Scholar
Edo Roth
Google Scholar ID: xUX0c90AAAAJ
Google
Citations & Impact
Citations
266
H-index
8
i10-index
7
Publications
15
Co-authors
12
list available
Contact
Resume (English only)
Academic Achievements
- Published multiple papers on differential privacy and distributed systems, including 'Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?' (2025), 'Mayfly: Private Aggregate Insights from Ephemeral Streams of On-Device User Data' (TPDP 2025), 'Releasing Large-Scale Human Mobility Histograms with Differential Privacy' (2024), 'Arboretum: A Planner for Massive-Scale Federated Analytics with Differential Privacy' (SOSP 2023), 'Mycelium: Large-Scale Distributed Graph Queries with Differential Privacy' (SOSP 2021), 'Do Not Overpay for Fault Tolerance!' (RTAS 2021), 'REBOUND: Defending Distributed Systems Against Attacks with Bounded-Time Recovery' (EuroSys 2021), 'Orchard: Differentially Private Analytics at Scale' (OSDI 2020), 'Testing Differential Privacy with Dual Interpreters' (OOPSLA 2020), 'Bounded-Time Recovery for Distributed Real-Time Systems' (RTAS 2020), 'Honeycrisp: Large-scale Differentially Private Aggregation Without a Trusted Core' (SOSP 2019), 'Fuzzi: A Three-Level Logic for Differential Privacy' (ICFP 2019).
Research Experience
- Currently works as a software engineer on a federated analytics research team at Google.
Education
- Received his PhD from the Department of Computer and Information Science at the University of Pennsylvania in 2022, advised by Andreas Haeberlen. Also spent two great summers interning at Microsoft Research.
Background
- A computer scientist with expertise in differential privacy and PETs/Cryptographic Engineering, and a passion for creating ethical and responsible AI systems. Deep experience working at the intersection of engineering, product, and compliance with stakeholders of varying technical backgrounds.
Miscellany
- Interests: Tennis
