Bridging Disciplines in Data Management Research to Solve Complex Data Problems

Scientific discovery has undergone profound transformations across multiple paradigms, each bringing new data challenges whose solutions demand bridging multiple areas of computer science. This talk presents a research journey spanning three scientific paradigms and projects that illustrate how domain-driven problems reveal fundamental data management challenges and drive interdisciplinary innovation. From the need to manage complex pipelines and their provenance in computational science (3rd paradigm), to new requirements that arise in data-driven discovery (4th paradigm) to support visual exploration of large-scale spatio-temporal data, and today's AI-powered discovery paradigm (5th paradigm), where AI enables effective and general approaches to the long-standing data integration problem. The projects share a common pattern: complex scientific challenges demand more than single-discipline solutions, and by embracing collaboration across computer science areas and working closely with domain experts, we can identify fundamental research opportunities that lead to both methodological advances and systems with real-world impact.