جست‌وجوی استاد و پژوهشگر

Weiyi Mengمشاهده پروفایل

استاد

• Metasearch Engines
• Web Database Integration
• Web Data Quality
• +۵ مورد دیگر

Weiyi Meng is a SUNY Distinguished Service Professor and Director of the School of Computing at Binghamton University, part of the State University of New York (SUNY). He has been a faculty member since 1992, following his Ph.D. in Computer Science from the University of Illinois at Chicago. His research focuses on metasearch engines, web database integration, sentiment analysis, and information retrieval. He leads the Database Laboratory and has held roles such as Interim Associate Dean for Research and Graduate Studies in the Watson College of Engineering and Applied Science. Education: Ph.D., Computer Science, 1992, University of Illinois at Chicago M.S., Computer Science, 1988, University of Illinois at Chicago B.S., Mathematics, 1982, Sichuan University, China Research Projects: Social Media Data Analytics Efficient and Effective Metasearch Integrated Access to Web Databases Truthfulness Analysis of Web Information Sentiment Analysis and Topic Sentiment Change Analysis Awards: Recognized as a Top Computer Scientist in the U.S. by Research.com Recipient of multiple NSF grants (e.g., IIS-0208574) Students: Supervised 11 PhD and 71 Master's students, including current advisees Guangyu Liu and Ulugbek Ergashev. His contributions include three books on database systems and metasearch, over 150 peer-reviewed publications, and leadership in conference organizing and editorial roles. Current projects emphasize scalable metasearch engines, social media analytics, and entity resolution in dynamic data streams.

Michael Haydenمشاهده پروفایل

استادیار

• Astrophysics
• Cosmology
• Galactic Archaeology
• +۳ مورد دیگر

Michael Hayden is an Assistant Professor in the Homer L. Dodge Department of Physics and Astronomy at the University of Oklahoma. His research focuses on Galactic Archaeology, studying the formation and evolution of the Milky Way through stellar populations, chemical evolution modeling, and large spectroscopic surveys like GALAH and 4MOST. He has led initiatives such as the GECKOS survey to analyze edge-on galaxies, advancing our understanding of galactic structure and dynamics. Education: B.S. in Physics from the University of Washington (2010), Ph.D. in Astrophysics from New Mexico State University (2015). Research Interests: Hayden investigates chemical enrichment patterns, radial migration of stars, and Milky Way disk evolution. He develops models to explain present-day Galactic structure using chemical clocks and large datasets. His work compares the Milky Way to other galaxies, particularly through IFU spectroscopy of edge-on systems to identify kinematic substructures and disk stability mechanisms. Awards: No explicit prizes or fellowships are listed in the provided texts, though his contributions to major surveys like GALAH and GECKOS reflect significant academic impact. Grants & Advising: As PI for GALAH and project scientist for 4MOST, Hayden coordinates large collaborative efforts. His research leverages grants supporting spectroscopic surveys and computational modeling. He advises projects on chemical tagging, stellar populations, and extragalactic comparisons. Labs/Teams: Central to his work are the GALAH survey (studying 800,000 stars), 4MOST (next-gen multi-object spectroscopy), and the GECKOS survey (edge-on galaxy analysis). These projects involve international collaborations to map Galactic and extragalactic structures.

• Security
• Privacy
• Embedded Systems
• +۴ مورد دیگر

Vincent Naessens is a Professor at the Department of Computer Science within KU Leuven's Faculty of Engineering Technology, leading the Subdivision Mobility and Security at the Distributed and Secure Software (DistriNet) research group across Ghent and Aalst campuses. His work bridges academic research and industrial applications in critical security domains. Research Focus: Dr. Naessens specializes in secure mobile platforms , advanced authentication systems , privacy-enhancing technologies , and industrial control system security . His research addresses real-world vulnerabilities in IoT ecosystems and develops novel anonymization techniques that balance data utility with privacy protection. Current work emphasizes self-healing embedded systems and secure smart building infrastructures. Publication Trends: Recent publications (2024-2025) reveal three dominant threads: (1) Advanced dataset anonymization methods addressing temporal data and k-anonymity limitations; (2) Deep security analysis of commercial IoT products exposing critical vulnerabilities; (3) Privacy-preserving collaborative data sharing architectures. His work consistently targets practical implementations with measurable privacy-utility tradeoffs. Research Leadership: As principal investigator for major projects including Towards Self-Healing Embedded Systems (2025-2029) and BUGATTI: Embedded Security Testing (2025-2028), he directs teams exploring exploit prevention and adaptive patching. Key funding sources include FWO and EU programs supporting his work on secure SCADA systems and privacy middleware. Research Environment: Leading DistriNet's Mobility and Security subdivision, Naessens oversees a dynamic team publishing at top venues like WOOT and ARES. The group maintains strong industry ties through projects like TRUSTI (IoT security updates) and SolidLab Flanders, with active participation in the Computer Science Department Council and Faculty Advisory Committee.

