Sudeepa Roy

Sudeepa Roy
Associate 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
News
I am serving as the PC Co-Chair of
ACM SIGMOD 2026
with Carsten Binnig. The Call For Papers can be found
here
I am serving as the PC Chair of the 28th International Conference on Database Theory (ICDT) 2025, to be held in Barcelona, Spain in March 2025 as the
EDBT/ICDT 2025 Joint Conference
In 2023-24, I spent a wonderful sabbatical in Berkeley, CA.
I spent Fall'23 at the Simons Institute for the Theory of Computing, University of California, Berkeley, where I co-organized (with Guy Van den Broeck, Hung Ngo, Dan Suciu, and Virginia Vassilevska Williams) a semester-long program titled
Logic and Algorithms in Database Theory and AI
. This program was attended by about 100 long-term and short-term participants. Please check out the webpage for the videos and slides of many tutorials and talks!
I spent Spring'24 as a Visiting Scientist at
RelationalAI
, and also as a Visiting Scholar at UC Berkeley
Sky Computing
, hosted by Prof. Joe Hellerstein.
I am honored to give an invited keynote at the EDBT/ICDT Joint Conference 2024 in Paestum, Italy on March 25, 2024 titled "How Database Theory Helps Teach Relational Queries in Database Education".
Background
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 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
Research
My research area is broadly in Databases, Data Management, and Data Analysis in Computer Science. My recent research focuses on foundational aspects of big data processing and responsible data analysis along three thrusts:
Data management:
I work on repairing noisy data, data provenance, developing tools to help new programmers and students learn and debug relational queries, query optimization, data exploration, and other problems. Earlier, I worked on information extraction,
workflow provenance, and crowd-sourcing.
Data analysis:
I work on interpretable and scalable causal inference techniques for complex observational data, meaningful explanations for different steps in the data analysis pipeline, and prescriptive data analytics. I also work on data science applications in different domains.
Database theory:
I work on various database theory problems at the intersection of databases, logic, and algorithms, including query evaluation and repair on databases annotated by semirings, evaluation of recursive datalog programs, circuits for data provenance, and query evaluation on probabilistic databases.
See my
publications
Awards
VLDB Endowment Early Career Research Contributions Award, 2022
[link]
[article]
NSF Career Award, 2016
[link]
Google Ph.D. Fellowship, 2011 (the first Google fellowship in Structured Data) [
link
SIGMOD Best Artifact Award - Honorable Mention, 2023
Distinguished Reviewer / PC member: SIGMOD 2023, VLDB 2021, SIGMOD 2020, SIGMOD 2017
Projects
Funding
NSF Award
IIS-2147061
: "FAI: An Interpretable AI Framework for Care of Critically Ill Patients Involving Matching and Decision Trees". Cynthia Rudin (PI), Sudeepa Roy (co-PI), and Alexander Volfovsky (co-PI). Duke University. 2022-2025. $625,000
(from the NSF program on
"Fairness in AI"
in collaboration with Amazon, total funding: $1 million).
NSF Award
IIS-2008107
: "III: Small: Helping Novices Learn and Debug Relational Queries". Jun Yang (PI), Sudeepa Roy (co-PI), and Kristin Stephens-Martinez (co-PI). Duke University. 2020-2023. $499,972.
NIH Award 1R01EB025021-01: "QuBBD: Collaborative Research: Matching Methods for Causal Inference: Big Data and Networks". PI:
Alexander Volfovsky
(Duke University, Statistical Science), Co-Investigators:
Allison Aiello
(University
of North Carolina, Chapel Hill, Global Public Health), and Sudeepa Roy and
Cynthia Rudin
(Duke University, Computer Science), 2017-2020, $848,708 (Duke's
share of the award $513,651).
NSF Award
IIS-1703431
: "III: Medium: Collaborative Research: A Unified and Declarative Approach to Causal Analysis for Big Data". PIs:
Lise Getoor
(University of California,
Santa Cruz), Sudeepa Roy (Duke University), and
Dan Suciu
(University of Washington, lead
institute), 2017-2021, $1,216,000 (Duke's share of the award $408,000).
