Research Projects

ForML

The ForML project investigates the use of abstract interpretation and counterexample-guided abstraction refinement with the purpose of efficiently answering queries about semantic properties of machine learning models such as robustness, fairness, and explainability. We also aim to formally verify existing and novel algorithms for answering such queries, and to derive certified implementations.

SAIF

The goal of SAIF is to use the vast knowledge accumulated over decades in formal methods to rethink them and address the novel safety concerns raised by machine learning-based systems. Through the synergy of a diverse consortium with complementary expertise, we aim to bring society closer to a state where it can benefit from achievements in machine learning without suffering undue consequences.

SeDaNo

[…page under construction…]

Libra

The goal of the Libra project is to develop new analyses and tools to reason about and certify fairness of decision-making software.

Lyra

The Lyra research project is a long-term research effort to enhance the understanding and reliabilty of data science software. It aims ad developing new practical and accessible analyses and tools to reason about and provide rigorous guarantees of the behavior of data analytics, big data, machine learning, and deep learning applications.

FuncTion

The goal of the FuncTion project was the development of a static analyzer which automatically infers ranking functions and sufficient precondition for program termination (and other liveness properties) by means of abstract interpretation.

Recent Publications

More Publications

. Quantitative Input Usage Static Analysis. In NFM, 2024.

PDF HAL

. Monotonicity and the Precision of Program Analysis. In POPL, 2024.

PDF ACM

. Abstract Interpretation-Based Feature Importance for Support Vector Machines. In VMCAI, 2024.

PDF Project HAL Springer

Recent Talks

More Talks

Machine Learning Interpretability and Verification
Friday, March 29, 2024 2:00 PM
Interpretability-Aware Verification of Machine Learning Software
Thursday, April 27, 2023 2:00 PM
Interpretability-Aware Verification of Machine Learning Software
Thursday, March 30, 2023 2:00 PM

People

Postdocs

  • Alessandro De Palma
    Postdoc, Inria & École Normale Supérieure, France: Nov 2023 -
  • Marco Campion
    Postdoc, Inria & École Normale Supérieure, France: Feb 2023 -

PhD Students

  • Naïm Moussaoui-Remil
    PhD, École Normale Supérieure, France: Nov 2023 -
  • Serge Durand (co-supervised with Zakaria Chihani)
    PhD, Université Paris-Saclay, France: Nov 2021 -
  • Denis Mazzucato
    PhD, École Normale Supérieure, France: Oct 2020 -

Former Students

  • Naïm Moussaoui-Remil (Master Student, École Normale Supérieure de Rennes, France)
    M2 Research Internship, Inria & École Normale Supérieure, France: Mar - Aug 2023
  • Kevin Pinochet (Master Student, University of Chile, Chile)
    Research Internship, Inria & École Normale Supérieure, France: Jan - Apr 2023
  • Abhinandan Pal (Bachelor Student, IIIT Kalyani, India)
    Research Internship, Inria & École Normale Supérieure, France: Nov 2022 - Jan 2023
  • Abhinandan Pal (Bachelor Student, IIIT Kalyani, India)
    Research Internship (remote): May - Jul 2022
  • Ali El Husseini (École Normale Supérieure Paris-Saclay, France)
    M2 Internship, École Normale Supérieure Paris-Saclay & Inria & École Normale Supérieure, France: Mar - Aug 2022
  • Luca Negrini (PhD Student, Università Ca’ Foscari Venezia, Italy)
    Research Internship, Inria & École Normale Supérieure, France: Jan - Apr 2022
  • Guruprerana Shabadi (Bachelor Student, École Polytechnique, France)
    L3 Internship, Inria & École Normale Supérieure, France: Jan - Mar 2022
    🥇 Winner of the Global Undergraduate Awards 2022
  • Abhinandan Pal (Bachelor Student, IIIT Kalyani, India)
    Research Internship (remote): Dec 2021 - Jan 2022
  • Serge Durand (Master Student, École Normale Supérieure Paris-Saclay, France)
    M1 Internship, École Normale Supérieure, France (remote): Jun - Aug 2020
  • Marco Zanella (PhD Student, Università degli Studi di Padova, Italy)
    Research Internship, Inria & École Normale Supérieure, France (remote): May - Aug 2020
  • Radwa Sherif Abdelbar (Bachelor Student, German University in Cairo, Egypt)
    Bachelor’s Thesis, ETH Zurich, Switzerland: Mar - Aug 2018
  • Lowis Engel (Master Student, ETH Zurich, Switzerland)
    Master’s Thesis, ETH Zurich, Switzerland: Feb - Aug 2018
  • Madelin Schumacher (Master Student, ETH Zurich, Switzerland)
    Master’s Thesis, ETH Zurich, Switzerland: Sep 2017 - Mar 2018
  • Samuel Ueltschi (Master Student, ETH Zurich, Switzerland)
    Master’s Thesis, ETH Zurich, Switzerland: Mar - Sep 2017
  • Mostafa Hassan (Bachelor Student, German University in Cairo, Egypt)
    Bachelor’s Thesis, ETH Zurich, Switzerland: Mar - Aug 2017
  • Simon Wehrli (Master Student, ETH Zurich, Switzerland)
    Master’s Thesis, ETH Zurich, Switzerland: Feb - Aug 2017
  • Flurin Rindisbacher (Master Student, ETH Zurich, Switzerland)
    Master’s Thesis, ETH Zurich, Switzerland: Mar - Aug 2017
  • Severin Münger (Master Student, ETH Zurich, Switzerland)
    Master’s Thesis, ETH Zurich, Switzerland: Sep 2016 - Mar 2017
  • Nathanaëlle Courant (Bachelor Student, École Normale Supérieure, France)
    L3 Internship, ETH Zurich, Switzerland: Jun - Jul 2016
  • Lukas Neukom (Master Student, ETH Zurich, Switzerland)
    Master’s Thesis, ETH Zurich, Switzerland: Mar - Sep 2016
  • Seraiah Walter (Master Student, ETH Zurich, Switzerland)
    Master’s Thesis, ETH Zurich, Switzerland: Feb - Aug 2016

Teaching

Year 2023-2024

Year 2022-2023

Year 2021-2022

Year 2020-2021

Software

ApronPy


Python Interface for the APRON Numerical Abstract Domain Library

FuncTion


Abstract Interpretation-based Static Analysis for (Conditional) Termination (and Other CTL Properties)

Libra


Perfectly Parallel Abstract Interpretation-based Fairness Certification for Neural Networks

Lyra


Abstract Interpretation-based Static Analysis for Data Science Applications

Typpete


SMT-based Static Type Inference for Python 3.x

Contact