• Speech Signal Processing
• Machine Learning
• Noise Reduction
• +۳ مورد دیگر

Ziye Yang is a researcher active in Speech Signal Processing and Machine Learning applications. His work focuses on advanced techniques like weighted prediction error , deep learning models , and noise reduction in audio systems. He has collaborated extensively with Jie Chen and Cédric Richard on topics including speech dereverberation and nonlinear residual echo suppression . His research spans multiple subfields including distributed speech processing , attention-based architectures , and deconvolution regularization . Recent publications (2024-2025) emphasize hybrid methods combining traditional signal processing with modern neural networks, particularly for reverberation modeling and echo suppression. Key trends in his work include Integration of data-driven priors in signal enhancement Development of plug-and-play frameworks for audio processing Application of attention mechanisms in dual-stream networks

Volker Marklمشاهده پروفایل

استاد

• Scalable Data Science
• Graph Mining
• Text Mining
• +۵ مورد دیگر

Prof. Dr. Volker Markl is a leading academic in scalable data science and systems research. He serves as Professor and Head of the Database Systems and Information Management (DIMA) group at Technische Universität Berlin, Chief Scientist at the German Research Center for Artificial Intelligence (DFKI), and Director of the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His work bridges hardware architectures, dataflow systems, and machine learning. Professor, Technische Universität Berlin (2008–present) Chief Scientist & Research Area Lead, DFKI (2018–present) Director, BIFOLD (2020–present) Co-Director, Berlin Center for Machine Learning (2018–2020) Research focuses on scalable data processing using novel hardware architectures, declarative data analysis, and systems for heterogeneous data. Key areas include graph mining, text mining, and GPU-accelerated analytics. His recent publications address dataflow optimization , GPU-based CSV loading, and IoT data management. Awards highlight his contributions to VLDB, SIGMOD, and German digital innovation. ICDE 2021 Best Paper ACM SIGMOD 2020 Best Paper Germany’s "Leading Digital Mind" (2014) Versatile leadership in VLDB Endowment

Ahmet Sayarمشاهده پروفایل

دانشیار

• Distributed Systems
• Big Data
• Data-Intensive Computing
• +۷ مورد دیگر

Assoc. Prof. Dr. Ahmet Sayar is an Associate Professor at the Department of Computer Engineering, Faculty of Engineering at Kocaeli University, Turkey. He holds an M.Sc. (2001) and Ph.D. (2009) in Computer Science from Syracuse and Indiana Universities, USA. His research focuses on Distributed Systems, Big Data, Data-Intensive Computing, Geographic Information Systems (GIS), and Exploratory Data Analysis (EDA). He has co-authored over 150 papers, 15 book chapters, and 1 book. Currently, he serves as Head of Student Affairs at Kocaeli University and as an associate editor for multiple journals. Education: M.Sc. in Computer Science, Syracuse University (2001) Ph.D. in Computer Science, Indiana University (2009) Research Interests: Parallel and Distributed Algorithms Big Data Analytics & Cloud Computing Machine Learning Applications (e.g., Image Processing, NLP) GIS and Remote Sensing Blockchain Technology for IoT and Logistics Recent Trends in Articles: His recent work emphasizes real-time data processing (e.g., video stream analytics), blockchain for supply chains, and AI-driven solutions for healthcare, transportation, and urban management. He explores ethical aspects of web scraping and low-code platforms for process automation. Advising & Grants: Advised 18 master’s/phd theses. Involved in TÜBİTAK projects and KOSGEB innovation programs. Member of editorial boards and national advisory committees. Labs/Teams: Leads university-level student affairs initiatives and collaborates on interdisciplinary projects involving distributed systems and big data frameworks.

