Sudeepa Roy | Scholars@Duke

Sudeepa Roy
Associate Professor of Computer Science

I joined the Department of Computer Science at Duke University in Fall 2015.

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.

I obtained my master's and bachelor's degrees in Computer Science from Indian Institute of Technology, Kanpur and Jadavpur University respectively.

Research Interests I am broadly interested in data and information management with a focus on foundational aspects of big data analysis. My research objective is to help users with heterogenous backgrounds and interests leverage the maximum benefit from the available data. While my ongoing work on explanations in databases directly aims to assist users get deep insights into data by providing rich explanations to their questions, my work in the areas of data and workow 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.

Current Appointments & Affiliations

Associate Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2022

Contact Information

Campus Box 90129, 308 Research Drive, Durham, NC 27708
LSRC D325, 308 Research Drive, Durham, NC 27708
sudeepa@cs.duke.edu (919) 660-6596
http://www.cs.duke.edu/~sudeepa

Background
Education, Training, & Certifications
- Ph.D., University of Pennsylvania 2012
Previous Appointments & Affiliations
- Assistant Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2015 - 2022
Recognition
Awards & Honors
- Google Ph.D. Fellowship. Google. 2011
Research
External Relationships
- Alibaba Group Holding Limited
- Amazon, Inc
- Apple Inc.
- Facebook Coporation
- Google, Inc.
- International Business Machines Corp.
- Microsoft Corporation
- Twitter INC
Publications & Artistic Works
Selected Publications
- Academic Articles
  - Glavic, B., A. Meliou, and S. Roy. “Trends in explanations: Understanding and debugging data-driven systems.” Foundations and Trends in Databases 11, no. 3 (August 2, 2021): 226–318. https://doi.org/10.1561/1900000074.
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  - Gupta, Neha R., Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, et al. “dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference,” January 5, 2021.
    Open Access Copy Link to Item
  - Wang, T., M. Morucci, M. U. Awan, Y. Liu, S. Roy, C. Rudin, and A. Volfovsky. “FLAME: A fast large-scale almost matching exactly approach to causal inference.” Journal of Machine Learning Research 22 (January 1, 2021).
    Open Access Copy
  - Wang, Tianyu, Marco Morucci, M Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference.” J. Mach. Learn. Res. 22 (2021): 31:1-31:1.
  - Amer-Yahia, S., S. Basu Roy, L. Chen, A. Morishima, J. Abello Monedero, P. Bourhis, F. Charoy, et al. “Making AI Machines Work for Humans in FoW.” Sigmod Record 49, no. 2 (December 9, 2020): 30–35. https://doi.org/10.1145/3442322.3442327.
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  - Tao, Y., X. He, A. MacHanavajjhala, and S. Roy. “Computing Local Sensitivities of Counting Queries with Joins.” Proceedings of the Acm Sigmod International Conference on Management of Data, June 14, 2020, 479–94. https://doi.org/10.1145/3318464.3389762.
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  - Livshits, E., B. Kimelfeld, and S. Roy. “Computing optimal repairs for functional dependencies.” Acm Transactions on Database Systems 45, no. 1 (February 17, 2020). https://doi.org/10.1145/3360904.
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  - Hu, X., S. Sun, S. Patwa, D. Panigrahi, and S. Roy. “Aggregated deletion propagation for counting conjunctive query answers^∗.” Proceedings of the Vldb Endowment 14, no. 2 (January 1, 2020): 228–40. https://doi.org/10.14778/3425879.3425892.
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  - Awan, M Usaid, Sudeepa Roy, Marco Morucci, Cynthia Rudin, Vittorio Orlandi, and Alexander Volfovsky. “Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference.” International Conference on Artificial Intelligence and Statistics, Vol 108 108 (2020): 3252–61.
    Link to Item
  - Gilad, A., Y. Hu, D. Deutch, and S. Roy. “MuSe: Multiple Deletion Semantics for Data Repair.” Proceedings of the Vldb Endowment 13, no. 12 (January 1, 2020): 2921–24. https://doi.org/10.14778/3415478.3415509.
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  - Hu, Xiao, Shouzhuo Sun, Shweta Patwa, Debmalya Panigrahi, and Sudeepa Roy. “Aggregated Deletion Propagation for Counting Conjunctive Query Answers.” Proc. Vldb Endow. 14 (2020): 228–40. https://doi.org/10.14778/3425879.3425892.
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  - Miao, Z., T. Chen, A. Bendeck, K. Day, S. Roy, and J. Yang. “I-Rex: An Interactive Relational Query Explainer for SQL.” Proceedings of the Vldb Endowment 13, no. 12 (January 1, 2020): 2997–3000. https://doi.org/10.14778/3415478.3415528.
