جست‌وجوی استاد و پژوهشگر

Abdullah Mueenمشاهده پروفایل

استاد

• Temporal Data Mining
• Blockchain Data Mining
• Social Media Mining
• +۵ مورد دیگر

Abdullah Mueen is a Professor and Associate Chair in the Department of Computer Science at the University of New Mexico (UNM), where he has been since 2013. Previously, he worked as a Scientist in the Cloud and Information Sciences Lab at Microsoft Corporation. Research Interests : His work focuses on Temporal Data Mining , with emphasis on efficiency , interactivity , and interpretability . Key areas include Blockchain Data Mining (e.g., BitLink for Bitcoin cluster analysis), Seismic Data Mining (e.g., PAW for aftershock detection), and Social Media Mining (e.g., DeBot for Twitter bot detection). Article Trends : His recent publications span four domains: Seismology : Algorithms for earthquake data analysis (e.g., focal depth inference, aftershock classification). Blockchain : Temporal linkage of Bitcoin addresses (BitLink) and cryptocurrency fraud detection. Traffic Safety : Multi-LiDAR data fusion for real-time road safety monitoring. Time Series Methods : Innovations like MASS similarity search and DAMP anomaly detection for massive datasets. Scientific Awards : ACM SIGKDD Test-of-Time Award (2022) UNM Provost Research Leader Award UNM School of Engineering Junior Faculty Research Excellence Award KDD 2012 Doctoral Dissertation Contest Runner-Up KDD 2012 Best Paper Award Advising and Grants : He has mentored 11 PhD students now employed at institutions like Microsoft, Meta, and Lawrence Livermore National Lab. His research is funded by NSF , NIH , DARPA , AFRL , NEC , Exxon , Microsoft , and LANL .

CHAN Chee Yongمشاهده پروفایل

دانشیار

• Database Systems
• Query Processing
• Query Optimization
• +۳ مورد دیگر

CHAN Chee Yong is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS) . He earned his Ph.D. in Computer Science from the University of Wisconsin-Madison and holds B.Sc. and M.Sc. degrees in Computer Science from NUS. Education : Ph.D., Computer Science, University of Wisconsin-Madison M.Sc., Computer Science, NUS B.Sc., Computer Science (1st Class Honours), NUS His research focuses on database systems , emphasizing query processing and optimization , transaction management , and database usability . He has contributed extensively to XML data dissemination, skyline computation, and multicore database performance optimization, with publications in venues like ACM SIGMOD, VLDB, and IEEE ICDE. Recent publications show increasing emphasis on join optimization , transaction healing , and spatial-keyword queries , reflecting trends in multicore systems, complex query processing, and XML data management. His work combines theoretical rigor with practical applications in distributed databases and data engineering. Notable professional roles include Associate Editor for the VLDB Journal , ACM SIGMOD Record , and IEEE Transactions on Knowledge and Data Engineering . He has served on program committees for major conferences like SIGMOD, ICDE, and VLDB across 2003-2026. Dr. Chan has supervised 9 PhD students and 10 M.Sc./M.Comp. students , including WANG TaiNing (2021), LI Meiying (2020), and TRAN Quoc Trung (2011). His advisees have been placed in institutions like the Institute for Infocomm Research and Huawei Shannon Lab.

• Journalism values and practice
• Digital media studies
• News design
• +۶ مورد دیگر

Dr. John O'Sullivan is a Professor in the School of Communications at Dublin City University (DCU), part of the Faculty of Humanities and Social Sciences. He has been teaching since 1996, specializing in journalism, media theory, and technology's societal impact. His research focuses on journalism practice, digital media, news audiences, and media ethics, particularly through projects like the EU-funded ReMED initiative examining news media in Ireland. He has held leadership roles, including Chair of the MA in Journalism program and Vice-President of the Journalism Studies Section at ECREA. His academic background includes extensive experience in newspaper editing and digital media initiatives. Research interests span media formats/platforms, participatory journalism, and the interplay between technology and democracy. He has supervised PhD students exploring digital news strategies, Southeast Asian journalism norms, and social media's democratic role. O'Sullivan's work bridges traditional and digital media, emphasizing post-digital journalism's challenges and opportunities. Publications span peer-reviewed journals like Journalism and Digital Journalism , addressing topics such as news consumption, algorithmic impact, and participatory methodologies. He has contributed to international conferences and research networks, including COST Actions on audience transformations and Eastern European media studies. His teaching emphasizes professional journalistic values in evolving media landscapes.

