معرفی
Sudeepa Roy is an Associate Professor of Computer Science at Duke University's Department of Computer Science within Trinity College of Arts & Sciences. She joined Duke in Fall 2015 after completing a postdoctoral research associate position at the University of Washington's Department of Computer Science and Engineering, where she worked with Professor Dan Suciu and the database group.
Her educational background includes a Ph.D. in Computer and Information Science from the University of Pennsylvania, where she was advised by Professors Susan Davidson and Sanjeev Khanna. During her doctoral studies, she completed two internships at IBM Research, Almaden.
Roy's research spans three interconnected thrusts in computer science: (1) Data management, focusing on repairing noisy data, data provenance, and tools for helping novices learn relational queries; (2) Data analysis, investigating interpretable causal inference techniques and meaningful explanations for data analysis pipelines; and (3) Database theory, exploring foundational problems at the intersection of databases, logic, and algorithms. Her work bridges theoretical rigor with practical applications across various domains.
Her recent publications demonstrate a strong trajectory in causal inference, database theory, and privacy-preserving data analysis, with a notable emphasis on making complex database operations interpretable and accessible. The publications reveal increasing focus on causal explanations, differentially private query processing, and novel approaches to database repairs and query optimization.
- VLDB Endowment Early Career Research Contributions Award, 2022
- NSF Career Award, 2016
- Google Ph.D. Fellowship, 2011 (the first Google fellowship in Structured Data)
- SIGMOD Best Artifact Award - Honorable Mention, 2023
Roy has successfully mentored numerous graduate and undergraduate students, with former PhD students securing positions at institutions like Yale University, Simon Fraser University, and Megagon Labs. Her research has been supported by multiple significant grants including an NSF Award IIS-2147061 on "FAI: An Interpretable AI Framework for Care of Critically Ill Patients," an NSF Award IIS-2008107 on "Helping Novices Learn and Debug Relational Queries," and an NIH Award 1R01EB025021-01 on causal inference methods for big data. She is an active member of the Duke Database Group (Duke Database Devils) and has served in leadership roles for major conferences including as PC Co-Chair of ACM SIGMOD 2026 and PC Chair of ICDT 2025.
