În data de 23 octombrie 2026, la ora 12:00, în Sala 203 (PBT), prof. Eduard Dragut (Temple University) prezintă:
Data Management Lab @ Temple: From Information Discovery to Intelligent Systems
Rezumat: The growing volume and diversity of digital information create fundamental challenges in how we discover, understand, integrate, and use data. At the Data Management Lab at Temple University, our research addresses these challenges at the intersection of data management, information retrieval, natural language processing, multimodal AI, knowledge graphs, and intelligent systems.
In this talk, I will introduce four interconnected research directions pursued in our lab. Our work in information retrieval investigates federated and thematic search for discovering relevant information across heterogeneous sources. In language and multimodal understanding, we develop methods for named entity recognition, scientific literature mining, health discourse analysis, and interpreting structured visual information such as ER diagrams and workflows. Our data management research spans database optimization, natural-language query interfaces, knowledge graphs, and heterogeneous data integration. Finally, our research in intelligent systems and applications explores AI agents and data-driven approaches to scientific discovery, climate and environmental research, and problems of societal importance.
Through selected projects, I will illustrate how these directions complement one another, from discovering information in diverse forms to extracting and integrating knowledge and enabling intelligent systems to reason over and use it.
I will conclude with an introduction to the dual PhD program between Temple University and the University of Bucharest, highlighting opportunities for joint research and doctoral study.
Bio: Eduard C. Dragut is a Professor of Computer and Information Sciences at Temple University, where he conducts research at the intersection of data management, information retrieval, natural language processing, and artificial intelligence. His work focuses on discovering, extracting, integrating, and understanding information from large, heterogeneous data sources, with particular interests in knowledge graphs, multimodal understanding, intelligent agents, and AI-driven scientific discovery. His research spans foundational advances in data management and AI as well as interdisciplinary applications in environmental and climate science, education, healthcare, and AI for social good. His work has appeared in leading conferences and journals, including SIGMOD, VLDB, NeurIPS, ACL, EMNLP, AAAI, and IEEE TKDE. He has led multiple federally funded research projects and actively mentors graduate and undergraduate students. He is a proud alumnus of the University of Bucharest, where he completed his undergraduate studies.