Metafas Dimitriosمشاهده پروفایل

مدرس

• Real-Time Software development
• System-On-a-Chip design
• DSP architectures
• +۴ مورد دیگر

Metafas Dimitrios is a Lecturer in the Department of Electrical and Electronic Engineering at the University of West Attica, School of Engineering. His office is located in Building Z, Room ZB201. He holds a Doctoral Degree from the University of Patras (1993) and an Electrical Engineering Diploma (1987). His research focuses on Real-Time Software, System-On-a-Chip design, DSP architectures, and applications of AI/Machine Learning in games and education. Teaches courses such as Introduction to Programming, Algorithms and Data Structures, and UML-based system design methodologies. Active in interdisciplinary research, including educational technology and embedded systems. Recent work emphasizes AI-driven educational tools and reinforcement learning applications in gaming. Earlier contributions span wireless communication systems, VLSI design, and hardware-software co-development.

Odysseas Papapetrouمشاهده پروفایل

استادیار

• Streaming Data Processing
• Big Data Management
• Approximation Algorithms
• +۵ مورد دیگر

Odysseas Papapetrou serves as Assistant Professor in the Information Systems group within the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His academic journey spans multiple prestigious institutions across Europe, establishing him as a significant contributor to the field of data management and stream processing. Academic Credentials: BSc in Computer Science from University of Cyprus MSc in Advanced Information Technologies from University of Cyprus MSc in Computer Science from University of Saarland and Max Planck Institute (2005) PhD in Computer Science from University of Hannover and L3S Research Center (2011) Dr. Papapetrou's research focuses on cutting-edge challenges in data-intensive systems, with particular emphasis on real-time processing of streaming data. His work develops approximation algorithms for distributed Big Data management, distributed data streams processing, and sketching/summarization techniques. He has made significant contributions to cloud computing aspects including cloud federations, cloud-based stream processing, and privacy-preserving query execution. His research bridges theoretical foundations with practical implementations, addressing the growing need for efficient data processing in high-velocity environments. Analysis of his recent publications reveals a consistent focus on multivariate data analysis, with increasing specialization in spatial data streams and advanced similarity search techniques. His work demonstrates a progression from foundational stream processing to increasingly complex multivariate scenarios, reflecting the evolving challenges in Big Data analytics. The publications show strong collaboration patterns with researchers across Europe, particularly in the Netherlands and Switzerland. Dr. Papapetrou actively contributes to the academic community through service roles including frequent participation as PC member and reviewer for top-tier data management conferences and journals such as TKDE and VLDB Journal. He serves as a grants evaluator for the Hong Kong Research Grants Council (RCG) and other funding schemes. His current educational activities at TU/e include teaching Capita Selecta Databases, Seminar Datamanagement, Data-intensive Systems and Applications, Big Data Management, and Datamodeling and Databases. He currently leads research within the Database Group at TU/e, focusing on developing novel techniques for stream processing and Big Data management. His work has received funding from the European Union's Horizon Europe research and innovation program STELAR under grant agreement No. 101070122, supporting his research on advanced data stream processing techniques.

• Agricultural Management
• Soil Science
• Environmental Management
• +۴ مورد دیگر

Alice Melland is a Senior Research Fellow at the University of Southern Queensland , specifically affiliated with the Centre for Agricultural Engineering. Her research focuses on agricultural sustainability , soil management , and environmental impact assessment in farming systems. Education : Bachelor of Agricultural Science, University of Melbourne (1997) PhD, University of Melbourne (2004) Her work examines nutrient transfer pathways , water quality in agricultural catchments, and soil rehabilitation following mining. Recent publications analyze phosphorus and nitrogen dynamics in runoff, strategic tillage for erosion control, and sustainable grazing practices on reclaimed lands. Key trends in her research include high-resolution environmental monitoring , catchment-scale modeling , and climate adaptation strategies for farming systems. Her collaborations span institutions in Australia, New Zealand, and Ireland , focusing on fertilizer efficiency , sediment transport , and policy evaluation for nutrient management.

