Amir Gilad

Amir Gilad

Assistant Professor

The Hebrew University

About

I am an 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 SIGMOD 2023 Best Artifact Honorable Mention.

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.

I am recruiting M.Sc. and Ph.D. students! Please contact me if you are interested in working together.

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

Recent Publications

(2024). Summarized Causal Explanations For Aggregate Views. In SIGMOD.

PDF Link

(2023). Characterizing and Verifying Queries Via CINSGEN. In SIGMOD.

PDF Link

(2023). The Consistency of Probabilistic Databases with Independent Cells. In ICDT, 2023.

PDF Link

(2023). PreFair: Privately Generating Justifiably Fair Synthetic Data. In PVLDB 16(5), 2023.

PDF Link

(2023). DPXPlain: Privately Explaining Aggregate Query Answers. In PVLDB 16(1), 2023.

PDF Link

(2023). Causal What-If and How-To Analysis Using HYPER. In ICDE, 2023.

PDF Link

(2023). FEDEX: An Explainability Framework for Data Exploration Steps. In PVLDB 15(13), 2023.

PDF Link

Teaching

Seminar on Causal Inference in Data Analysis

Extended Introduction to Computer Science

Workshop: Google Technologies

Contact