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  - Morucci, Marco, Vittorio Orlandi, Cynthia Rudin, Sudeepa Roy, and Alexander Volfovsky. “Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation.” Conference on Uncertainty in Artificial Intelligence (Uai 2020) 124 (2020): 1089–98.
    Open Access Copy Link to Item
  - Miao, Zhengjie, Sudeepa Roy, and Jun Yang. “Explaining Wrong Queries Using Small Examples.” Proceedings. Acm Sigmod International Conference on Management of Data 2019 (June 2019): 503–20. https://doi.org/10.1145/3299869.3319866.
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  - Dieng, Awa, Yameng Liu, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Interpretable Almost-Exact Matching for Causal Inference.” Proceedings of Machine Learning Research 89 (April 2019): 2445–53.
  - Usaid Awan, M., Y. Liu, M. Morucci, S. Roy, C. Rudin, and A. Volfovsky. “Interpretable almost-matching-exactly with instrumental variables.” 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019, January 1, 2019.
  - Liu, Yameng, Aw Dieng, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Interpretable Almost Matching Exactly for Causal Inference,” June 18, 2018.
    Link to Item
  - Livshits, Ester, Benny Kimelfeld, and Sudeepa Roy. “Computing Optimal Repairs for Functional Dependencies.” Proceedings of the ... Acm Sigact Sigmod Sigart Symposium on Principles of Database Systems. Acm Sigact Sigmod Sigart Symposium on Principles of Database Systems 2018 (June 2018): 225–37. https://doi.org/10.1145/3196959.3196980.
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  - Yang, Jun, Pankaj K. Agarwal, Sudeepa Roy, Brett Walenz, You Wu, Cong Yu, and Chengkai Li. “Query Perturbation Analysis: An Adventure of Database Researchers in Fact-Checking.” Ieee Data Eng. Bull. 41 (2018): 28–42.
  - Koutris, P., T. Milo, S. Roy, and D. Suciu. “Answering Conjunctive Queries with Inequalities.” Theory of Computing Systems 61, no. 1 (July 1, 2017): 2–30. https://doi.org/10.1007/s00224-016-9684-2.
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  - Davidson, S., S. Khanna, T. Milo, and S. Roy. “Top-k and clustering with noisy comparisons.” Acm Transactions on Database Systems 39, no. 4 (December 30, 2014). https://doi.org/10.1145/2684066.
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  - Meenakshi, B., A. Bhatnagar, and S. Roy. “Tool for translating simulink models into input language of a model checker.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4260 LNCS (January 1, 2006): 606–20. https://doi.org/10.1007/11901433_33.
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- Book Sections
  - Roy, S., L. Orr, and D. Suciu. “Explaining query answers with explanation-ready databases.” In Proceedings of the VLDB Endowment, 9:348–59, 2016.
- Other Articles
  - Panigrahi, Debmalya, Shweta Patwa, and Sudeepa Roy. “Generalized Deletion Propagation on Counting Conjunctive Query Answers.” Corr, 2019.
  - Davidson, S., S. Khanna, S. Roy, J. Stoyanovich, V. Tannen, and Y. Chen. “On Provenance and Privacy.” International Conference on Database Theory (Icdt), 2011.
  - Davidson, S., Z. Bao, and S. Roy. “Hiding Data and Structure in Work ow Provenance.” International Workshop on Databases in Networked Information Systems (Dnis), 2011.
  - Davidson, S., S. Khanna, S. Roy, and S. C. Boulakia. “Privacy Issues in Scientific Work ow Provenance.” International Workshop on Work Ow Approaches to New Data Centric Science (Wands), 2010.
  - Davidson, Susan B., Sanjeev Khanna, Debmalya Panigrahi, and Sudeepa Roy. “Preserving Module Privacy in Workflow Provenance.” Corr, 2010.
- Conference Papers
  - Galhotra, S., A. Gilad, S. Roy, and B. Salimi. “HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach.” In Proceedings of the Acm Sigmod International Conference on Management of Data, 1598–1611, 2022. https://doi.org/10.1145/3514221.3526149.
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  - Gilad, A., Z. Miao, S. Roy, and J. Yang. “Understanding Queries by Conditional Instances.” In Proceedings of the Acm Sigmod International Conference on Management of Data, 355–68, 2022. https://doi.org/10.1145/3514221.3517898.
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  - Hu, X., Y. Liu, H. Xiu, P. K. Agarwal, D. Panigrahi, S. Roy, and J. Yang. “Selectivity Functions of Range Queries are Learnable.” In Proceedings of the Acm Sigmod International Conference on Management of Data, 959–72, 2022. https://doi.org/10.1145/3514221.3517896.
