جست‌وجوی استاد و پژوهشگر

• Database Systems
• Web Data Extraction
• Information Integration
• +۴ مورد دیگر

Jayant Madhavan is a researcher at Google specializing in database systems, web data extraction, and information integration. His work primarily focuses on extracting structured data from the web, schema matching, and developing techniques for managing and visualizing large datasets, particularly through projects like Google Fusion Tables and WebTables. Madhavan's research interests center around the challenges of working with web data. His work explores methods for extracting structured information from unstructured web content, particularly focusing on tables and lists. He has made significant contributions to the field of schema matching, developing techniques that enable integration of data from diverse sources. His research also extends to geospatial data processing and visualization, where he has developed algorithms for efficiently handling large geographical datasets for map visualization. His publication record shows a consistent focus on practical applications of database research to web-scale problems. The evolution of his work demonstrates a progression from foundational research on schema matching and data integration to applied work on Google products like Fusion Tables, which enable non-experts to work with structured data. His most recent work examines the ecosystem of structured data on the web and how to effectively extract and utilize this information. Madhavan has collaborated extensively with Alon Y. Halevy (43 co-authored papers) and other researchers at Google, forming a core group that has advanced the state of the art in web data management. His work bridges theoretical database research with practical applications, making significant contributions to both academic literature and real-world data management systems.

Amine Mhedhbiمشاهده پروفایل

استادیار

• Database Management
• Graph-Structured Databases
• Query Optimization
• +۴ مورد دیگر

Amine Mhedhbi is an Assistant Professor at Polytechnique Montréal in the Department of Computer Engineering and Software Engineering. He is affiliated with the Institute for Data Valorization (IVADO) and the Software Engineering for Machine Learning Applications (SEMLA) group. His research focuses on data management systems, particularly graph-structured databases, multimodal data engineering, and AI-driven query optimization. Ph.D. in Computer Science from University of Waterloo Former technical advisor to enterprise companies Prior applied research leadership at Distyl AI and internships at Microsoft Research His recent work explores integrating large language models (LLMs) into database systems, optimizing SQL generation, and advancing graph database architectures. Key projects include GraphflowDB and FlockMTL , addressing scalability and declarative semantic applications. Scientific awards include: NSERC Discovery Grant with Discovery Launch Supplement (2025) Cheriton School Distinguished Dissertation Award (2024) Microsoft Research Ph.D. Fellowship (2020) VLDB Best Paper Award (2018) He supervises graduate students in database systems and machine learning applications and serves on program committees for top-tier conferences like VLDB and SIGMOD.

• Semantic Web
• Data Integration
• Ontology Matching
• +۴ مورد دیگر

Dominique Ritze is a Research Fellow at the Data and Web Science Group of the University of Mannheim. Her research focuses on ontology alignment, semantic web technologies, linked open data integration, and knowledge organization systems. She collaborates with Prof. Dr. Christian Bizer and Prof. Dr. Kai Eckert on projects like InFoLiS II, aiming to advance data integration and semantic web applications. Education: MSc Computer Science (Diplom-Informatikerin) Research Interests: Dominique’s work bridges theoretical and applied aspects of semantic web technologies. Key areas include ontology evaluation frameworks, cross-domain data integration, and the development of tools for provenance tracking and data reuse. She has contributed to methodologies for aligning knowledge organization systems (KOS) and enhancing discovery systems with linked data. Publications Trends: Her articles from 2010-2015 emphasize ontology alignment (e.g., OAEI evaluations), semantic web applications, and data integration techniques. Notable contributions include the ICE-Map visualization for KOS evaluation and the Mannheim Search Join Engine for cross-website table integration. Awards: No scientific awards explicitly listed in the provided texts. Projects & Teams: Active in the Data and Web Science Group, leading projects on web table matching and semantic data integration. Collaborates with global research networks through initiatives like the Ontology Alignment Evaluation Initiative.

