Publications

Selected publications grouped by research themes. For the complete indexed list, see Google Scholar and DBLP.

Google Scholar DBLP
Agents & LLMs Data Discovery Responsible AI Causality & Explanations Entity Resolution & Integration Algorithms & Theory

Agents, LLMs, and Post-Training

Post-training, reinforcement learning, fine-tuning data selection, and agent reliability.

  1. Ning Wang, Zhengxin Zhang, Maosen Tang, Yitang Gao, Claire Cardie, Sainyam Galhotra. HARP: Efficient Data Selection for Finetuning Large Language Models. arXiv preprint. 2026. [PDF] [Details]
  2. Zhengxin Zhang, Ning Wang, Sainyam Galhotra, Claire Cardie. How Far Are We From True Auto-Research?. arXiv preprint. 2026. [PDF] [Details]
  3. Ning Wang, Kuanyan Zhu, Daniel Yuehwoon Yee, Yitang Gao, Shiying Huang, Zirun Xu, Sainyam Galhotra. Pruning Minimal Reasoning Graphs for Efficient Retrieval-Augmented Generation. arXiv preprint. 2026. [PDF] [Details]
  4. Anna Mazhar, Huzaifa Suri, Sainyam Galhotra. Trace-Level Analysis of Information Contamination in Multi-Agent Systems. Proceedings of the ACM Conference on AI and Agentic Systems, 483-496. 2026. [PDF] [Details]
  5. V. Katharki, Sainyam Galhotra. Goal-Oriented Reliability and Self-Improvement for Multi-Agent Systems. Proceedings of the ACM Conference on AI and Agentic Systems, 1367-1371. 2026. [Details]
  6. Ning Wang, Sainyam Galhotra. QJoin: Transformation-aware Joinable Data Discovery Using Reinforcement Learning. arXiv preprint. 2025. [PDF] [Details]
  7. Shubham Mohole, Sainyam Galhotra. SIFOTL: A Principled, Statistically-Informed Fidelity-Optimization Method for Tabular Learning. arXiv preprint. 2025. [PDF] [Details]
  8. Shubham Mohole, Hongjun Choi, Shusen Liu, Christine Klymko, Shashank Kushwaha, Derek Shi, Wesam Sakla, Sainyam Galhotra, Ruben Glatt. VERIRAG: A Post-Retrieval Auditing of Scientific Study Summaries. arXiv preprint. 2025. [PDF] [Details]
  9. Shubham Mohole, Sainyam Galhotra. FAIR-SHEPHERD: An Interactive System for Auditable Fairness via Population-Grounded Surrogates. 2025 IEEE International Conference on Data Mining Workshops (ICDMW), 2610-2613. 2025. [Details]
  10. Shubham Mohole, Hongjun Choi, Shusen Liu, Shashank Kushwaha, Derek Shi, Sainyam Galhotra, Ruben Glatt. VERIRAG: Healthcare Claim Verification via Statistical Audit. Lawrence Livermore National Laboratory (LLNL), Livermore, CA, United States. 2025. [Details]
  11. Shubham Mohole, Hongjun Choi, Shusen Liu, Christine Klymko, Shashank Kushwaha, Derek Shi, Wesam Sakla, Sainyam Galhotra, Ruben Glatt. VERIRAG: Healthcare Claim Verification via Statistical Audit in Retrieval-Augmented Generation. Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, 2025. 2025. [Details]

Data Discovery and Data Lakes

Finding, joining, retrieving, and explaining data for downstream tasks.

  1. Wen-Zhi Li, Sainyam Galhotra. Octopus: A Lightweight Entity-Aware System for Multi-Table Data Discovery and Cell-Level Retrieval. arXiv preprint. 2026. [PDF] [Details]
  2. Ning Wang, Sainyam Galhotra. QJoin: Transformation-aware Joinable Data Discovery Using Reinforcement Learning. arXiv preprint. 2025. [PDF] [Details]
  3. Andrew Kang, Yashnil Saha, Sainyam Galhotra. Towards General-Purpose Data Discovery: A Programming Languages Approach. arXiv preprint. 2025. [PDF] [Details]
  4. Ziawasch Abedjan, Mahdi Esmailoghli, Sainyam Galhotra. Data Discovery in Data Lakes: Operations, Indexes, Systems. Proceedings of the VLDB Endowment, 18(12), 5455-5459. 2025. [Details]
  5. Yue Gong, Sainyam Galhotra, Raul Castro Fernandez. Nexus: Correlation Discovery over Collections of Spatio-Temporal Tabular Data. 2024 International Conference on Management of Data, SIGMOD/PODS 2024, Santiago AA, Chile, June 9-15, 2024. 2024. [DOI] [Details]
  6. Kevin Dharmawan, Chirag A. Kawediya, Yue Gong, Zaki Indra Yudhistira, Zhiru Zhu, Sainyam Galhotra, Adila Alfa Krisnadhi, Raul Castro Fernandez. Demonstration of Ver: View Discovery in the Wild. Companion of the 2024 International Conference on Management of Data, SIGMOD/PODS 2024, Santiago AA, Chile, June 9-15, 2024. 2024. [DOI] [Details]
  7. Sainyam Galhotra, Yue Gong, Raul Castro Fernandez. Metam: Goal-Oriented Data Discovery. 39th IEEE International Conference on Data Engineering, ICDE 2023, Anaheim, CA, USA, April 3-7, 2023*. 2023. [DOI] [Details]
  8. Yue Gong, Zhiru Zhu, Sainyam Galhotra, Raul Castro Fernandez. Ver: View Discovery in the Wild. 39th IEEE International Conference on Data Engineering, ICDE 2023, Anaheim, CA, USA, April 3-7, 2023. 2023. [DOI] [Details]
  9. Sainyam Galhotra, Udayan Khurana. Automated Relational Data Explanation using External Semantic Knowledge. VLDB. 2022. [PDF] [Details]
  10. Sainyam Galhotra, Udayan Khurana. Semantic Search over Structured Data. CIKM ‘20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020. 2020. [PDF] [DOI] [Details]

Responsible AI, Fairness, and Unlearning

Auditing models and systems for fairness, robustness, forgetting, and reliability.

  1. Anna Mazhar, Sainyam Galhotra. Towards Reliable Testing of Machine Unlearning. arXiv preprint. 2026. [PDF] [Details]
  2. Eric Enouen, Sainyam Galhotra. Debugging Concept Bottleneck Models through Removal and Retraining. International Conference on Learning Representations (ICLR), 2026. 2026. [PDF] [Details]
  3. Samyak Jain, Ronak Kalvani, Sainyam Galhotra, Sayan Ranu. Is Graph Unlearning Ready for Practice? A Benchmark on Efficiency, Utility, and Forgetting. International Conference on Learning Representations (ICLR), 2026. 2026. [Details]
  4. Anna Mazhar, Sainyam Galhotra. Causal Fuzzing for Verifying Machine Unlearning. arXiv preprint. 2025. [PDF] [Details]
  5. Zeyu Song, Sainyam Galhotra, Shagufta Mehnaz. Benchmarking Robust Aggregation in Decentralized Gradient Marketplaces. arXiv preprint. 2025. [PDF] [Details]
  6. Shubham Mohole, Sainyam Galhotra. FAIR-SHEPHERD: An Interactive System for Auditable Fairness via Population-Grounded Surrogates. 2025 IEEE International Conference on Data Mining Workshops (ICDMW), 2610-2613. 2025. [Details]
  7. Yash Kurkure, Miles Shamo, Joseph Wiseman, Sainyam Galhotra, Stavros Sintos. Faster Algorithms for Fair Max-Min Diversification in R(^mboxd). Proc. ACM Manag. Data. 2024. [DOI] [Details]
  8. Sainyam Galhotra, Joseph Y. Halpern. Intervention and Conditioning in Causal Bayesian Networks. Neurips 2024. 2024. [DOI] [Details]
  9. Jiongli Zhu, Sainyam Galhotra, Nazanin Sabri, Babak Salimi. Consistent Range Approximation for Fair Predictive Modeling. PVLDB. 2023. [PDF] [DOI] [Details]
  10. Jiongli Zhu, Sainyam Galhotra, Nazanin Sabri, Babak Salimi. Consistent Range Approximation for Fair Predictive Modeling. Proc. VLDB Endow.. 2023. [PDF] [DOI] [Details]
  11. Suman K. Bera, Syamantak Das, Sainyam Galhotra, Sagar Sudhir Kale. Fair k-Center Clustering in MapReduce and Streaming Settings. WWW ‘22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022. 2022. [PDF] [DOI] [Details]
  12. Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney. Causal Feature Selection for Algorithmic Fairness. SIGMOD ‘22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022. 2022. [PDF] [DOI] [Details]
  13. Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney. Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. Entropy. 2021. [PDF] [DOI] [Details]
  14. Sainyam Galhotra, Sandhya Saisubramanian, Shlomo Zilberstein. Learning to Generate Fair Clusters from Demonstrations. AIES ‘21: AAAI/ACM Conference on AI, Ethics, and Society, Virtual Event, USA, May 19-21, 2021. 2021. [PDF] [DOI] [Details]
  15. Sainyam Galhotra, Yuriy Brun, Alexandra Meliou. 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. 2017. [PDF] [DOI] [Details]

Causality and Explanations

Causal reasoning, counterfactuals, explainability, and what-if analysis.

  1. Anna Mazhar, Sainyam Galhotra. Causal Fuzzing for Verifying Machine Unlearning. arXiv preprint. 2025. [PDF] [Details]
  2. Sainyam Galhotra, Joseph Y. Halpern. Intervention and Conditioning in Causal Bayesian Networks. Neurips 2024. 2024. [DOI] [Details]
  3. Fangzhu Shen, Kayvon Heravi, Oscar Gomez, Sainyam Galhotra, Amir Gilad, Sudeepa Roy, Babak Salimi. Causal What-If and How-To Analysis Using HypeR. 39th IEEE International Conference on Data Engineering, ICDE 2023, Anaheim, CA, USA, April 3-7, 2023. 2023. [DOI] [Details]
  4. Sainyam Galhotra, Amir Gilad, Sudeepa Roy, Babak Salimi. 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. 2022. [PDF] [DOI] [Details]
  5. Romila Pradhan, Aditya Lahiri, Sainyam Galhotra, Babak Salimi. 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. 2022. [PDF] [DOI] [Details]
  6. Romila Pradhan, Aditya Lahiri, Sainyam Galhotra, Babak Salimi. Explainable AI: Foundations, Applications, Opportunities for Data Management Research. SIGMOD ‘22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022. 2022. [PDF] [DOI] [Details]
  7. Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney. Causal Feature Selection for Algorithmic Fairness. SIGMOD ‘22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022. 2022. [PDF] [DOI] [Details]
  8. Sainyam Galhotra, Romila Pradhan, Babak Salimi. Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals. SIGMOD ‘21: International Conference on Management of Data, Virtual Event, China, June 20-25, 2021. 2021. [PDF] [DOI] [Details]
  9. Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney. Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. Entropy. 2021. [PDF] [DOI] [Details]

Entity Resolution and Data Integration

Entity resolution, blocking, view discovery, taxonomies, and integration systems.

