Raul Castro Fernandez

Raul Castro Fernandez is an Assistant Professor of Computer Science at the University of Chicago and co-founder and Chief Research Officer at invocate. He originated the concept of 'data ecology,' which frames his research on how data moves through and transforms technological, economic, and social systems—and how we can design interventions to make those ecosystems more valuable, equitable, and resilient. He co-leads the Data Ecology research initiative at the Data Science Institute and co-runs Chicago Data Night, a forum that brings together industry and academia in Chicago.

Dr. Castro Fernandez's research centers on data ecology, data discovery, data markets, and data integration. His work develops both theoretical frameworks and practical systems that help organizations find, evaluate, and use data effectively. He approaches data as a socio-technical phenomenon, examining how data shapes our world and how we can shape it back through technical, economic, and social interventions. His research bridges computer science, economics, and social science to address fundamental challenges in data ecosystems.

His recent publications reveal a strong focus on applying large language models to data management challenges, particularly for tabular data discovery and integration. He has developed innovative systems like Pneuma for LLM-based tabular data navigation, Solo for natural language data discovery, and Nexus for correlation discovery in spatio-temporal data. His work also addresses critical challenges in data valuation, data markets, and responsible data sharing, with applications across industry and research contexts.

• SIGMOD Test-of-Time Award (2023)
• NSF CAREER Award (2024)
• Sloan Research Fellowship (2025)

Dr. Castro Fernandez actively mentors students across multiple levels, advising PhD students including Qiming Wang, Yue Gong, and Zhiru Zhu, as well as numerous master's and undergraduate students. His group has developed influential systems including Data Station (for trustworthy data sharing), Ver (for view discovery), Metam (for goal-oriented data discovery), and Solo (for natural language data discovery). These systems address fundamental challenges in data discovery, sharing, and integration, with applications across various domains.

He leads the Data Ecology research group at the University of Chicago, which focuses on developing technical, economic, and social interventions to make data ecosystems more valuable, equitable, and resilient. His team works at the intersection of database systems, machine learning, and economics to build practical systems that address real-world data challenges faced by organizations and individuals, with a particular emphasis on the socio-technical aspects of data sharing and discovery.