Welcome to my page. I’m currently pursuing a PhD (Started in January, 2023) at the School of Computing, National University of Singapore. I am fortunate to be guided by Late Prof. Stéphane Bressan, Prof. Pierre Senellart and Prof. Tan Kian Lee, whose expertise helps shape my research in computing, data quality management, analytics etc.
I completed my M.Sc. in Big Data Analytics from Ramakrishna Mission Vivekananda Educational and Research Institute (RKMVERI) in Belur, India in 2022, and my BE in Electrical Engineering from Jadavpur University, Kolkata, India in 2018.
This site is a reflection of my research, publications, and teaching activities. I hope you find the information here useful and informative. Please feel free to reach out if you have any questions or if our work intersects.
Research
My research is centered around Data Quality and Data Uncertainty Management, with a particular emphasis on probabilistic databases. In today's data-driven world, ensuring the quality and managing the uncertainty of data are crucial for the reliability and effectiveness of data-intensive applications.
Probabilistic Databases: I explore techniques for handling uncertainty in data, which is essential for making accurate and reliable decisions in uncertain environments. Probabilistic databases provide a framework for modeling and querying uncertain data, and my work focuses on improving these systems to enhance their efficiency and applicability.
Data Quality and Machine Learning/Artificial Intelligence: I investigate the impact of data quality on machine learning and artificial intelligence. High-quality data is fundamental for training effective models, and understanding how data imperfections affect model performance is a key aspect of my research. I work on methods to ensure that the data used in AI systems is accurate, complete, and relevant.
Privacy-Preserving Machine Learning/Artificial Intelligence: Additionally, I have a keen interest in privacy-preserving techniques for machine learning and AI. As data privacy concerns grow, developing methods that protect sensitive information while still enabling meaningful analysis and model training is increasingly important. My research includes exploring approaches to achieve privacy without compromising the utility of machine learning models.
Through my research, I aim to advance the understanding and techniques related to data quality, uncertainty, and privacy, contributing to more robust and reliable data systems.
Publications
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Expected Shapley Value is Shapley Value for Expected Utility Game
ECSQARU 2025
Co-authors: Antoine Gauquier, Pierre Senellart
Paper link -
Discovering Voting Power for Ensemble Methods
DEXA 2025
Co-authors: Angelo Saadeh, Pierre Senellart, and Stéphane Bressan
Paper link -
Expected Shapley-Like Scores of Boolean Functions: Complexity and Applications to Probabilistic Databases
PODS 2024
Co-authors: Mikael Monet, Pierre Senellart, and Stéphane Bressan
Paper link | Presentation link -
Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack using Public Data
NeurIPS 2023
Co-authors: Debabrota Basu
Paper link | Presentation link
Demonstration
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Using a Probabilistic Database in an Image Retrieval Application
EDBT 2025
Co-authors: Fajrian Yunus, Pierre Senellart, Talel Abdessalem, and Stéphane Bressan
Paper link | Demonstration link
Teaching
As part of my PhD program, I have had the opportunity to assist Prof. Stéphane Bressan and Adi Yoga Sidi Prabawa with the following courses:
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Data Management and Warehousing (BT5110)
Course Details -
Database Applications Design and Tuning (CS4221/5421)
Course Details -
Maritime Data Analytics (MTM5004)
Course Details -
Database Technology and Management (IT2002)
Course Details
Awards
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Research Achievement Award in the Academic Year 2023/2024 by the School of Computing, NUS
Certificate -
Teaching Fellowship Scheme awarded by the School of Computing, NUS in 2024/2025 Semester 1
CV
Contact
You can reach me via email at: