Amir Gilad

Assistant Professor

The Hebrew University

About

I am a Scharf-Ullman endowed Assistant Professor (Senior Lecturer) at the Hebrew University of Jerusalem’s School of Computer Science and Engineering.

My research focuses on responsible data science including uses of causal inference in data analysis, differential privacy, fairness and representation aspects in data, and the development of tools and algorithms that aim to assist users in understanding and gaining insights into data.

I am a recipient of the 2017 VLDB Best Paper Award, the 2018 SIGMOD Research Highlight Award, the 2019 Google Ph.D. Fellowship in Structured Data and Database Management, and the 2024 Alon Scholarship for the Integration of Outstanding Faculty.

Before joining the Hebrew University, I was a postdoctoral researcher in the Database Group at Duke University, hosted by Prof. Sudeepa Roy, Prof. Ashwin Machanavajjhala, and Prof. Jun Yang. I obtained my Ph.D in Computer Science from Tel Aviv University, where I was advised by Prof. Daniel Deutch.

Interests

Data Management
Responsible Data Science
Causal Inference Applications for Data Analysis
Differential Privacy

Education

Postdoc in the Database Group
Duke University
PhD in Computer Science
Tel Aviv University
MSc in Computer Science
Tel Aviv University
BSc in Mathematics and Computer Science
Tel Aviv University

Projects

Data Quality Assessment and Repair

Develop approaches for assessing data quality and bias, and devise algorithms to repair the data.

Private and Fair Data Generation

Generate data in a Differentially Private manner that is faithful to the private data and also satisfies fairness and integrity constraints.

Causal inference usage in data analysis

Employ causal inference principles to enhance and improve data analysis capabilities.

Recent Publications

Shubhankar Mohapatra, Amir Gilad, Xi He, Benny Kimelfeld (2025). Computing Inconsistency Measures Under Differential Privacy. In SIGMOD.

PDF Link

Yuchao Tao, Amir Gilad, Sudeepa Roy, Ashwin Machanavajjhala (2025). Differentially private explanations for aggregate query answers. In VLDB J. 34(2), Best of VLDB Special Issue.

PDF Link

Yuxi Liu, Fangzhu Shen, Kushagra Ghosh, Amir Gilad, Benny Kimelfeld, Sudeepa Roy (2024). The Cost of Representation by Subset Repairs. In PVLDB 18(2), 2025.

PDF Link

Shunit Agmon, Amir Gilad, Brit Youngmann, Shahar Zoarets, Benny Kimelfeld (2024). Finding Convincing Views to Endorse a Claim. In PVLDB 18(2), 2025.

PDF Link

Shweta Patwa, Danyu Sun, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy (2024). DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms. In PVLDB 17(1), 2024.

PDF Link

Itay Elyashiv, Amir Gilad, Edna Isakov, Tal Tikochinsky, Amit Somech (2024). PD-Explain: A Unified Python-native Framework for Query Explanations Over DataFrames. In PVLDB 17(12), 2024.

PDF Link

Yihao Hu, Amir Gilad, Sudeepa Roy, Jun Ynag (2024). Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries. In SIGMOD.

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