NSF CAREER Award
IIS-1552538
: "CAREER: FIREFLY - Rich Explanations for Database Queries". Principal Investigator. 2016-2021, $550,000.
Services
Organization / Advisory
Award Committee Member
Test-of-Time Award Committee for Symposium on PrincipleS of Database Systems (PODS) 2022
Best Demonstration Award Committee, ACM SIGMOD International Conference on Management of Data (SIGMOD) 2020
Test-of-Time Award Committee for International Conference on Database Theory (ICDT) 2015
Program Committee Member
SIGMOD (Research Track):
2024
(Associate Editor),
2023
2022
(Associate Editor),
2021
2020
2019
2018
2017
2015
2014
VLDB (VLDB Review Board):
2021
2017
PODS:
2020
2018
2016
ICDT:   2023,
2021,
2018
2015
ACM FaaCT:
2023
SIGMOD (Demonstration Track):
2020
2016
2015
ICDE (Demonstration Track):
2016
VLDB Journal Special Issue on Data Science for Responsible Data Management:   2021
PVLDB Reproducibility Program Board:   2018
IJCAI (Special Track on AI and The Web):
2016
COMAD:   2023 and 2022 (Applied Data science Track),
2021
(Senior PC Member),
2020
2019
2018
2017
TaPP:   2023, 2019,
2016
2015
2013
WebDB:
2016
2015
HILDA:
2017
SIGMOD Student Research Competition:   2020,
2020
2017
SIGMOD Undergraduate Research Competition:
2016
VLDB Ph.D. Workshop:
2022
2016
External Reviewer
Frequent reviewer of ACM Transactions on Database Systems (TODS),
IEEE Transactions on Knowledge and Data Engineering (TKDE), VLDB Journal
ACM Transactions on Algorithms,
SIAM Journal on Computing
PODS (2013, 2011), VLDB (2010), SODA (2010)
Teaching
Spring 2025:
CompSci 590.06
-- Causal Inference,
Fairness, and Explanations
Fall 2024:
CompSci 316
-- Introduction to Databases
Spring 2023:
CompSci 590.01
-- Causal Inference in Data Analysis
with Applications to Fairness and Explanations
Fall 2022:
CompSci 316
-- Introduction to Databases
Spring 2022:
CompSci 516
-- -- Database Systems
Fall 2020:
CompSci 316
-- Introduction to Databases
Spring 2020:
CompSci 316
-- Introduction to Databases
Fall 2019:
CompSci 516
-- -- Database Systems
Spring 2019:
CompSci 316
-- Introduction to Databases
Fall 2018:
CompSci 516
-- Database Systems
Fall 2017:
CompSci 516
-- Database Systems
Spring 2017:
CompSci 316
-- Introduction to Databases
Fall 2016:
CompSci 516
-- Data Intensive Computing Systems
Spring 2016:
CompSci 516
-- Data Intensive Computing Systems
Fall 2015:
CompSci 590.06
-- Understanding Data: Theory and Applications
Students
I am fortunate to work with a number of wonderful graduate/undergraduate students and postdocs at Duke!
(and the list below does not include the great students/postdocs advised my colleagues at Duke and other schools I work with).