Albert H. Titusمشاهده پروفایل

استاد

• Wearable Sensors
• Biosensors
• Bioinstrumentation
• +۵ مورد دیگر

Albert H. Titus is a Professor in the Department of Biomedical Engineering and an Adjunct Professor in the Department of Electrical Engineering at the University at Buffalo, State University of New York. He serves as Associate Vice President for Regulatory Support in the Office of the Vice President for Research and Economic Development. His research focuses on analog VLSI design for neuromorphic visual processing, biosensors, wearable devices, optoelectronic systems, and neural networks. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology (1997) MS in Electrical Engineering, University at Buffalo (1991) BS in Electrical Engineering, University at Buffalo (1989) Research Interests: His work spans wearable and implantable sensors, bioinstrumentation, neural network-based visual processing, analog VLSI implementations, optoelectronics, and electronic packaging. He pioneered CMOS-based neuromorphic systems and developed patented technologies for glare sensing and RF power calorimetry. Publication Trends: His recent articles emphasize CMOS-integrated sensors, machine learning for bioimpedance analysis, implantable medical devices, and xerogel-based optical biosensors. These works bridge biomedical engineering and microelectronics. Scientific Recognition: He is a Fellow of the National Academy of Inventors and has received the SUNY Chancellor’s Award for Excellence in Service (2017), NSF CAREER award, and Western New York Inventor of the Year (2010). His inventions include a patented low-power glare sensor (U.S. Patent 7,586,079) featured in Popular Science’s 2011 Top Ten Inventions. Academic Leadership: As a faculty member, he has supervised nearly 20 PhD and over 40 MS students, while teaching courses in circuits, IC design, sensors, and signal processing across electrical and biomedical engineering disciplines.

Andrea Fumagalliمشاهده پروفایل

استاد

• Optical Networking
• Photonic Slot Routing
• Wavelength Routing and Protection
• +۷ مورد دیگر

Andrea Fumagalli is a Professor in the Department of Electrical Engineering at the Erik Jonsson School of Engineering and Computer Science , The University of Texas at Dallas. He earned his Ph.D. (1992) and Laurea (1987) in Electrical Engineering from Politecnico di Torino, Italy. Research Interests: All-Optical Network Architectures, Photonic Slot Routing, Wavelength Routing and Protection, Sensor Networks, Cooperative Wireless Networks, Network Optimization, Next Generation Internet (NGI), and Multi-hop Optical Networks. Education: Ph.D., Electrical Engineering, Politecnico di Torino (1992) Laurea, Electrical Engineering, Politecnico di Torino (1987) Key Research Trends: His recent publications focus on 5G networking, optical network automation, elastic optical networks, network reliability, and cross-layer optimization. He explores FPGA acceleration in 5G Low-PHY functions, live migration of containerized network components, and spectral fragmentation mitigation in EONs. Scientific Awards: Best Teaching Award, Electrical Engineering, UTD (2002) Best Thesis Award for Ph.D. Advisee Isabella Cerutti (2002) IEEE ComSoc Distinguished Lecturer Tour (2000) Best Paper Award (1999): 'An Optimal Design Algorithm for Photonic Slot Routing Networks Migrating to Optical Packet Switching' Advising and Grants: He advised Ph.D. student Isabella Cerutti. In 2001, he secured a $300,000 grant from FUNDACAO CPqD for optical network reliability research. He leads the Optical Networking Advanced Research (OpNeAR) Lab at UTD, collaborating on international projects like the Italian government-funded grid computing initiative (2002) and the OMEGA Test-bed for differentiated reliability. Laboratories and Teams: He directs the OpNeAR Lab , which develops tools for optical network emulation and reliability testing. His projects involve partnerships with institutions in Brazil (Unicamp), Sweden (KTH), Italy (Politecnico di Torino, Scuola Superiore Sant'Anna), and CNR/CNIT.