Axel Polleresمشاهده پروفایل

استاد

• Semantic Web
• Knowledge Engineering
• Data Management
• +۱۲ مورد دیگر

Axel Polleres is a full professor in the area of 'Data and Knowledge Engineering' at the Vienna University of Economics and Business (WU Wien) and a faculty member at the Complexity Science Hub (CSH) in Vienna, a position he has held since January 2017. His work bridges academic research and real-world applications in semantic technologies, knowledge graphs, and data governance. His research interests lie at the intersection of semantic web technologies, knowledge engineering, and data management. Focused on querying and reasoning over ontologies, logic programming, and rule-based systems, he explores how structured data can be effectively managed, validated, and integrated—especially through standards like SPARQL and SHACL. His work extends to applications in open data, legal data, biomedical informatics, and socio-environmental challenges such as climate and public health. The most recent publications highlight a growing trend toward practical and societal applications of knowledge graphs, including climate risk assessment, crisis response, healthcare accessibility, and open data governance. These works combine technical rigor in semantic modeling with impactful real-world use cases, particularly in European and Austrian contexts. Axel Polleres has been actively involved in international standardization, notably as co-chair of the W3C SPARQL Working Group, and serves on the editorial boards of the Semantic Web Journal and Journal of Web Semantics . He has contributed to numerous European and national research projects and has published over 100 articles in top-tier journals and conferences. He advises and collaborates with researchers across disciplines and institutions, contributing to interdisciplinary teams working on complex societal challenges. His work is supported by significant research grants, though specific funding sources are not detailed in the provided text. He leads and participates in research groups focused on semantic technologies, knowledge graphs, and data-driven policy.

Fatemeh Nargesianمشاهده پروفایل

استادیار

• Data Management
• Data Science
• Data Discovery and Integration
• +۴ مورد دیگر

Fatemeh Nargesian is the James P. Wilmot Distinguished Assistant Professor of Computer Science at the University of Rochester's Hajim School of Engineering & Applied Sciences. She holds a PhD from the University of Toronto. Her research focuses on data management, particularly in discovering and integrating heterogeneous data across large repositories. She applies probabilistic methods and machine learning to optimize data acquisition costs, enable scalable climate data analysis, and develop algorithms for data lake navigation. Education: PhD in Computer Science, University of Toronto Research Interests: Data discovery and integration, probabilistic data management, automated feature engineering, and climate data analysis. Her work addresses challenges in data lakes, including efficient search algorithms and semantic table unions. She also explores fairness in entity matching and cost-efficient dataset construction. Key Contributions: Includes the OpenForge, PLUTUS, and TSUPY frameworks for data integration, as well as award-winning systems like Interactive Navigation of Open Data Linkages (Best Demo Award, 2017). Her recent work emphasizes climate network analysis and ethical data practices. Lab/Teams: Her research involves developing tools like TSUBASA for real-time climate data analysis and Pylon for semantic table union search in data lakes. She collaborates on projects such as Dataset Evolver, an interactive feature engineering notebook.

Sebastián Ferradaمشاهده پروفایل

استادیار

• Knowledge Graphs
• Graph Databases
• Multimedia Databases
• +۳ مورد دیگر

Sebastián Ferrada is an Assistant Professor at the Data & Artificial Intelligence Initiative of Universidad de Chile. He also serves as Young Researcher at the Institute for Foundational Research on Data (IMFD) and Collaborating Researcher at the National Center for Artificial Intelligence Research (CENIA). His research focuses on Knowledge Graphs, with special emphasis on extraction, management, and applications for querying, browsing, and AI systems. His academic background includes: PhD in Computer Science (2021), Universidad de Chile MSc in Computer Science (2017), Universidad de Chile BEng in Computer Science (2017), Universidad de Chile Sebastián's research explores several key areas: Multimedia Databases with applications to Wikimedia Commons images Graph Databases and Knowledge Graphs construction Federated Data Management across heterogeneous RDF sources SPARQL query extensions for similarity-based operations Graph data management and compression techniques His recent publications demonstrate strong trends in knowledge graph construction, similarity-based querying, and efficient graph data management. These works combine theoretical advancements with practical implementations in real-world systems like IMGpedia and MillenniumDB. Scientific achievements include: Best Paper Award at CoopIS 2023 Best Demonstration Award runner-up at SIGMOD/PODS 2024 Best Student Paper (Resources Track) and Best Poster at ISWC 2017 First prize in CLEI 2017 for his Master's thesis Sebastián currently leads the Fondecyt project on graph data management and contributes to the U-Inicia project on AI processes in graph databases. He serves on the editorial board of Transactions on Graph Data and Knowledge.