Current students / postdocs
Yuxi Liu (PhD, co-advised with
Jun Yang
Haibo Xiu (PhD, co-advised with
Jun Yang
Fangzhu Shen (PhD)
Former students and postdocs
Dr. Amir Gilad
(Postdoc, 2023, earlier a visiting student from Tel Aviv University, First Employment: Faculty member at the Hebrew University of Jerusalem, Israel)
Dr. Harsh Parikh
(PhD, 2023, co-advised with
Cynthia Rudin
, First Employment: postdoc at Johns Hopkins University, Will join as an Assistant Professor at Yale University, School of Public Health)
Dr. Zhengjie Miao
(PhD, 2022, Co-winner of Best Dissertation Award at Duke CS, First employment: Megagon Labs, Now: Assistant Professor at Simon Fraser University, Canada)
Dr. Prajakta Kalmegh (PhD, 2019, co-advised with Shivnath Babu, First employment: Unravel Data)
Tingyu Wang (MS, 2023)
Danyu Sun (MS, 2023)
Kehan Lyu (MS, 2020)
Yameng Liu (MS, 2019)
Andrew Lee (MS, 2018)
Xiaodan Zhu (MS, 2016)
Kushagra Ghosh (Undergraduate, Spring 2023)
Haoning Jiang (Undergraduate, Fall 2021)
James Leong (Undergraduate, Spring 2021)
Aparimeya Taneja (Undergraduate, Spring 2021)
Kevin Day (Undergraduate, Fall 2020)
Jeremy Cohen (Undergraduate, Fall 2019)
Niyaz Nurbhasha (Undergraduate, Fall 2019)
Cheryl Wang (Undergraduate, Fall 2018)
Frederick Xu (Undergraduate, Fall 2017 and Spring 2018, Honorable mention for the Computing Research Association's (CRA) Outstanding Undergraduate Researcher Award for 2019)
Harrison Lundberg (Undergraduate, Fall 2017, Co-winner of the Alex Vasilos award for Excellence in Computer
Science Research)
Duke CS+ undergraduate summer internship mentoring:
James Lim (2021), Allen Pan (2021), Zachary Zheng (2021), Alexander Bendeck (2020), Jeffrey Luo (2020)
Publications
Book Chapters
Trends in Explanations: Understanding and Debugging Data-driven Systems
[pdf]
(with Boris Glavic and Alexandra Meliou)
Foundations and Trends in Databases, Vol 11, No. 3, 2021
Uncertain Data Lineage
[pdf]
Encyclopedia of Database Systems, 2nd edition, Springer, 2018.
Provenance: Privacy and Security
[pdf]
(with Susan Davidson)
Encyclopedia of Database Systems, 2nd edition, Springer, 2018.
Tutorials
Causality and Explanations in Databases
[pdf]
[slides]
(with Alexandra Meliou and Dan Suciu)
International Conference on Very Large Data Bases (VLDB) 2014.
Journal Publications
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference
[pdf]
[arxiv]
(with Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Cynthia Rudin, and Alexander Volfovsky)
Journal of Machine Learning Research (JMLR), Vol. 22, No. 31, pages 1−41, 2021.
Computing Optimal Repairs for Functional Dependencies
[pdf]
(with Ester Livshits and Benny Kimelfeld)
ACM Transactions on Database Systems (TODS), Vol. 45, Issue 1, pages 4:1--4:46, 2020 (best paper special issue).
Exact Model Counting of Query Expressions: Limitations of Propositional Methods
[pdf]
(with Paul Beame, Jerry Li, and Dan Suciu)
ACM Transactions on Database Systems (TODS), Vol. 42, Issue 1, pages 1:1-1:46, 2017.
(Preliminary versions in ICDT 2014 and UAI 2013)
Answering Conjunctive Queries with Inequalities
[pdf]
(with Paraschos Koutris, Tova Milo, and Dan Suciu)
Theory of Computing Systems (TOCS), Springer, Vol. 61, Number 1, pages 2-30, 2017.
(A preliminary version appeared in ICDT 2015)
Top-k and Clustering with Noisy Comparisons
[pdf]
(with Susan B. Davidson, Sanjeev Khanna, and Tova Milo)
ACM Transactions on Database Systems (TODS), Vol. 39, Issue 4, pages 35:1--35:39, 2014 (best paper special issue).
