معرفی
Amir Gilad is a Scharf-Ullman endowed Assistant Professor (Senior Lecturer) at the Hebrew University of Jerusalem’s School of Computer Science and Engineering. His research focuses on responsible data science, including causal inference, differential privacy, fairness in data, and tools for data analysis. He holds a Ph.D. in Computer Science from Tel Aviv University, where he was advised by Prof. Daniel Deutch. Prior to this, he was a postdoctoral researcher at Duke University, mentored by Prof. Sudeepa Roy, Prof. Ashwin Machanavajjhala, and Prof. Jun Yang.
- Education:
- Ph.D. in Computer Science, Tel Aviv University (Advisor: Daniel Deutch)
- MSc in Computer Science, Tel Aviv University
- BSc in Mathematics and Computer Science, Tel Aviv University
His research interests span data quality assessment, private and fair data generation, and causal inference applications. He has received notable awards, including the 2024 Alon Scholarship and the 2019 Google Ph.D. Fellowship.
- Recent Projects:
- Developing algorithms for data quality repair and assessing bias in datasets
- Generating differentially private data that satisfies fairness constraints
- Applying causal inference to enhance data analysis tools
Awards and Honors:
- 2024 Alon Scholarship for Outstanding Faculty Integration
- 2019 Google Ph.D. Fellowship in Structured Data
- 2018 SIGMOD Research Highlight Award
- 2017 VLDB Best Paper Award
Teaching: Courses include “Topics in Responsible Data Science” and “Seminar on Causal Inference in Data Analysis” at Hebrew University, and “Extended Introduction to Computer Science” at Tel Aviv University. He has also led workshops on Google Technologies.
Labs & Teams: His work is centered around the School of Computer Science and Engineering’s database group, focusing on foundational and applied aspects of privacy-aware data systems.