Laura Haasمشاهده پروفایل

استاد

• Data Management
• Database Systems
• Information Integration
• +۴ مورد دیگر

Laura Haas serves as the Donna M and Robert J Manning Dean at the University of Massachusetts Amherst's College of Information and Computer Sciences. Prior to this, she spent 36 years at IBM, where she rose to the level of IBM Fellow and held leadership roles such as Director of the Accelerated Discovery Lab and Director of Computer Science at IBM's Almaden Research Center. Her research focuses on data integration, database systems, and accelerating discovery through data-driven approaches. She pioneered systems like Starburst, Garlic, and Clio, and founded IBM's Accelerated Discovery Lab to enhance data analysis tools for domain experts. Haas holds a PhD from the University of Texas at Austin (1981) and an AB from Harvard University (1978). She has held sabbaticals at the University of Wisconsin-Madison and ETH Zurich. Her contributions earned her awards like the ACM SIGMOD Codd Innovation Award and Anita Borg Institute Technical Leadership Award. She is a Fellow of the ACM and member of the National Academy of Engineering. Her research spans federated data systems, schema mapping, and big data challenges. She advocates for data science education reforms and chairs key initiatives like the National Academies Computer Science and Telecommunications Board. The DREAM Lab under her leadership explores data systems for exploration and analytics. Education: PhD, University of Texas at Austin, 1981 AB, Harvard University, 1978 Awards: ACM SIGMOD Codd Innovation Award Anita Borg Institute Technical Leadership Award IBM Fellow Member, National Academy of Engineering Labs/Teams: DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) Her work bridges theoretical foundations and practical systems, emphasizing tools that empower domain experts over technical complexities. Current initiatives focus on democratizing data science through education and tooling advancements.

Jeffrey D. Heflinمشاهده پروفایل

استاد

• Semantic Web
• Ontologies
• Artificial Intelligence
• +۳ مورد دیگر

Jeffrey D. Heflin is a Professor in the Department of Computer Science & Engineering at Lehigh University, leading the Semantic Web and Agent Technologies (SWAT) lab. His research focuses on semantic interoperability, ontology reasoning, and distributed knowledge systems. He is a pioneer in Semantic Web research, having authored the first Ph.D. dissertation on the topic. Heflin contributed to key Semantic Web languages like SHOE, DAML+OIL, and OWL, and developed the Lehigh University Benchmark (LUBM), a standard for evaluating large-scale Semantic Web systems. He holds a Ph.D., M.S., and B.S. in Computer Science from the University of Maryland and the College of William and Mary. His service includes editorial roles for the Journal of Web Semantics and organizing ISWC conferences, including co-chairing ISWC 2012. He received the 2004 NSF CAREER Award for his work on distributed ontology systems. Heflin's research addresses challenges in data discovery, table search, and neural-symbolic integration. His lab explores scalable solutions for semantic integration, including projects like the 'Google for research data' initiative and advancements in contextual tag clouds for linked data exploration.

• Database Systems
• Temporal Data Management
• Time Series Analysis
• +۳ مورد دیگر

Anton Dignös is an Associate Professor at the Free University of Bozen-Bolzano, Italy. His research focuses on temporal databases, time series analysis, and database system optimization. He has co-authored numerous papers in top venues such as VLDB, ICDE, and ACM Computing Surveys, with a strong emphasis on query processing, indexing techniques, and benchmarking tools for database systems. Notable contributions include the SEER toolkit for time series benchmarking and foundational work on temporal anomaly detection in healthcare systems. His work spans theoretical advancements in join algorithms and practical applications in manufacturing and monitoring systems. He has collaborated extensively with researchers like Johann Gamper and Michael H. Böhlen, contributing to projects like TSM-Bench and the development of efficient interval join methods optimized for modern hardware.

Michael Neffمشاهده پروفایل

استاد

• Character Animation
• Gesture and Nonverbal Communication
• Virtual Reality
• +۴ مورد دیگر