(A preliminary version appeared in ICDT 2013)
Invited Articles
Toward Interpretable and Actionable Data Analysis with Explanations and Causality
[pdf]
PVLDB, Vol 15(12), 2022 (Article for the VLDB Early Career Research Award)
Making AI Machines Work for Humans in FoW
[pdf]
(with Sihem Amer-Yahia, Senjuti Basu Roy, Lei Chen, Atsuyuki Morishima, James Abello Monedero, Pierre Bourhis, François Charoy, Marina Danilevsky, Gautam Das, Gianluca Demartini, Shady Elbassuoni, David Gross-Amblard, Emilie Hoareau, Munenari Inoguchi, Jared B. Kenworthy, Itaru Kitahara, Dongwon Lee, Yunyao Li, Ria Mae Borromeo, Paolo Papotti, H. Raghav Rao, Pierre Senellart, Keishi Tajima, Saravanan Thirumuruganathan, Marion Tommasi, Kazutoshi Umemoto, Andrea Wiggins, and Koichiro Yoshida)
SIGMOD Record 2020 (49(2), pages 30-35)
On Benchmarking for Crowdsourcing and Future of Work Platforms
[pdf]
(with Ria Mae Borromeo, Lei Chen, Abhishek Dubey, and Saravanan Thirumuruganathan)
IEEE Data Engineering Bulletin 2019 (42(4), pages 46-54)
Query Perturbation Analysis: An Adventure of Database Researchers in Fact-Checkings
[pdf]
(with Jun Yang, Pankaj K. Agarwal, Brett Walenz, You Wu, Cong Yu, and Chengkai Li)
IEEE Data Engineering Bulletin 2018 (41(3), pages 28-42)
On the Complexity of Evaluating Order Queries with the Crowd
[pdf]
(with Benoit Groz and Tova Milo)
IEEE Data Engineering Bulletin 2015 (38(3), pages 44-58)
Conference Publications
Refining Labeling Functions with Limited Labeled Data.
(with Chenjie Li, Amir Gilad, Boris Glavic, and Zhengjie
Miao).
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025.
Fair and Actionable Causal Prescription Ruleset.
(with Benton Li, Nativ Levy, Brit Youngmann, and Sainyam Galhotra)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2025.
Circuits and Formulas for Datalog over Semirings.
(with Austen Z. Fan and Paraschos Koutris)
ACM Principles of Database Systems (PODS), 2025.
Graph Neural Network based Double Machine Learning Estimator of Network Causal Effects.
(with Seyedeh Baharan Khatami, Harsh Parikh, Haowei Chen, and Babak Salimi)
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
The Cost of Representation by Subset Repairs.
(with Yuxi Liu*, Fangzhu Shen*, Kushagra Ghosh, Amir Gilad, and Benny Kimelfeld)
Proceedings of the VLDB Endowment (PVLDB), 2024.
Qr-Hint: Actionable Hints for Guided SQL Query Debugging.
(with Yihao Hu, Amir Gilad, Kristin Stephens-Martinez, and Jun Yang)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2024.
Summarized Causal Explanations For Aggregate Views.
(with Brit Youngmann, Amir Gilad, and Michael Cafarella)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2024.
Evaluating Datalog over Semirings: A Grounding-based Approachg.
(with Hangdong Zhao, Shaleen Deep, Paris Koutris, and Val Tannen)
ACM Principles of Database Systems (PODS), 2024.
Evaluating Pre-Trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference.
(with Travis Seale-Carlisle*,
Saksham Jain*,
Courtney Lee,
Caroline Levenson,
Swathi Ramprasad,
Brandon Garrett, Cynthia Rudin, and Alexander Volfovsky)
AAAI Conference on Artificial Intelligence (AAAI), 2024, AI for Social Impact (AISI) special track.
DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms.
(with Shweta Patwa, Danyu Sun, Amir Gilad, and Ashwin Machanavajjhala)
Proceedings of the VLDB Endowment (PVLDB), Vol 17 (1), 2023.
Explaining Differentially Private Query Results With DPXPlain.
(with Tingyu Wang, Yuchao Tao, Amir Gilad, and Ashwin Machanavajjhala)
Proceedings of the VLDB Endowment (PVLDB), 2023, Demonstration Track.
Characterizing and Verifying Queries Via CINSGEN.
(with Hanze Meng, Zhengjie Miao, Amir Gilad, and Jun Yang)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2023, Demonstration Track.
Causal What-If and How-To Analysis Using HypeR.
(with Fangzhu Shen, Kayvon Heravi, Oscar Gomez, Sainyam Galhotra, Amir Gilad, and Babak Salimi)
International Conference on Data Engineering, Demonstration Track, 2023.
DPXPlain: Privately Explaining Aggregate Qery Answers.
