tools. He also investigates sustainability in HPC through carbon credit systems.

Bryan HOOI Kuen-Yewمشاهده پروفایل

استادیار

• Machine Learning
• Graph Algorithms
• Anomaly Detection
• +۷ مورد دیگر

Bryan HOOI Kuen-Yew is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the School of Computing and the Institute of Data Science. His research focuses on enhancing the reliability and applicability of machine learning systems, particularly in trustworthiness, graph learning, and real-world applications such as cybersecurity and biomedical informatics. He holds a PhD in Machine Learning from Carnegie Mellon University (2019), and dual MSc (Computer Science) and BSc (Mathematics) degrees from Stanford University (2014). His work has been recognized with awards including the KDD 2016 Best Paper Award (Research Track) and the ECML-PKDD 2018 Runner-Up Best Student Data Mining Paper Award. Research Interests: Trustworthy AI: Mitigating hallucinations, biases, and distribution shifts in large language models. Graph Algorithms: Developing robust methods for graph mining, anomaly detection, and fraud detection in dynamic networks. Multimodal Learning: Leveraging vision-language models and knowledge graphs for improved reasoning and security. Biomedical Applications: Applying AI to healthcare challenges, including drug design and medical data analysis. Selected Awards: ECML-PKDD 2018 Runner-Up Best Student Data Mining Paper Award KDD 2016 Best Paper Award (Research Track) NUS Computing Teaching Excellence Award (2024) Advising and Grants: Actively supervises PhD and master’s students, with research supported by grants focusing on AI security, graph learning, and trustworthy systems. Collaborates on projects such as PhishAgent (multimodal phishing detection) and UniGraph (cross-domain graph foundation models).

Mubarak Shahمشاهده پروفایل

استاد

• Computer Vision
• Video Surveillance
• Visual Tracking
• +۷ مورد دیگر

Dr. Mubarak Shah is the Trustee Chair Professor of Computer Science and Founding Director of the Center for Research in Computer Vision (CRCV) at the University of Central Florida. His research focuses on computer vision, including video surveillance, visual tracking, human activity recognition, and UAV video analysis. He has held prestigious fellowships from ACM, NAI, AAAS, IAPR, and IEEE. Roles: Trustee Chair Professor, Director of CRCV, Graduate Faculty Member Affiliations: University of Central Florida, College of Engineering and Computer Science Research interests span visual analysis of crowded scenes, video registration, and privacy-aware diffusion models. He has pioneered geolocalization techniques and multimodal learning frameworks. His work impacts security, autonomous systems, and medical imaging. Recent publications emphasize diffusion models, 3D object detection, and adversarial learning. His work has been presented at CVPR, ECCV, and NeurIPS. Awards: Pegasus Professor (2006) ACM SIGMM Technical Award (2019) Multiple fellowships and recognitions from IEEE, AAAS, and others Advising and mentorship include over 50 graduate students and NSF REU programs. He leads CRCV, a hub for interdisciplinary vision research, and contributes to high-impact datasets like MAVREC. Labs/Teams: CRCV hosts cutting-edge projects in geolocalization, action recognition, and medical vision. Collaborative efforts include global partnerships in AI and robotics.

Bin Yangمشاهده پروفایل

استاد

• Computer Science
• Data Management
• Machine Learning
• +۹ مورد دیگر

Bin Yang is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, artificial intelligence, and machine learning, with emphasis on spatiotemporal data analysis, representation learning, and time series forecasting. He leads projects like aSTEP (spatio-temporal data analytics) and Light-AI for Cognitive Power Electronics. Key contributions include advancements in trajectory data processing, anomaly detection, and lightweight neural architectures. Research interests span machine learning, data mining, intelligent transport systems, and material science applications. Notable awards include the IJCAI 2019 Distinguished PC Member and Sapere Aude Research Leader (2018). He has supervised six PhD students and contributed to over 119 publications. His work is supported by grants from Villum Foundation and EU initiatives. Yang is involved in interdisciplinary collaborations, including data-driven decision-making frameworks and environmental monitoring systems. Labs and teams include the Daisy Center for Data-intensive Systems and AI for the People initiatives. Projects emphasize real-world applications in smart cities, traffic forecasting, and sustainable technologies. His research bridges theoretical foundations with practical implementations in domains like oceanography and crowdsourcing systems.

Tianyi Liمشاهده پروفایل

استادیار

• Digital Twin
• Graph Neural Networks
• Energy Engineering
• +۸ مورد دیگر

Tianyi Li is a Tenure Track Assistant Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design and the Daisy - Center for Data-intensive Systems. His research focuses on data engineering, spatiotemporal systems, and digital twins, with applications in energy, transportation, and vehicular networks. Key areas include trajectory data management, graph neural networks, compression techniques, and federated learning. He has contributed to over 37 publications, including works on digital twin frameworks for autonomous driving and smart grids, trajectory prediction systems, and efficient time series compression. His research integrates machine learning, distributed systems, and cybersecurity to address challenges in data-intensive environments. Key Projects: Digital twin-enabled voltage control, federated vehicular networks, trajectory simplification frameworks. Labs: Involved with the Daisy Center, focusing on data-centric systems and scalable solutions. Technical Expertise: Blockchain systems, energy systems optimization, and real-time data processing.