Michael Neff is a Professor at the University of California, Davis, affiliated with the Department of Computer Science and Department of Cinema and Digital Media. He directs the Motion Lab, an interdisciplinary research group exploring the intersection of computation and human movement. Education: PhD in Computer Science from the University of Toronto (2005), Certified Laban/Bartenieff Movement Analyst (CLMA, 2009) Research Interests: Focus on character animation tools, gesture and nonverbal communication modeling, physics-based animation, and applying performing arts concepts to virtual character movement. His work bridges art and science through collaborations with robotics, psychology, and dance departments. Recent Research Trends: Over the past five years, his publications demonstrate a focus on speech-driven gesture synthesis, physics-based character control in VR environments, and multimodal analysis of movement perception. Key themes include tension modeling, motion style transfer, and avatar trust dynamics. Awards & Recognition: NSF CAREER Award (2009-14) Isadora Duncan Award for Visual Design (2009) Best Paper Awards at Intelligent Virtual Agents (2007), Motion in Games (2015, 2016) Alain Fournier Memorial Award (2005) IBM PhD Fellowship for advisee Simbarashe Nyatsanga Advising & Collaborations: Has mentored 18 graduate students (PhD/Masters) and numerous undergraduates. Collaborates across disciplines with computer scientists, dancers, and psychologists. Currently chairs the Department of Cinema and Digital Media. Laboratory Facilities: Established a motion capture lab in 2007 featuring a 12-camera optical system in a 750 sq ft studio, supporting interdisciplinary research in movement analysis.

Luigi Palopoliمشاهده پروفایل

استاد

• Hybrid Systems
• Real-Time Scheduling
• Quality of Service Control
• +۱۲ مورد دیگر

Luigi Palopoli is a Full Professor and Director at the Department of Information Engineering and Computer Science (DISI), University of Trento, Italy. He is also a member of the University Council. His research spans hybrid systems, real-time scheduling, quality of service control, robotics, embedded systems, and cyber-physical systems. His research interests include: Hybrid Systems and Real-Time Scheduling Service and Assistive Robotics for Elderly Support AI Applications in Robotics and Embedded Systems Outlier Detection and Anomaly Explanation Audio Super-Resolution and Signal Processing Biological Network Analysis and Graph Alignment His recent publications reflect a strong interdisciplinary focus, combining AI, robotics, real-time systems, and bioinformatics. Trends show increasing work in explainable AI, assistive technologies, and multimodal learning (e.g., vision transformers for audio). He has made notable contributions to robotic navigation for the elderly, real-time embedded systems, and logic-based outlier detection. Scientific awards include: Outstanding Paper Award at RTAS 2017 He advises students and collaborates widely, especially with Daniele Fontanelli, Luca Abeni, and other researchers in robotics and AI. He maintains an active research group, evidenced by a dedicated YouTube channel for the Embedded Intelligence and Robotic Systems group. He is involved in teaching, including courses on Robot Planning and Real-Time Operating Systems, and participates in AI seminar series. There is no indication of retirement or former status; he is actively engaged in research, teaching, and academic service.

Prateek Jainمشاهده پروفایل

مدرس

• Knowledge Graphs
• Natural Language Processing
• Machine Learning
• +۶ مورد دیگر

Prateek Jain is a Lecturer at the School of Information, San José State University, where he teaches courses in Big Data Analytics and Python programming. He holds a Ph.D. in Computer Science and Engineering from Wright State University and has over a decade of industrial experience in data science and artificial intelligence at leading institutions such as IBM T.J. Watson Research Center, Nuance Research Labs, and Liveperson. His research interests include Knowledge Graphs, Natural Language Processing, Machine Learning, and Big Data, with over 30 peer-reviewed publications and patents in these areas. His academic and professional background bridges advanced research and practical deployment of AI-driven systems. Ph.D. (Computer Science and Engineering), Wright State University (2007–2012) Ph.D. (Computer Science), University of Georgia (2006–2007, transferred) BS (Information and Communication Technology), Dhirubhai Ambani Institute of ICT (2002–2006) Prateek's research publications focus on the development and application of Knowledge Graphs, particularly in areas like NLP, enterprise search, healthcare, and financial services. His work emphasizes scalable, explainable, and practical AI solutions, with recurring themes in graph embeddings, semantic reasoning, and large-scale data integration. He has served as an Adjunct Instructor at Chabot College and Northwestern Polytechnic University, and currently contributes to academic instruction at SJSU while maintaining an active role in industry as Principal Data Scientist at Liveperson. His industrial roles have spanned: Principal Data Scientist, Knowledge Graphs – Liveperson Inc. (2021–Present) Engineering Manager, Machine Learning – AppZen Inc. (2018–2021) Principal Research Engineer – Nuance Inc. (2016–2018) VP/Data Scientist – BlackRock Inc. (2015–2016) Data Scientist – Ignition One (2015) Research Staff Member – IBM T.J. Watson Research Center (2012–2015) There are no listed scientific awards or advisees in the provided information.