[pdf]
(with Yuchao Tao, Amir Gilad, and Ashwin Machanavajjhala)
Proceedings of the VLDB Endowment (PVLDB), Vol 16 (1), 2022.
HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach.
(with Sainyam Galhotra*, Amir Gilad*, and Babak Salimi)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2022.
Selectivity Functions of Range Queries are Learnable.
(with Xiao Hu, Yuxi Liu, Haibo Xiu, Pankaj Agarwal, Debmalya Panigrahi, and Jun Yang)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2022.
Understanding Queries by Conditional Instances.
[arxiv]
(with Amir Gilad*, Zhengjie Miao*, and Jun Yang)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2022.
CaJaDE: Explaining Query Results by Augmenting Provenance with Context.
(with Chenjie Li, Juseung Lee, Zhengjie Miao, and Boris Glavic)
Proceedings of the VLDB Endowment (PVLDB), Vol 15, demonstration track, 2022.
Putting Things into Context: Rich Explanations for Query Answers using Join Graphs.
[pdf]
[arxiv]
(with Chenjie Li, Zhengjie Miao, Qitian Zeng, and Boris Glavic)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2021.
Properties of Inconsistency Measures for Databases
[pdf]
(with Ester Livshits, Rina Kochirgan, Segev Tsur, Ihab Ilyas, and Benny Kimelfeld)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2021.
Aggregated Deletion Propagation for Counting Conjunctive Query Answers
[pdf]
[full version]
(with Xiao Hu, Shouzhuo Sun, Shweta Patwa, and Debmalya Panigrahi)
Proceedings of the VLDB Endowment (PVLDB), Vol 14, 2020.
I-Rex: An Interactive Relational Query Explainer for SQL
[pdf]
(with Zhengjie Miao, Tiangang Chen, Alexander Bendeck, Kevin Day, and Jun Yang)
Proceedings of the VLDB Endowment (PVLDB), Vol 13, demonstration track, 2020.
MuSe: Multiple Deletion Semantics for Data Repair
[pdf]
(with Amir Gilad, Yihao Hu, and Daniel Deutch)
Proceedings of the VLDB Endowment (PVLDB), Vol 13, demonstration track, 2020.
On Multiple Semantics for Declarative Database Repairs
[pdf]
[arxiv]
(with Amir Gilad and Daniel Deutch)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2020.
Computing Local Sensitivities of Counting Queries with Joins
[pdf]
[arxiv]
(with Yuchao Tao, Xi He, and Ashwin Machanavajjhala)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2020.
Causal Relational Learning
[pdf]
[arxiv]
(with Babak Salimi, Harsh Parikh, Moe Kayali, Lise Getoor, and Dan Suciu)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2020.
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
[arxiv]
(with Marco Morucci*, Vittorio Orlandi*, Cynthia Rudin, and Alexander Volfovsky)
To appear in Conference on Uncertainty in Artificial Intelligence (UAI), 2020.
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
[arxiv]
(with M. Usaid Awan*, Marco Morucci*, Vittorio Orlandi*, Cynthia Rudin, and Alexander Volfovsky)
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
Learning to Sample: Counting with Complex Queries
[arxiv]
(with Brett Walenz, Stavros Sintos, and Jun Yang)
Proceedings of the VLDB Endowment (PVLDB), Vol 13, 2019.
Almost Matching Exactly With Instrumental Variables
[arxiv]
(with M.Usaid Awan*, Yameng Liu*, Marco Morucci*, Cynthia Rudin, and Alexander Volfovsky)
Conference on Uncertainty in Artificial Intelligence (UAI) 2019.
CAPE: Explaining Outliers by Counterbalancing
[pdf]
(with Zhengjie Miao*, Qitian Zeng*, Chenjie Li, Boris Glavic, and Oliver Kennedy)
Proceedings of the VLDB Endowment (PVLDB), Vol 12, demonstration track, 2019.
LensXPlain: Visualizing and Explaining Contributing Subsets for Aggregate Query Answers
[pdf]
(with Zhengjie Miao and Andrew Lee)
Proceedings of the VLDB Endowment (PVLDB), Vol 12, demonstration track, 2019.
Almost-Exact Matching with Replacement for Causal Inference
[arxiv]
(with Awn Dieng*, Yameng Liu*, Cynthia Rudin, and Alexander Volfovsky)
International Conference on
Artificial Intelligence and Statistics (AISTATS), 2019.
RATest: Explaining Wrong Queries Using Small Examples
[pdf]
(with Zhengjie Miao and Jun Yang)
ACM SIGMOD International Conference on Management of Data (SIGMOD), demonstration track, 2019.
Explaining Wrong Queries Using Small Examples
[pdf]
(with Zhengjie Miao and Jun Yang)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2019.
Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances
[pdf]
(with Zhengjie Miao*, Qitian Zeng*, and Boris Glavic)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2019.
iQCAR: inter-Query Contention Analyzer for Data Analytics Frameworks
[pdf]
(with Prajakta Kalmegh and Shivnath Babu)
ACM SIGMOD International Conference on Management of Data (SIGMOD), 2019.
Interactive
Summarization and Exploration of Top Aggregate Query Answers
[pdf]
(with Yuhao Wen, Xiaodan Zhu, and Jun Yang)
Proceedings of the VLDB Endowment (PVLDB) 2018, Vol 11 Issue 13/VLDB 2019.
Computing Optimal Repairs for Functional Dependencies
[arxiv]
(with Ester Livshits and Benny Kimelfeld)
ACM Principles of Database Systems (PODS) 2018.
iQCAR: A demonstration of an Inter-query Contention Analyzer for Cluster Computing Frameworks
[pdf]
(with Prajakta Kalmegh, Harrison Lundberg, Frederick Xu, and Shivnath Babu)
ACM SIGMOD International Conference on Management of Data (SIGMOD), demonstration track, 2018.
QAGView: Interactively Summarizing High-Valued Aggregate Query Answers
[pdf]
(with Yuhao Wen, Xiaodan Zhu, and Jun Yang)
ACM SIGMOD International Conference on Management of Data (SIGMOD), demonstration track, 2018.
Optimizing Iceberg Queries with Complex Joins
[pdf]
(with Brett Walenz and Jun Yang)
ACM SIGMOD International Conference on Management of Data (SIGMOD) 2017.
Explaining Query Answers with Explanation-Ready Databases
[pdf]
[slides]
(with Laurel Orr and Dan Suciu)
Proceedings of the VLDB Endowment (PVLDB) Vol 9/VLDB 2016.
Answering Conjunctive Queries with Inequalities
[pdf]
(with Paraschos Koutris, Tova Milo, and Dan Suciu)
International Conference on Database Theory (ICDT) 2015
A Formal Approach to Finding Explanations for Database Queries
[pdf]
[slides]
(with Dan Suciu)
ACM SIGMOD International Conference on Management of Data (SIGMOD) 2014.
Circuits for Datalog Provenance
[pdf]
[slides]
(with Daniel Deutch, Tova Milo, and Val Tannen)
International Conference on Database Theory (ICDT) 2014.
Model Counting of Query Expressions: Limitations of Propositional Methods
[pdf]
(with Paul Beame, Jerry Li, and Dan Suciu)
International Conference on Database Theory (ICDT) 2014.
Invited to ACM TODS as one of the best papers in ICDT 2014
Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases
[pdf]
[slides]
(with Paul Beame, Jerry Li, and Dan Suciu)
Conference on Uncertainty in Artificial Intelligence (UAI) 2013.
Provenance-based Dictionary Refinement in Information Extraction
[pdf]
[slides]
(with Laura Chiticariu, Vitaly Feldman, Frederick R Reiss and Huaiyu Zhu)
ACM SIGMOD International Conference on Management of Data (SIGMOD) 2013.
Using the Crowd for Top-k and Group-by Queries
[pdf]
[slides]
(with Susan B. Davidson, Sanjeev Khanna and Tova Milo)
International Conference on Database Theory (ICDT) 2013.
Invited to ACM TODS as one of the best papers in ICDT 2013
A Propagation Model for Provenance Views of Public/Private Workflows
[pdf]
[slides]
(with Susan B. Davidson and Tova Milo)
International Conference on Database Theory (ICDT) 2013.
