Sudeepa Roy

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                               Assistant Professor
                               Department of Computer Science
                               Duke University
                               308 Research Drive
                               Campus Box 90129
                               Durham, NC 27708-0129

                               Office: D325 LSRC Building
                               Phone: (919)-660-6596
                               Fax: (919) 660-6519
                               E-mail: sudeepa AT cs DOT duke DOT edu      

Background       Research       Projects       Funding       Services       Teaching     Students       Publications       Patents


I joined the Department of Computer Science at Duke University in Fall 2015.
I am a member of the Duke Database Group (a.k.a. Duke Database Devils; more about Duke Blue Devils),
which is part of the Duke Systems and Architecture Group.

Before joining Duke, I was a postdoctoral research associate in the Department of Computer Science and Engineering,
University of Washington where I worked with Prof. Dan Suciu and the database group.

I graduated from the University of Pennsylvania with a Ph.D. in Computer and Information Science where I was advised by
Prof. Susan Davidson and Prof. Sanjeev Khanna. During my Ph.D., I did two internships at IBM Research, Almaden,
and received a Google PhD fellowship in Structured Data in 2011.

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I am broadly interested in data and information management with a focus on foundational aspects of databases and
big data analysis. My current research focuses on building tools and techniques to help users leverage the maximum benefit
from the available data. While my ongoing work on causality and explanations in databases directly aims to assist users get
deep insights into data by providing causal analysis and rich explanations to their questions, my work in the areas of data and workflow provenance,
probabilistic databases, and crowd-sourcing
probes into compelling, fundamental questions that need to be answered
to enable end-to-end processing and analysis of unstructured, noisy, and unreliable data in today's world while preserving its entire context.

See my publications.




Project page for
FIREFly: Formal Interactive Rich Explanations on the Fly

Project page for
Hume: A Unified and Declarative Approach to Causal Analysis for Big Data

Project page for
FLAME: Fast, Large-scale, Almost Matching Exactly

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