Anupam Joshiمشاهده پروفایل

استاد

• Networked Computing
• Intelligent Systems
• Cybersecurity
• +۶ مورد دیگر

Anupam Joshi is the Acting Dean of the College of Information Technology and Engineering and Oros Family Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He also directs UMBC’s Center for Cybersecurity and leads the National Cybersecurity FFRDC for the University System of Maryland. His research focuses on networked computing, AI-driven cybersecurity, privacy-preserving technologies, and policy-driven security frameworks. He holds a Ph.D. in Computer Science from Purdue University (1993), an M.S. (1991), and a B.Tech in Electrical Engineering from the Indian Institute of Technology, Delhi (1989). Dr. Joshi’s work spans over 400 publications with 32,650+ citations (h-index 92) and nine patents. His grants include funding from NSF, DARPA, NASA, NIST, and industry partners like IBM and Northrop Grumman. Key contributions include developing CAPD frameworks for IoT security, FABULA for automated threat intelligence, and KiNETGAN for intrusion detection through synthetic data. He is an IEEE Fellow and pioneer in applying AI to secure critical infrastructure, smart grids, and healthcare systems. His research trends emphasize AI-empowered cybersecurity, privacy compliance in data sharing (e.g., agriculture, healthcare), and mitigating attacks on smart systems. Notable projects include combating fake cybersecurity reports using provenance analysis, securing EV charging infrastructure, and enhancing smart farming resilience through policy-driven access control. Awards: IEEE Fellow Grants: Over $30M from NSF, DoD, NASA, and industry collaborations Labs/Teams: Director of UMBC Center for Cybersecurity, Cybersecurity Knowledge Graph initiatives Future work includes advancing neurosymbolic AI for cybersecurity, semantic data extraction from scientific literature, and AI ethics in healthcare applications.

• Decision Intelligence
• Strategic Management
• International Management
• +۵ مورد دیگر

Dr. Roger Moser is a Senior Lecturer at Macquarie University's Department of Management Innovation, Strategy and Entrepreneurship Research Centre. He also holds adjunct roles at the Indian Institute of Management Udaipur (since 2012) and the University of St. Gallen (since 2012). His research focuses on Decision Intelligence, Strategic Management, and International Management, particularly exploring how executives leverage data (small/big) to enhance decision-making frameworks. He has over 99 publications since 2005, with notable works in journals like Journal of Service Research and Journal of Business Research . Dr. Moser holds a Dr. rer. pol. from EBS European Business School (2006) and a lic. oec. HSG (MSc) from the University of St. Gallen (2003). His research interests include Decision Model Innovation, Social Capital in emerging markets, and strategic alignment in supply chains. He has conducted impactful studies on access-based services in India, supplier integration in China's automotive sector, and humanitarian logistics using satellite data. His recent work emphasizes technology integration in B2B value creation, decision frameworks for uncertainty reduction (e.g., in agriculture and disaster management), and AI-driven expert systems. He has presented at global forums on topics like digital transformation and decision intelligence applications, contributing to both academic and practitioner discourse. External Roles: Adjunct Professor of Business Policy, Indian Institute of Management Udaipur Titularprofessor / Permanent Lecturer, University of St. Gallen Notable impacts include a project providing clean drinking water in India through access-based solutions, recognized for quality-of-life and societal contributions. His research has been cited over 1,666 times with an h-index of 21.

Laura Marusterمشاهده پروفایل

استادیار

• Process Mining
• Process Modelling
• Healthcare Networks
• +۵ مورد دیگر

Laura Maruster is an Assistant Professor at the Faculty of Economics and Business, University of Groningen, Netherlands. She holds an MSc from the West University of Timisoara, Romania, and a PhD in Technology Management from Eindhoven University of Technology, Netherlands. Her research focuses on process mining, process modelling, and healthcare networks, with applications in operations research and data science. She collaborates extensively with industry and public sector partners on projects like the Casimir initiative (RuG and Gasunie). Teaching areas include business processes, data mining, and research methodology. Recent work includes studies on healthcare logistics optimization and process mining beyond traditional workflows. Her publications appear in leading journals such as BMJ Open , IEEE Transactions on Engineering Management , and Computers in Industry . Research interests span healthcare networks, emergency medical services analytics, and enterprise process optimization. Notable recent contributions include analyzing interhospital patient transfers and redesigning engineering design processes using data-driven methods.