Krešimir Pripužićمشاهده پروفایل

استاد

• Telecommunications
• Data Processing
• Machine Learning
• +۴ مورد دیگر

Prof. Dr. Krešimir Pripužić is a Full Professor at the Department of Telecommunications, School of Electrical Engineering and Computing, University of Zagreb. He serves as Vice Dean for Education and focuses on telecommunications, machine learning, and IoT systems . His work bridges distributed computing with geospatial data and mobile crowdsensing . Location: M1-06, University of Zagreb Email: kresimir.pripuzic@fer.unizg.hr His research spans stream processing , sensor management , and publish/subscribe middleware . Key trends include real-time anomaly detection , energy-efficient IoT , and Apache Spark-based distributed systems . Projects highlight collaboration with OpenIoT and CUPUS ecosystems.

Raja Nagisettyمشاهده پروفایل

استادیار

• Environmental Engineering
• Hydrology
• Water Quality Modeling
• +۴ مورد دیگر

Raja Nagisetty is an Assistant Professor in the Department of Environmental Engineering at Montana Tech's Lance College of Mines & Engineering. Holding a Ph.D. in Civil and Environmental Engineering from the University of Louisville (2009), he is a licensed Professional Engineer in Kentucky. Education: B.Tech (2000) and M.Eng (2002) from Indian institutions Professional Experience: 2014-present at Montana Tech, prior roles at University of Louisville and Indian institutions His research focuses on water quality modeling , stormwater management , and environmental health assessments , with recent work examining wildfire particulate matter impacts and mine waste remediation strategies. Current projects include modeling snowmelt runoff systems and evaluating geothermal seep interactions with river systems. Recent publications demonstrate expertise in dissolved oxygen dynamics , heavy metal characterization , and remote sensing applications . He serves on graduate councils and thesis committees, including chairs for Matt Strozewski and William George. Professional Affiliation: Montana American Water Resources Association Notable Award: Faculty Certificate of Appreciation from Sree Sastha Engineering & Technology (2004-2005) His work spans multiple environmental engineering domains, from drinking water infrastructure challenges to climate change adaptation strategies, with significant contributions to watershed management and pollution analysis.

Drago Žagarمشاهده پروفایل

استاد

• Communication Networks and Protocols
• Codes and Coding
• Quality of Service in Networks
• +۷ مورد دیگر

Drago Žagar is a Full Professor at the Faculty of Electrical Engineering, Computer Science and Information Technologies (FERIT), University of Osijek, where he also serves as Head of Department. He has been permanently appointed since 2015 and has a long-standing affiliation with the institution since 1990, with brief periods at other units within the same university. His educational background includes a B.Eng. (1990), M.Sc. (1995), and Ph.D. (2002), all from the Faculty of Electrical Engineering and Computing in Zagreb. His research spans key areas in modern networking and information systems, including communication networks and protocols , quality of service , cybersecurity , and IoT applications in agriculture and logistics . His recent work emphasizes energy-efficient monitoring, anomaly detection using machine learning, smart labels for supply chains, and adaptive video streaming. The 15 most recent publications reflect a strong trend in applying sensor networks and data analytics to real-world problems in agriculture, network security, and multimedia systems. The interdisciplinary nature of his work bridges computer networks, industrial informatics, and smart technologies. He has led significant projects such as Smart Label for Measuring and Monitoring Product Storage , IoT Ecosystems for Agriculture , and Boosting Telecommunications Engineering Education under Erasmus+. He also managed national and EU-funded initiatives focused on broadband access in rural areas and IPv6 security. He has mentored numerous students and authored key educational materials, including laboratory manuals in computer networks and electronics. His teaching includes courses on Computer and Mobile Networks and Computer Systems Security . He is actively engaged in research, with publications in journals such as Sensors , Electronics , Journal of Industrial Information Integration , and Telecommunications Systems . His work is indexed in Google Scholar, Scopus, Web of Science, and CRORIS.