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  - Li, C., J. Lee, Z. Miao, B. Glavic, and S. Roy. “CaJaDE: Explaining Query Results by Augmenting Provenance with Context.” In Proceedings of the Vldb Endowment, 15:3594–97, 2022. https://doi.org/10.14778/3554821.3554852.
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  - Roy, S. “Toward Interpretable and Actionable Data Analysis with Explanations and Causality.” In Proceedings of the Vldb Endowment, 15:3812–20, 2022. https://doi.org/10.14778/3554821.3554902.
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  - Tao, Y., A. Gilad, A. Machanavajjhala, and S. Roy. “DPXPlain: Privately Explaining Aggregate Query Answers.” In Proceedings of the Vldb Endowment, 16:113–26, 2022. https://doi.org/10.14778/3561261.3561271.
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  - Li, C., Z. Miao, Q. Zeng, B. Glavic, and S. Roy. “Putting Things into Context: Rich Explanations for Query Answers using Join Graphs.” In Proceedings of the Acm Sigmod International Conference on Management of Data, 1051–63, 2021. https://doi.org/10.1145/3448016.3459246.
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  - Livshits, E., R. Kochirgan, S. Tsur, I. F. Ilyas, B. Kimelfeld, and S. Roy. “Properties of Inconsistency Measures for Databases.” In Proceedings of the Acm Sigmod International Conference on Management of Data, 1182–94, 2021. https://doi.org/10.1145/3448016.3457310.
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  - Gilad, A., D. Deutch, and S. Roy. “On Multiple Semantics for Declarative Database Repairs.” In Proceedings of the Acm Sigmod International Conference on Management of Data, 817–31, 2020. https://doi.org/10.1145/3318464.3389721.
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  - Salimi, B., H. Parikh, M. Kayali, L. Getoor, S. Roy, and D. Suciu. “Causal Relational Learning.” In Proceedings of the Acm Sigmod International Conference on Management of Data, 241–56, 2020. https://doi.org/10.1145/3318464.3389759.
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  - Awan, M Usaid, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference.” In Aistats, edited by Silvia Chiappa and Roberto Calandra, 108:3252–62. PMLR, 2020.
  - Morucci, Marco, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation.” In Uai, edited by Ryan P. Adams and Vibhav Gogate, 124:1089–98. AUAI Press, 2020.
  - Walenz, B., S. Sintos, S. Roy, and J. Yang. “Learning to sample: Counting with complex queries.” In Proceedings of the Vldb Endowment, 13:389–401, 2020. https://doi.org/10.14778/3368289.3368302.
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  - Kalmegh, Prajakta, Shivnath Babu, and Sudeepa Roy. “iQCAR: inter-Query Contention Analyzer for Data Analytics Frameworks.” In Proceedings. Acm Sigmod International Conference on Management of Data, 2019:918–35, 2019. https://doi.org/10.1145/3299869.3319904.
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  - Miao, Zhengjie, Qitian Zeng, Boris Glavic, and Sudeepa Roy. “Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances.” In Proceedings. Acm Sigmod International Conference on Management of Data, 2019:485–502, 2019. https://doi.org/10.1145/3299869.3300066.
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  - Miao, Zhengjie, Sudeepa Roy, and Jun Yang. “RATest: Explaining Wrong Relational Queries Using Small Examples.” In Proceedings. Acm Sigmod International Conference on Management of Data, 2019:1961–64, 2019. https://doi.org/10.1145/3299869.3320236.
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  - Usaid Awan, M., Y. Liu, M. Morucci, S. Roy, C. Rudin, and A. Volfovsky. “Interpretable almost-matching-exactly with instrumental variables.” In 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019, 2019.
  - Kalmegh, Prajakta, Shivnath Babu, and Sudeepa Roy. “iQCAR.” In Proceedings of the Acm Symposium on Cloud Computing. ACM, 2018. https://doi.org/10.1145/3267809.3275473.
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  - Kalmegh, Prajakta, Harrison Lundberg, Frederick Xu, Shivnath Babu, and Sudeepa Roy. “iQCAR: A Demonstration of an Inter-Query Contention Analyzer for Cluster Computing Frameworks.” In Proceedings. Acm Sigmod International Conference on Management of Data, 2018:1721–24, 2018. https://doi.org/10.1145/3183713.3193567.
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  - Wen, Yuhao, Xiaodan Zhu, Sudeepa Roy, and Jun Yang. “QAGView: Interactively Summarizing High-Valued Aggregate Query Answers.” In Proceedings. Acm Sigmod International Conference on Management of Data, 2018:1709–12, 2018. https://doi.org/10.1145/3183713.3193566.
