Sainyam Galhotra

Sainyam Galhotra

Postdoctoral Scholar

The University of Chicago

Sainyam Galhotra

I am a Computing Innovation Fellow pursuing postdoctoral research at the University of Chicago under the mentorship of Raul Castro Fernandez. The goal of my research is to develop data discovery and integration tools for effective and responsible analytics. My work has leveraged techniques from causal inference, data management, theoretical computer science, crowdsourcing and HCI to understand various aspects of trustworthy system design including robustness, explainability, and fairness. I received my Ph.D. from University of Massachusetts Amherst under the supervision of Barna Saha. I completed my undergraduate studies from Indian Institute of Technology Delhi (IIT Delhi) in May, 2014 under the guidance of Prof. Amitabha Bagchi. Prior to joining UMass, I worked as a budding scientist at Xerox Research Centre India, Bangalore for a year.

I am in the academic job market. Please reach out to me ([email protected] or [email protected]) if you are hiring.

Download my resumé.

Interests
  • Data Management
  • Responsible Data Science
Education
  • PhD, 2021

    University of Massachusetts Amherst

  • MS, 2017

    University of Massachusetts Amherst

  • BTech, 2014

    Indian Institute of Technology Delhi

Experience

 
 
 
 
 
Postdoctoral Scholar
University of Chicago
May 2021 – Present Chicago

Accomplish­ments

Selected as a Rising Star in Data Science at the Data Science Institute, UChicago
DAAD AInet Fellow
ACM SIGMOD Entity Resolution Programming Contest – Top 5 finalist
Most reproducible paper award in SIGMOD 2018 and 2019
First recipient of Krithi Ramamritham Computer Science Scholarship
Best paper award in SIGSOFT FSE 2017

Recent Publications

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(2023). Ver: View Discovery in the Wild. ICDE.

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(2022). Explainable AI: Foundations, Applications, Opportunities for Data Management Research. SIGMOD ‘22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022.

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(2022). Causal Feature Selection for Algorithmic Fairness. SIGMOD ‘22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022.

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(2022). DataPrism: Exposing Disconnect between Data and Systems. SIGMOD ‘22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022.

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(2022). Explainable AI: Foundations, Applications, Opportunities for Data Management Research. 38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, May 9-12, 2022.

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(2022). Fair k-Center Clustering in MapReduce and Streaming Settings. WWW ‘22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022.

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(2022). Hierarchical Entity Resolution using an Oracle. SIGMOD ‘22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022.

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(2022). HypeR: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach. SIGMOD ‘22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022.

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(2021). BEER: Blocking for Effective Entity Resolution. SIGMOD ‘21: International Conference on Management of Data, Virtual Event, China, June 20-25, 2021.

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(2021). Adaptive Rule Discovery for Labeling Text Data. SIGMOD ‘21: International Conference on Management of Data, Virtual Event, China, June 20-25, 2021.

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(2021). Demonstration of Generating Explanations for Black-Box Algorithms Using Lewis. VLDB.

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(2021). Efficient and effective ER with progressive blocking. VLDB J..

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(2021). Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals. SIGMOD ‘21: International Conference on Management of Data, Virtual Event, China, June 20-25, 2021.

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(2021). How to Design Robust Algorithms using Noisy Comparison Oracle. Proc. VLDB Endow..

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(2021). Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. Entropy.

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(2021). Learning to Generate Fair Clusters from Demonstrations. AIES ‘21: AAAI/ACM Conference on AI, Ethics, and Society, Virtual Event, USA, May 19-21, 2021.

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(2021). Semantic Concept Annotation for Tabular Data. CIKM ‘21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021.

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(2020). Balancing the Tradeoff Between Clustering Value and Interpretability. AIES ‘20: AAAI/ACM Conference on AI, Ethics, and Society, New York, NY, USA, February 7-8, 2020.

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(2020). Reliable Clustering with Applications to Data Integration. Proceedings of the VLDB 2020 PhD Workshop co-located with the 46th International Conference on Very Large Databases (VLDB 2020), ONLINE, August 31 - September 4, 2020.

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(2020). Semantic Search over Structured Data. CIKM ‘20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020.

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(2019). Connectivity of Random Annulus Graphs and the Geometric Block Model. Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 2019, September 20-22, 2019, Massachusetts Institute of Technology, Cambridge, MA, USA.

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(2019). Crowd-Sourced Entity Resolution with Control Queries. Proceedings of the 27th Italian Symposium on Advanced Database Systems, Castiglione della Pescaia (Grosseto), Italy, June 16-19, 2019.

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(2019). Influence Maximization Revisited: The State of the Art and the Gaps that Remain. Advances in Database Technology - 22nd International Conference on Extending Database Technology, EDBT 2019, Lisbon, Portugal, March 26-29, 2019.

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(2019). Automated Feature Enhancement for Predictive Modeling using External Knowledge. 2019 International Conference on Data Mining Workshops, ICDM Workshops 2019, Beijing, China, November 8-11, 2019.

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(2018). The Geometric Block Model. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018.

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(2018). Robust Entity Resolution using Random Graphs. Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018, Houston, TX, USA, June 10-15, 2018.

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(2018). The Geometric Block Model and Applications. 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018, Monticello, IL, USA, October 2-5, 2018.

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(2018). Robust Entity Resolution Using a CrowdOracle. IEEE Data Eng. Bull..

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(2017). Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study. Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD Conference 2017, Chicago, IL, USA, May 14-19, 2017.

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(2017). Fairness testing: testing software for discrimination. Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2017, Paderborn, Germany, September 4-8, 2017.

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(2016). QA(^mboxRT): A System for Real-Time Holistic Quality Assurance for Contact Center Dialogues. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA.

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(2016). Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models. Proceedings of the 2016 International Conference on Management of Data, SIGMOD Conference 2016, San Francisco, CA, USA, June 26 - July 01, 2016.

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(2015). ASIM: A Scalable Algorithm for Influence Maximization under the Independent Cascade Model. Proceedings of the 24th International Conference on World Wide Web Companion, WWW 2015, Florence, Italy, May 18-22, 2015 - Companion Volume.

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(2015). STAR: Real-time Spatio-Temporal Analysis and Prediction of Traffic Insights using Social Media. Companion Volume to the Proceedings of the 2nd IKDD Conference on Data Sciences, CODS 2015 Companion Volume, Bangalore, India, March 20, 2015.

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(2015). Tracking the Conductance of Rapidly Evolving Topic-Subgraphs. Proc. VLDB Endow..

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(2015). Optimal Radius for Connectivity in Duty-Cycled Wireless Sensor Networks. ACM Trans. Sens. Networks.

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(2014). Min-d-Occur: Ensuring Future Occurrences in Streaming Sets. Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, UAI 2014, Quebec City, Quebec, Canada, July 23-27, 2014.

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(2014). Optimal Radius for Connectivity in Duty-Cycled Wireless Sensor Networks. CoRR.

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(2013). Optimal radius for connectivity in duty-cycled wireless sensor networks. 16th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM ‘13, Barcelona, Spain, November 3-8, 2013.

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