Caterina Urban

Research Scientist (Chargé de Recherche)

I am a member of the INRIA research team ANTIQUE (ANalyse StaTIQUE). Previously, I was a postdoctoral researcher at the Chair of Programming Methodology, led by Peter Müller at ETH Zurich. I completed my Ph.D. in Computer Science, working under the joint supervision of Radhia Cousot and Antoine Miné at École Normale Supérieure. I hold a Bachelor’s and a Master’s degree in Computer Science, both received with full marks and honors (summa cum laude) from the Università degli Studi di Udine.

My research interests span the whole spectrum of formal methods and aim at developing methods and tools to enhance the reliability of computer software and understanding complex software systems. My main area of expertise is static analysis based on abstract interpretation, which provides rigorous mathematical guarantees of the behavior of computer programs. I am currently engaged in a long-term research effort to enhance the understanding and reliability of data science software, which nowadays plays an increasingly important role in critical decision making in fields such as finance and medicine.

News

📑 Our paper Perfectly Parallel Fairness Certification of Neural Networks has been conditionally accepted at OOPSLA 2020!

👥 I am a member of the NFM 2021 program committee

👥 I am co-chairing with Lisa Nguyen Quang Do the 10th International Workshop on the State of the Art in Program Analysis (SOAP 2021) !

👥 I am co-chairing with Marijana Lazić the 7th Logic Mentoring Workshop at FLOC 2022!

👩🏼‍💻 I have been developing a Python interface for the APRON numerical abstract domain library. Contributions are welcome!

Interests

Static Program Analysis
Abstract Interpretation
Programming Languages
Data Science
Machine Learning

Education

PhD in Computer Science, 2015

École Normale Supérieure, Paris, France
MSc in Computer Science, 2011

Università degli Studi di Udine, Udine, Italy
BSc in Computer Science, 2009

Università degli Studi di Udine, Udine, Italy

Research Projects

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.

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.

Recent Publications

More Publications

Caterina Urban, Maria Christakis, Valentin Wüstholz, Fuyuan Zhang. Perfectly Parallel Fairness Certification of Neural Networks. Conditionally Accepted at OOPSLA, 2020.

Code Project Project Artifact

Caterina Urban. What Programs Want: Automatic Inference of Input Data Specifications. CoRR abs/2007.10688, 2020.

PDF Code Project BibTeX arXiv

Caterina Urban, Maria Christakis, Valentin Wüstholz, Fuyuan Zhang. Perfectly Parallel Fairness Certification of Neural Networks. CoRR abs/1912.02499, 2019.

PDF Code Project Project BibTeX arXiv