  1. Wen-Zhi Li, Sainyam Galhotra. Octopus: A Lightweight Entity-Aware System for Multi-Table Data Discovery and Cell-Level Retrieval. arXiv preprint. 2026. [PDF] [Details]
  2. Yue Gong, Sainyam Galhotra, Raul Castro Fernandez. Nexus: Correlation Discovery over Collections of Spatio-Temporal Tabular Data. 2024 International Conference on Management of Data, SIGMOD/PODS 2024, Santiago AA, Chile, June 9-15, 2024. 2024. [DOI] [Details]
  3. Kevin Dharmawan, Chirag A. Kawediya, Yue Gong, Zaki Indra Yudhistira, Zhiru Zhu, Sainyam Galhotra, Adila Alfa Krisnadhi, Raul Castro Fernandez. Demonstration of Ver: View Discovery in the Wild. Companion of the 2024 International Conference on Management of Data, SIGMOD/PODS 2024, Santiago AA, Chile, June 9-15, 2024. 2024. [DOI] [Details]
  4. Donatella Firmani, Sainyam Galhotra, Barna Saha, Divesh Srivastava. Building Taxonomies with Triplet Queries. Proceedings of the 32nd Symposium of Advanced Database Systems, Villasimius, Italy, June 23rd to 26th, 2024. 2024. [PDF] [Details]
  5. Yue Gong, Zhiru Zhu, Sainyam Galhotra, Raul Castro Fernandez. Ver: View Discovery in the Wild. 39th IEEE International Conference on Data Engineering, ICDE 2023, Anaheim, CA, USA, April 3-7, 2023. 2023. [DOI] [Details]
  6. Sainyam Galhotra, Donatella Firmani, Barna Saha, Divesh Srivastava. Hierarchical Entity Resolution using an Oracle. SIGMOD ‘22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022. 2022. [PDF] [DOI] [Details]
  7. Sainyam Galhotra, Donatella Firmani, Barna Saha, Divesh Srivastava. BEER: Blocking for Effective Entity Resolution. SIGMOD ‘21: International Conference on Management of Data, Virtual Event, China, June 20-25, 2021. 2021. [PDF] [DOI] [Details]
  8. Sainyam Galhotra, Donatella Firmani, Barna Saha, Divesh Srivastava. Efficient and effective ER with progressive blocking. VLDB J.. 2021. [PDF] [DOI] [Details]
  9. Sainyam Galhotra. 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. 2020. [PDF] [Details]
  10. Sainyam Galhotra, Donatella Firmani, Barna Saha, Divesh Srivastava. 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. 2019. [PDF] [Details]
  11. Sainyam Galhotra, Donatella Firmani, Barna Saha, Divesh Srivastava. 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. 2018. [PDF] [DOI] [Details]
  12. Donatella Firmani, Sainyam Galhotra, Barna Saha, Divesh Srivastava. Robust Entity Resolution Using a CrowdOracle. IEEE Data Eng. Bull.. 2018. [PDF] [Details]

Algorithms and Theory

Clustering, graph models, approximation, streaming, and algorithmic foundations.

  1. Rahul Raychaudhury, Aryan Esmailpour, Sainyam Galhotra, Stavros Sintos. Metric $k$-clustering using only Weak Comparison Oracles. International Conference on Learning Representations (ICLR), 2026. 2026. [PDF] [Details]
  2. Yash Kurkure, Miles Shamo, Joseph Wiseman, Sainyam Galhotra, Stavros Sintos. Faster Algorithms for Fair Max-Min Diversification in R(^mboxd). Proc. ACM Manag. Data. 2024. [DOI] [Details]
  3. Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha. Community Recovery in the Geometric Block Model. J. Mach. Learn. Res.. 2023. [Details]
  4. Suman K. Bera, Syamantak Das, Sainyam Galhotra, Sagar Sudhir Kale. Fair k-Center Clustering in MapReduce and Streaming Settings. WWW ‘22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022. 2022. [PDF] [DOI] [Details]
  5. Raghavendra Addanki, Sainyam Galhotra, Barna Saha. How to Design Robust Algorithms using Noisy Comparison Oracle. Proc. VLDB Endow.. 2021. [PDF] [DOI] [Details]
  6. Sandhya Saisubramanian, Sainyam Galhotra, Shlomo Zilberstein. 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. 2020. [PDF] [DOI] [Details]
  7. Sainyam Galhotra. 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. 2020. [PDF] [Details]
  8. Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha. 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. 2019. [PDF] [DOI] [Details]
  9. Akhil Arora, Sainyam Galhotra, Sayan Ranu. 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. 2019. [PDF] [DOI] [Details]
  10. Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha. 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. 2018. [PDF] [Details]
  11. Sainyam Galhotra, Soumyabrata Pal, Arya Mazumdar, Barna Saha. The Geometric Block Model and Applications. 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018, Monticello, IL, USA, October 2-5, 2018. 2018. [PDF] [DOI] [Details]
  12. Akhil Arora, Sainyam Galhotra, Sayan Ranu. 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. 2017. [PDF] [DOI] [Details]
  13. Sainyam Galhotra, Akhil Arora, Shourya Roy. 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. 2016. [PDF] [DOI] [Details]
  14. Sainyam Galhotra, Akhil Arora, Srinivas Virinchi, Shourya Roy. 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. 2015. [PDF] [DOI] [Details]
  15. Sainyam Galhotra, Amitabha Bagchi, Srikanta Bedathur, Maya Ramanath, Vidit Jain. Tracking the Conductance of Rapidly Evolving Topic-Subgraphs. Proc. VLDB Endow.. 2015. [PDF] [DOI] [Details]
  16. Vidit Jain, Sainyam Galhotra. 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. 2014. [PDF] [Details]