Queries with Difference on Probabilistic Databases
[pdf]
[slides]
(with Sanjeev Khanna and Val Tannen)
International Conference on Very Large Data Bases (VLDB) 2011.
Provenance Views for Module Privacy
[pdf]
[slides]
(with Susan B. Davidson, Sanjeev Khanna, Tova Milo, and Debmalya Panigrahi)
ACM Principles of Database Systems (PODS) 2011.
Faster Query Answering in Probabilistic Databases using Read-Once Functions
[pdf]
[slides]
(with Vittorio Perduca and Val Tannen)
International Conference on Database Theory (ICDT) 2011.
Enabling Privacy in Provenance-Aware Workflow Systems
[pdf]
(with Susan Davidson, Sanjeev Khanna, Julia Stoyanovich, Val Tannen, Yi Chen and Tova Milo)
Vision Track, Conference on Innovative Data Systems Research (CIDR) 2011.
An Optimal Labeling Scheme for Workflow Provenance Using Skeleton Labels
[pdf]
(with Zhuowei Bao, Susan Davidson and Sanjeev Khanna)
ACM SIGMOD International Conference on Management of Data (SIGMOD) 2010.
Optimizing User Views for Workflows
[pdf]
[slides]
(with Olivier Biton, Susan Davidson and Sanjeev Khanna)
International Conference on Database Theory (ICDT) 2009.
STCON in Directed Unique-Path Graphs
[pdf]
[slides]
(with Sampath Kannan and Sanjeev Khanna)
Foundations of Software Technology and Theoretical Computer Science (FSTTCS) 2008.
Automatic Translation of Simulink Models into
Input Language of a Model Checker
[pdf]
(with Meenakshi B. and Abhishek Bhatnagar)
International Conference on Formal Engineering Methods (ICFEM) 2006.
Workshop, Poster, and Other Publications
I-Rex: An Interactive Relational Query Debugger for SQL
[link]
(with Yihao Hu, Zhengjie Miao, James Leong, James Lim, Zachary Zheng, Kristin Stephens-Martinez, and Jun Yang)
ACM Technical Symposium on Computer Science Education (SIGCSE), Demonstration, 2022.
AME: Interpretable Almost Exact Matching for Causal Inference
[link]
(with Haoning Jiang, Tommy Howell, Neha Gupta, Vittorio Perduca, Marco Morucci, Harsh Parikh, Cynthia Rudin, and Alexander Volfovsky)
Conference on Neural Information Processing Systems (NeurIPS), Demonstration, 2021.
iQCAR: Inter-Query Contention Analyze
[pdf]
(with Prajakta Kalmegh and Shivnath Babu)
Symposium on Cloud Computing (SOCC), Poster, 2018.
Hiding Data and Structure in Workflow Provenance
[pdf]
(with Susan B. Davidson and Zhuowei Bao)
Invited paper, International Workshop on Databases in Networked Information Systems (DNIS) 2011.
Privacy Issues in Scientific Workflow Provenance
[pdf]
[slides]
(with Susan Davidson, Sanjeev Khanna and Sarah Cohen Boulakia)
International Workshop on
Workflow Approaches to New Data-centric Science (WANDS) 2010.
* = equal contributions
Ph.D. Dissertation
Provenance and Uncertainty
[pdf]
Sudeepa Roy
University of Pennsylvania, August 2012
Patents
Refining a dictionary for information extraction
(with Laura Chiticariu, Vitaly Feldman, Frederick Reiss, and Huaiyu Zhu)
Assignee: International Business Machines Corporation (IBM)
Publication Number: US 8775419 B2,  2014
Automatic Translation of Simulink Models into Input Language of a Model Checker
(with Meenakshi B. and Abhishek Bhatnagar)
Assignee: Honeywell International Inc.
Publication Number: US 7698668 B2,  2010
Miscellaneous
Reports
On "Go With the Winners" Algorithm
[pdf]
Sudeepa Roy
M. Tech. Thesis, IIT Kanpur, 2006.
Advisors: Prof. Manindra Agrawal and Prof. Somenath Biswas