Michal Slupczynskiمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction (HCI)
• DevOps, MLOps, LLMOps
• Workflow Automation
• +۱۵ مورد دیگر

Michal Slupczynski is a PhD student and research assistant at the Chair for Computer Science 5 (Informatik 5) within the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen University. He earned his Master’s degree in Software Systems Engineering from the same institution in 2020 and has been actively contributing to research and teaching since December 2020. His research interests include human-computer interaction (UI/UX, personal fabrication), DevOps/MLOps/LLMOps, workflow automation, decentralized P2P communities, augmented and virtual reality (especially collaborative environments), interactive storytelling, agile project management, digital transformation, gamification, and games. He applies these interests in the context of educational technology, psychomotor skill acquisition, and learning analytics. His recent publications reflect a strong focus on sensor-based motion analysis, AI-driven feedback systems, immersive learning environments, and explainable AI. He has contributed to advancements in sports motion analysis using the Kabsch algorithm, cloud-based feedback for psychomotor training, citation recommendation chatbots, and MLOps pipelines for educational data. Michal is actively involved in teaching, offering courses such as Datenbanken und Informationssysteme and seminars on Social Computing, Web Science, and High-Tech Entrepreneurship. He has supervised numerous thesis projects on topics ranging from LLMs in maintenance and legacy system modernization to blockchain escrow systems and gamification in education. He is engaged in key research projects including MILKI-PSY (Multimodal Immersive Learning with AI for Psychomotor Skills), TrainSpot (Train-the-Trainer HotSpot), Blockchain4DatenMarktplatz.NRW, and las2peer, highlighting his work in decentralized systems and technology-enhanced learning. He has also served as an editor for workshop proceedings and presented at international conferences such as EC-TEL and CollabTech.

• Epistemology
• Research Methodology
• Social Media Analytics
• +۵ مورد دیگر

Cláudia Freire is a Postdoctoral Researcher at the Centre for Research in Communication and Culture (CECC) within the Faculty of Human Sciences at the Catholic University of Portugal. Her work intersects epistemology, digital methodologies, and sustainable development goals, with a focus on social media analytics and civil diplomacy. Her academic credentials include: Ph.D. in Communication Sciences, University of São Paulo (2015) Master's in Communication Sciences, University of São Paulo (2009) Bachelor's in Arts and Humanities, University of Minas Gerais (1999) Bachelor's in Communication Sciences (Marketing), Catholic University of Minas Gerais (1997) Freire's research centers on epistemological foundations of digital communication, specializing in social media monitoring techniques, algorithmic analysis of television fiction audiences, and data visualization methodologies. She critically examines how platform algorithms shape online discourse while advancing civil diplomacy frameworks for digital inclusion. Her work integrates UN Sustainable Development Goals through communication science applications. Analysis of her 2014-2023 publications reveals consistent evolution from foundational epistemology studies toward applied streaming platform analytics. Key trends include methodological innovation in social media monitoring, algorithmic influence mapping in television fiction fandoms, and visual representation of audience data – demonstrating increasing technical sophistication while maintaining critical theoretical grounding. Freire actively contributes to academic governance through thesis examinations and journal peer review. Recent activities include: Examination of doctoral theses on algorithmic communication gatekeepers (2014) Peer review for MATRIZes and Conexión journals (2023) Collaboration with Observatório Ibero-Americano da Ficção Televisiva (OBITEL) Her current postdoctoral project investigates diachronic methodological shifts in television drama research through CECC's interdisciplinary framework, leveraging OBITEL's international network for comparative analysis of Ibero-American media studies.

H. Trautmannمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Evolutionary Optimization
• +۶ مورد دیگر

H. Trautmann is a Professor of Machine Learning and Optimization at the Paderborn University and holds a Guest Professor position in Data Science: Statistics and Optimization at the University of Twente . Affiliated with the European Research Center for Information Systems (ERCIS) , they lead the ERCIS Competence Center for Social Media Analytics and co-head the ERCIS Research Cluster on Data Science and AI . Research focuses on Data Science , Evolutionary Optimization , Algorithm Selection , and Social Media Analytics , integrating computational methods with social interaction analysis. Expertise spans Machine Learning , Multiobjective Optimization , Data Stream Mining , and Continuous Optimization .