Ningchuan Xiaoمشاهده پروفایل

استاد

• Machine Learning and Spatial Data
• Census Data
• Spatial Optimization
• +۳ مورد دیگر

Ningchuan Xiao is a Professor of Geography at The Ohio State University's Department of Geography. His work bridges Geographic Information Science (GIScience) with computational methods, emphasizing spatial optimization, cartography, and machine learning integration. Education: Ph.D. in Geography from The University of Iowa (2003). Courses taught include GIS fundamentals, cartography, and Python-based spatial analysis. Research Interests: Spatial Optimization: Developing algorithms for land acquisition, redistricting, and resource allocation. Machine Learning & Cartography: Exploring AI-driven map interpretation and ethical visualization of complex data. Census Data: Innovating privacy-preserving techniques while maintaining data utility, including temporal/spatial modeling. Open Source Tools: Authored GIS Algorithms (2016) and maintains GitHub repository 'gisalgs' for accessible code. Publications: Recent work (2023-2025) highlights advancements in synthetic microdata generation, privacy-utility tradeoffs in census aggregation, and AI-driven cartographic recognition. His 2022 studies include traffic camera analytics and choropleth map QA systems. Awards: Not explicitly listed in the provided texts. Advising & Grants: Collaborated with researchers like Y. Lin, J. Li, and S. Bao. Projects include the Sustainable Columbus Observatory (SCO) for urban sustainability metrics. Research is supported through academic partnerships and computational initiatives.

Jacco van Ossenbruggenمشاهده پروفایل

استاد

• Cultural Heritage
• Digital Libraries
• Ontology Engineering
• +۷ مورد دیگر

Prof. Jacco van Ossenbruggen is a Full Professor in Intelligent Information Systems at Vrije Universiteit Amsterdam (VU), affiliated with the Network Institute. He serves on the Management Board of ODISSEI, a national research infrastructure for social sciences and economics. His academic background includes a PhD in Computer Science (2001) from VU’s Faculty of Science, focusing on hypermedia processing. Research Interests: His work centers on cultural AI, FAIR data principles, ontology engineering, and semantic web technologies. Key areas include inclusive cultural heritage metadata, bias mitigation in AI systems, and knowledge discovery via linked data. Recent projects involve leveraging large language models (LLMs) for metadata enrichment and ontology construction. Key Contributions: He leads initiatives like the Cultural AI Lab, exploring AI applications for cultural heritage. His research bridges technical innovations (e.g., semantic integration of restricted-access data) with societal impacts (e.g., ethical AI frameworks for public-sector applications). Developed frameworks for evaluating entity alignment in knowledge graphs Pioneered FAIR-aligned data management plans for scientific communities Designed tools like Alter Heritage for collaborative metadata curation Grants & Projects: Principal Investigator of the ODISSEI Portal project (2020–2024), advancing open data infrastructures. Active in funding initiatives promoting reproducible research and ethical data practices. Labs/Teams: Cultural AI Lab at VU, focusing on AI-driven solutions for cultural heritage preservation and accessibility.

• IoT Systems
• Data Quality
• Fog Analytics
• +۷ مورد دیگر

John Byabazaire is a Research Fellow at the School of Computer Science, University College Dublin (UCD). He holds a PhD in Computer Science from UCD (2024), following a BSc (Gulu University, 2013) and MSc (Waterford Institute of Technology, 2018). His research focuses on IoT systems for data collection, remote sensing, AI-driven end-to-end system management, and fog analytics. He has held academic roles including Assistant Lecturer at Gulu University (2018–2019) and teaching roles at UCD since 2019, including Occasional Lecturer and Senior Teaching Assistant. His research spans smart agriculture, data quality in IoT, and education technology. Notable contributions include frameworks for yield mapping in precision agriculture, trust-based data validation in IoT, and machine learning approaches for livestock health monitoring. He has secured grants like the National ICT Initiatives Support Program (Uganda Government, 2019–2020). Teaching includes courses on cloud computing, web development, and distributed systems. His articles emphasize IoT data quality, agricultural analytics, and educational technology innovation. He actively promotes technology adoption in African education and agriculture sectors through collaborative projects.