Aamir Cheemaمشاهده پروفایل

دانشیار

• Urban Computing
• Sustainable Transportation Systems
• Smart Buildings
• +۵ مورد دیگر

Aamir Cheema is an Associate Professor and Director of Education at the Department of Software Systems & Cybersecurity at Monash University. He co-directs the Urban Computing Lab and has held significant roles such as proceedings chair for ICDE 2019 and DASFAA 2015. His research focuses on data management, cloud/edge computing, and sustainable urban systems, with projects addressing electric vehicle integration, spatiotemporal data analytics for road safety, and smart building technologies. Education: PhD in Computer Science & Engineering (UNSW, 2011), Master of Engineering (UNSW, 2007), Bachelor of Science in Electrical Engineering (University of Engineering and Technology Lahore, 2005). Research interests include urban computing applications for sustainable transportation, smart buildings, and digital twins. Notable projects involve using big spatiotemporal data to reduce emissions, bidirectional EV charging systems, and indoor data management for emergency evacuations. Recipient of major awards including the ARC Future Fellowship (2018) and Victorian Young Tall Poppy Science Award (2019). Active in academic service roles such as Associate Editor of IEEE TKDE and conference organization (ADC 2015-2016 co-chair). Current projects include Electric vehicles for residential energy optimization (2025-2027) GenAI-driven ICT education reform (2025) Blockchain for peer-to-peer energy trading (2024-2025) Spatiotemporal data-driven road safety improvements (2023-2026)

• Artificial Intelligence
• Machine Learning
• Autonomous Systems
• +۴ مورد دیگر

Loo Junn Yong is a Lecturer at the Malaysia School of Information Technology, Monash University, specializing in artificial intelligence, machine learning, and autonomous systems. His research focuses on applying advanced algorithms to real-world challenges in robotics, brain network analysis, and traffic safety. He leads projects such as 'Efficient and Interpretable Multi-view Deformable Transformer with 3D Position Embeddings and Intelligent Queries for Autonomous Driving' and collaborates on ASEAN NCAP assessment protocols for vehicle safety. His work spans generative models, trajectory prediction, and ethical decision-making in autonomous systems. Notable contributions include frameworks for brain disorder identification using dynamic graph representation and energy-efficient trajectory planning for electric vehicles. He actively publishes in top conferences like IJCAI and IEEE SMC. His research aligns with UN SDGs through innovations in sustainable transport and health diagnostics.

• Computation Theory and Mathematics
• Distributed Computing
• Information Systems
• +۷ مورد دیگر

David Taniar is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University's Faculty of Information Technology. He holds a PhD in Computer Science from Victoria University (1997), and prior degrees from Swinburne University of Technology. His research focuses on computation theory, distributed systems, health informatics, and AI applications in healthcare. Taniar has led/co-investigated over 15 projects including digital health interventions, eco-friendly navigation systems, and healthcare data analytics. Current projects include a 2023-2026 initiative on pathology requesting practices in Emergency Departments. He has published 508+ works, spanning medical image analysis, spatial data processing, and cybersecurity. Awards include the 2021 Good Design Award for a hand hygiene management system. Education: Doctor of Philosophy, Computer Science, Victoria University (1997) Master of Applied Science, Computer Science, Swinburne University (1992) Graduate Diploma, Computer Science, Swinburne University (1990) Research Focus: Combines AI/ML with domain-specific challenges in healthcare, transportation, and distributed systems. Recent work addresses disease detection via medical imaging (e.g., glaucoma, diabetic ulcers), spatiotemporal data optimization for eco-routing, and ED data utilization. Key Projects: A digital health intervention to improve pathology requests in Emergency Departments (2023-2026) Eco-friendly navigation using big spatiotemporal data (2023) Actionable clinical data systems for quality care accreditation (2021-2025) Awards & Activities: Recipient of 2021 Good Design Award for healthcare innovation Editor-in-Chief of International Journal of Business Data Communications and Networking (2007) Chief Examiner for multiple IT courses at Monash

Holger Meyerمشاهده پروفایل

استاد

• Databases
• Digital Libraries
• Information Retrieval
• +۵ مورد دیگر

Dr. Holger Meyer is a faculty member at the University of Rostock's Faculty of Computer Science and Electrical Engineering, specifically in the Institute of Computer Science. He is based at the Konrad Zuse House on Albert-Einstein-Straße 22, Room 332, and can be reached at phone number 0381-498 7597 or via email at hme@informatik.uni-rostock.de. Dr. Meyer teaches a variety of database-related courses including Database Application Programming (Bachelor), Databases for Users (Bachelor), Databases III (Master), and Digital Libraries and Multimedia Information Retrieval (Master). He also supervises projects under KSWS/Project/NEidI. His research focuses on database systems, digital libraries, information retrieval, data science, and XML database technologies. Dr. Meyer has made significant contributions to the development of digital archive systems, particularly through the Hydra.PowerGraph System, which enables building digital archives with directed and typed hypergraphs. His work also extends to applying crowdsourcing techniques in cultural heritage projects like the Mecklenburg Field Name Archive and developing SQL-based approaches for machine learning and signal processing tasks. Dr. Meyer's recent publications (2017-2019) demonstrate a strong emphasis on practical applications of database technologies across diverse domains including environmental monitoring (particulate matter analysis), automotive engineering, digital humanities, and historical geography. His work bridges theoretical database concepts with real-world applications, particularly through SQL-based implementations of machine learning operations and signal processing techniques. Throughout his career, Dr. Meyer has collaborated extensively with colleagues at the University of Rostock, particularly with Professor Andreas Heuer, Meike Klettke, and other researchers in the database and information systems group. These collaborations have resulted in numerous publications in prestigious venues including Datenbank-Spektrum, LNI proceedings, and international conferences.