👋 Hello, World!
Angelo
Saadeh
Data Privacy · Data Quality · Machine Learning
I am a research fellow within the Valda team of the Computer Science Department at École Normale Supérieure – PSL, in the framework of the PR[AI]RIE Artificial Intelligence Cluster.

Valda is a joint team between Inria, CNRS and ENS-PSL. More information about Valda can be found here.

I currently develop algorithms for data provenance, uncertainty, and probabilistic inference in SQL systems. Previously, I was a research fellow at CNRS@CREATE in Singapore (Descartes program, WP2: Hybrid AI), where I built voting algorithms for ensemble learning and added differential privacy to machine learning pipelines.

Before that, during my Ph.D. at Télécom Paris, I worked on privacy-preserving ML systems using cryptographic tools and differential privacy. 😎

Get in Touch

Have a question or want to collaborate? Send me a message below.

Resume
Experience & Education
Experience
Researcher
École Normale Supérieure, Paris
Feb 2026 – Ongoing
  • Implement provenance and uncertainty features in ProvSQL (advanced SQL, semirings).
  • Develop efficient algorithms for provenance circuits and probabilistic inference.
  • Analyse complexity and semantics of provenance for advanced SQL queries.
Researcher
CNRS@CREATE, Singapore
Jun 2023 – Jun 2025
  • Integrate data privacy mechanisms like differential privacy into aggregation, training, and inference.
  • Design ensemble learning methods for aggregation across multiple heterogeneous models.
  • Build permutation-invariant machine learning models to measure the quality of data.
  • Construct integer-based mappings that linearize multidimensional data spaces for smart indexing.
Graduate Researcher
Télécom Paris, France
Nov 2019 – Jun 2023
  • Design hybrid privacy-preserving machine learning models with cryptographic tools and differential privacy.
  • Implement secure federated learning protocols ensuring data confidentiality across multiple parties.
  • Deploy and test a socket network for secure two-party computations in machine learning tasks.
Research Intern
CNRS@CREATE, Singapore
Aug 2022 – Nov 2022
  • Apply secure computation protocols for unsupervised information retrieval algorithms; truth discovery.
Research Intern
Inria Saclay, France
Mar 2019 – Aug 2019
  • Accelerate computations on encrypted data using mathematical tools like error-correcting codes and hyperinvertible matrices.
Education
Ph.D. in Computer Science
Télécom Paris, France
Nov 2019 – Jun 2023
Thesis: Applications of Secure Multi-party Computation Machine Learning
Advisors: Daniel Augot (Inria) and Matthieu Rambaud (Télécom Paris)
Doctoral scholarship: Labex Digicosme
M.Sc. in Computer Science
Université Paris-Saclay, France
Sep 2017 – Sep 2019
Specialization: Cryptography and Applied Algebra
Thesis: Mathematical Tools for Secure Multi-party Computation
B.Sc. in Mathematics
Lebanese University, Lebanon
Sep 2014 – Jul 2017
Skills
Programming
PythonCC++Java
Libraries & Tools
PyTorch / PySyftTensorFlow / TF-encrypted PandasScikit-learn Socket NetworksLaTeX
Databases & Web
SQL / PostgreSQLXML / XQueryHTML
Languages
English (fluent)French (fluent) Arabic (fluent)Italian (B2) Mandarin (HSK2)
Research
Publications & Projects
Discovering Voting Power for Ensemble Methods
DEXA 2025 · Bangkok, Thailand · Lecture Notes in Computer Science, Vol. 16046
Pratik Karmakar, Angelo Saadeh, Pierre Senellart, and Stéphane Bressan
doi →
TF-MPC: Confidential Truth-Finding with Multi-Party Computation
GitHub Repository · 2022
Angelo Saadeh
github →
Applications of Secure Multi-party Computation in Machine Learning
PhD Thesis · Institut Polytechnique de Paris · 2023
Angelo Saadeh
hal →
Epsilon-Differentially Private and Fully Secure Logistic Regression on Vertically Split Data
ICDIS 2022 · 4th International Conference on Data Intelligence and Security
Angelo Saadeh, Vaibhavi Kumari, and Stéphane Bressan
doi →
Confidential Truth Finding with Multi-Party Computation
DEXA 2023 · Penang, Malaysia · Springer
Angelo Saadeh, Pierre Senellart, and Stéphane Bressan
doi →
WannaFly: Dummy Ransomware for Red Team Exercises
REDOCS 2021 · Airbus
Aboubacar Djibo Maman Sani, Boukerrou Hamid, Marinho Dylan, Saadeh Angelo, and Somers Benjamin
pdf →
Teaching
Courses & Lab Sessions
INFMDI731
Advanced Cryptography
Télécom Paris · Spring 2021
Intervention: Secure Multi-Party Computation
MDI210
Optimization and Numerical Analysis
Télécom Paris · Lab sessions: Fall 2020  ·  Course: Fall 2021
Course lectures and Java lab sessions covering optimization methods and numerical techniques.
BT5110
Data Management and Warehousing
National University of Singapore (NUS) · Lab sessions: Fall 2023
PostgreSQL lab sessions covering data management, warehousing concepts, and SQL querying techniques.
Cool Stuff
Side Projects
Kalendar

A tile puzzle: place 6 out of the 7 pieces on the 7×5 board to cover every cell except today's date. Drag pieces from the tray, rotate & flip with the button. Every day has many solutions, you can see them if you click "Show Solution" to see one possible solution.

⟶ Solutions on GitHub
Target day:
Board
Pieces
Chinese Flashcards
Practice HSK vocabulary with flashcards. Click the card to reveal pinyin, then click again for the definition. Select multiple HSK levels below.
⟶ View on GitHub
click to reveal
—
0/0
HSK-3 Français
Cartes Mémoire · Sélectionner les leçons pour réviser le vocabulaire. Cliquer sur la carte pour afficher le pinyin, puis cliquer à nouveau pour faire apparaître la définition.
cliquez pour révéler
0/0
🔐 Secure Multi-party Computation
Simulate an addition protocol for n parties. All computations are done modulo 220 = 1048576.

Step 1. Choose number of parties (n) and hidden parties (k). You won't have access to the information of the hidden parties

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A special message just for you!

Let's go to Paris I don't care if we get lost in the scene!
Paint the city like Picasso would've done in his dreams
Do the things that we were way too terrified of before <3