Dan Sheldonمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Machine Learning
• +۵ مورد دیگر

Dan Sheldon is a Professor in the Department of Computer Science at the University of Massachusetts Amherst, holding a Five College joint faculty position with Mount Holyoke College. His research focuses on developing algorithms to address environmental challenges using large datasets, emphasizing computational sustainability. Key areas include spatial optimization for endangered species conservation, continent-scale bird migration modeling, and interpreting weather radar data for ecological insights. Methodologically, his work leverages probabilistic inference, network modeling, and machine learning. Sheldon earned a PhD in Computer Science from Cornell University and an AB in Mathematics from Dartmouth College. His postdoctoral training at Oregon State University was supported by an NSF Bioinformatics Fellowship. He co-leads the BirdCast project, an NSF-funded initiative applying novel machine learning to avian migration studies. His research affiliations include the Center for Data Science and the Computational Social Science Institute. Research interests span computational biology, machine learning, and data privacy. Notable contributions include algorithms for ecological decision-making, differentially private synthetic data techniques, and Gaussian process applications in environmental forecasting. Awards include an NSF Fellowship in Bioinformatics. Current projects integrate radar data analysis, biodiversity tracking, and privacy-preserving statistical methods. Grants include the BirdCast NSF grant and collaborations in computational sustainability. His work bridges theoretical computer science with applied ecological challenges, emphasizing interdisciplinary approaches to global-scale environmental problems.

Oya Beyanمشاهده پروفایل

استاد

• Biomedical Informatics
• Semantic Web Technologies
• Clinical Decision Support
• +۵ مورد دیگر

Prof. Dr. Oya Beyan is a Professor at the University of Cologne's Institute for Biomedical Informatics and a Core Scientist at the Center for Data and Simulation Science. Her research focuses on enabling FAIR (Findable, Accessible, Interoperable, Reusable) data management, distributed analytics on sensitive medical data, and data-driven innovations in healthcare. She leads projects like the PADME platform for federated machine learning and privacy-preserving analytics. Key areas include biomedical informatics, semantic web technologies, clinical decision support systems, and ethical challenges in data science. Research Interests: FAIR Data Principles & Infrastructure Privacy-Preserving Distributed Learning Explainable AI in Healthcare Semantic Interoperability Medical Data Integration Ethical & Social Implications of Data Use Notable Contributions: Development of the Personal Health Train framework for decentralized medical data analysis Leadership in EU-funded initiatives like NFDI4Health and Medical Informatics Collaborations Pioneering work on federated learning applications in oncology and rare disease research Lab & Affiliations: Prof. Beyan's work is anchored in the Institute for Biomedical Informatics and the Center for Data and Simulation Science, fostering interdisciplinary collaboration between computational science and medical research.

Hui Zhangمشاهده پروفایل

استاد

• Computer Science
• Networking
• Data-Driven Systems
• +۳ مورد دیگر

Hui Zhang is a Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on data-driven networking systems, video streaming optimization, and network control frameworks. He has contributed to innovations in adaptive resource allocation, real-time analytics, and sustainable strategies for resource utilization. Key research themes include time-state analytics, network anomaly detection, and integrating machine learning for enhanced performance. His work addresses challenges in content delivery networks (CDNs), peer-to-peer systems, and environmental applications like waste management. Recent publications (2021–2024) highlight advancements in neural network-based prediction, timeline frameworks, and sustainable material science innovations. No scientific awards are mentioned in the provided text. His research emphasizes practical solutions for improving video quality of experience (QoE), network efficiency, and cross-disciplinary applications.

• Transport Planning
• Urban Sustainability
• Data Analysis
• +۵ مورد دیگر

Roles and Affiliations: Doina Olaru is a Professor in the Department of Management and Organisations at the University of Western Australia (UWA) Business School. She is affiliated with the Planning and Transport Research Centre and holds a visiting position at the University of Burgos, Spain. Her work focuses on transport planning, urban sustainability, and data-driven decision making. Education: PhD in Transport Engineering (University Politehnica of Bucharest, 2000). Prior industry experience includes roles as a railway engineer and Postdoctoral Research Scientist at CSIRO. Research Interests: Urban transport systems, travel behavior modeling, accessibility analysis, environmental impacts of transport, and applications of artificial intelligence. She emphasizes sustainable solutions integrating land-use and transport policies. Grants and Collaborations: Principal investigator on 31 grants, including ARC Linkage Projects and industry partnerships with iMOVE CRC. Collaborates with institutions like the University of Oxford, University of Leeds, and University of Sydney. Awards: Multiple teaching awards (UWA Business School) and recognition for contributions to transport research, including the Dennis Moore Australian Computer Society Orator honor. Teaching: Courses include Data Analysis and Decision Making, and Quantitative Data Analysis. Over 10 teaching excellence nominations. Current Projects: Focus areas include smart transport technologies, roundabout modeling via drone analytics, and hybrid work impacts on transport demand.