• Information Management
• Knowledge Management
• Data Integration
• +۵ مورد دیگر

Khalid Belhajjame is a Lecturer at Paris Dauphine University, where he is a member of the LAMSADE research laboratory. His academic career spans multiple prestigious institutions, having previously worked as a researcher at the University of Manchester and completed his PhD at the University of Grenoble. His research focuses on information and knowledge management systems with applications across various scientific domains. Belhajjame's research interests center on information and knowledge management, with specific contributions to pay-as-you data integration, e-Science, scientific workflow management, provenance tracking and exploitation, and semantic web services. His work bridges theoretical computer science with practical applications in astronomy, biodiversity, and life sciences. He has developed innovative approaches to workflow management, data integration, and provenance tracking that enhance scientific reproducibility and data transparency. His publication record shows a clear trajectory of advancing scientific workflow systems and data management techniques. Recent work focuses on workflow validation, knowledge graph maintenance, bioinformatics data analysis, and privacy-aware workflows. His research demonstrates increasing sophistication in handling complex data provenance while maintaining usability and transparency. The interdisciplinary nature of his work is evident in applications spanning genomics, business process analytics, and cloud computing. Belhajjame actively contributes to the scientific community through multiple roles. He serves on the editorial board of the MethodX Elsevier journal and has participated in numerous European, French, and UK-funded projects. His leadership extends to co-leading ProvBench, a provenance benchmarking initiative, and participating in influential working groups including the W3C Provenance working group and the NSF-funded DataONE working group on scientific workflows and provenance. He has established himself as a key contributor to research object frameworks and scientific workflow standards. His collaborations span multiple continents and disciplines, reflecting the broad applicability of his work in data management and workflow systems. His current research continues to push boundaries in making scientific workflows more transparent, reusable, and privacy-preserving.

Dario Colazzoمشاهده پروفایل

استاد

• Databases
• Programming Languages
• Cloud Computing
• +۷ مورد دیگر

Dario Colazzo is a Professor at Université Paris-Dauphine, affiliated with the LAMSADE laboratory. His research focuses on database systems and programming languages, particularly in cloud databases, type systems for semi-structured data, and query processing optimizations. He has contributed extensively to XML and XQuery research, including parallel query execution and static analysis techniques. His teaching includes courses on programming (Java), UML, database systems, and semantic web technologies at both undergraduate and graduate levels (in French). Key research interests span XML/XQuery optimization, JSON processing, distributed systems, and type systems for safe and efficient data handling. His work emphasizes scalable cloud-based solutions and efficient query execution models. Publications highlight advancements in parallel query processing (e.g., PAXQuery), type-based optimizations for XML updates, and semantic graph analytics. He has collaborated with institutions like EDBT, WWW, and VLDB conferences.

Diego Calvaneseمشاهده پروفایل

استاد مهمان

• Artificial Intelligence for Data Management
• Knowledge Representation and Reasoning
• Virtual Knowledge Graphs
• +۵ مورد دیگر

Diego Calvanese is a Visiting Professor at Umeå University's Department of Computing Science and holds a full professorship at the Free University of Bozen-Bolzano, Italy. His research focuses on AI for data management, including virtual knowledge graphs (VKG), ontology-based data access (OBDA), and formal methods like description logics. He is part-time at Umeå, balancing roles with his primary position in Italy. Calvanese has received prestigious awards including the AAAI Classic Paper Award (2021), EurAI Fellow (2015), and ACM Fellow (2019). He supervises three doctoral students at Umeå and has authored over 350 publications, with an h-index of 71. His work emphasizes data integration, geospatial systems, and ethical AI applications. Key Roles: Associate Programme Chair (IJCAI 2025), Programme Chair (IJCAI-ECAI 2026), Head of AI for Data Management Research Group Research Interests: Knowledge representation, graph data management, explainable AI, and telemonitoring systems like reCOVeryaID. His research group develops tools like Ontop, a VKG system enabling seamless data access across heterogeneous sources. Current projects include geospatial data integration and temporal OBDA frameworks. Calvanese has served on over 150 program committees and editorial boards, including Artificial Intelligence and JAIR. His work bridges technical advancements with societal impacts, addressing AI's role in healthcare, climate, and democracy.