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  - Miao, Z., A. Lee, and S. Roy. “LensXPlain: Visualizing and explaining contributing subsets for aggregate query answers.” In Proceedings of the Vldb Endowment, 12:1898–1901, 2018. https://doi.org/10.14778/3352063.3352094.
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  - Miao, Z., Q. Zeng, C. Li, B. Glavic, O. Kennedy, and S. Roy. “CAPE: Explaining outliers by counterbalancing.” In Proceedings of the Vldb Endowment, 12:1806–9, 2018. https://doi.org/10.14778/3352063.3352071.
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  - Rekatsinas, T., S. Roy, M. Vartak, C. Zhang, and N. Polyzotis. “Opportunities for data management research in the era of horizontal AI/ML.” In Proceedings of the Vldb Endowment, 12:2323–24, 2018. https://doi.org/10.14778/3352063.3352149.
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  - Wen, Y., X. Zhu, S. Roy, and J. Yang. “Interactive summarization and exploration of top aggregate query answers.” In Proceedings of the Vldb Endowment, 11:2196–2208, 2018. https://doi.org/10.14778/3275366.3275369.
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  - Walenz, B., S. Roy, and J. Yang. “Optimizing iceberg queries with complex joins.” In Proceedings of the Acm Sigmod International Conference on Management of Data, Part F127746:1243–44, 2017. https://doi.org/10.1145/3035918.3064053.
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  - Beame, P., J. Li, S. Roy, and D. Suciu. “Exact model counting of query expressions: Limitations of propositional methods.” In Acm Transactions on Database Systems, Vol. 42, 2017. https://doi.org/10.1145/2984632.
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  - Koutris, P., T. Milo, S. Roy, and D. Suciu. “Answering Conjunctive Queries with Inequalities,” 2015.
  - Beame, P., J. Li, S. Roy, and D. Suciu. “Model Counting of Query Expressions: Limitations of Propositional Methods,” 2014.
  - Deutch, D., T. Milo, S. Roy, and V. Tannen. “Circuits for Datalog Provenance,” 2014.
  - Roy, S., and D. Suciu. “A Formal Approach to Finding Explanations for Database Queries,” 2014.
  - Meliou, A., S. Roy, and D. Suciu. “Causality and Explanations in Databases,” 2014.
  - Davidson, S. B., S. Khanna, T. Milo, and S. Roy. “Using the Crowd for Top-k and Group-by Queries,” 2013.
  - Davidson, S. B., T. Milo, and S. Roy. “A Propagation Model for Provenance Views of Public/Private Workflows,” 2013.
  - Roy, S., L. Chiticariu, V. Feldman, F. R. Reiss, and H. Zhu. “Provenance-based Dictionary Renement in Information Extraction,” 2013.
  - Beame, P., J. Li, S. Roy, and D. Suciu. “Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases,” 2013.
  - Davidson, S. B., S. Khanna, T. Milo, D. Panigrahi, and S. Roy. “Provenance views for module privacy.” In Proceedings of the Acm Sigact Sigmod Sigart Symposium on Principles of Database Systems, 175–86, 2011. https://doi.org/10.1145/1989284.1989305.
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  - Davidson, S. B., S. Khanna, T. Milo, D. Panigrahi, and S. Roy. “Provenance Views for Module Privacy,” 2011.
  - Roy, S., V. Perduca, and V. Tannen. “Faster Query Answering in Probabilistic Databases using Read-Once Functions,” 2011.
  - Davidson, S., S. Khanna, S. Roy, J. Stoyanovich, V. Tannen, Y. Chen, and T. Milo. “Enabling Privacy in Provenance-Aware Work ow Systems,” 2011.
  - Khanna, S., S. Roy, and V. Tannen. “Queries with Difference on Probabilistic Databases,” 2011.
  - Bao, Z., S. Davidson, S. Khanna, and S. Roy. “An Optimal Labeling Scheme for Work ow Provenance using Skeleton Labels,” 2010.
  - Biton, O., S. Davidfson, S. Khanna, and S. Roy. “Optimizing User Views for Workflows,” 2009.
  - Kannan, S., S. Khanna, and S. Roy. “STCON in Directed Unique-Path Graphs,” 2008.
  - Meenaksji, B., A. Bhatnagar, and S. Roy. “Automatic Translation of Simulink Models into Input Language of a Model Checker,” 2006.
  - Aarons, G., T. Abe, J. Abernathy, M. Ablikim, H. Abramowicz, D. Adey, C. Adloff, et al. “Detector concepts.” In Lcws 2005 2005 International Linear Collider Workshop, 2005.
Teaching & Mentoring
Recent Courses
- COMPSCI 394: Research Independent Study 2023
- COMPSCI 590: Advanced Topics in Computer Science 2023
- COMPSCI 316D: Introduction to Database Systems 2022
- COMPSCI 391: Independent Study 2022
- COMPSCI 516: Database Systems 2022
- COMPSCI 391: Independent Study 2021
Advising & Mentoring
Available to mentor:
- PhD
- Undergraduate

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Education, Training, & Certifications

Previous Appointments & Affiliations

Awards & Honors

External Relationships

Selected Publications

Academic Articles

Book Sections

Other Articles

Conference Papers

Recent Courses

Advising & Mentoring

Available to mentor: