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The MVA Master’s degree is 30 years old

Life in the lab, Formation

Véronique ALMADOVAR : médaille de cristal 2025 du CNRS

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Gabriele Facciolo nommé membre senior de l'IUF

Awards and honors

Frédéric Dias nommé membre senior de l'IUF

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Engineering Sciences

Failure prognostics under sensor degradation: A comparative study of online and batch Bayesian inference

Accurate failure prognostics and Remaining Useful Life (RUL) prediction are critical for prognostics and health management, but remain challenging when sensor degradation and stochastic dependencies are present. This study compares two Bayesian…

Hai-Canh Vu, Mitra Fouladirad, David Bassir, Thao Nguyen-Trang

Reliability Engineering and System Safety - 2027

Engineering Sciences

Semiautomated Planning of Urban Drainage System by CNN-Based Land Use Segmentation and GIS-Based Drainage Generation Algorithm

Qianqian Zhou, Mansheng Lin, Wanen Feng, Qisheng Zhong, Jing Xiao, Gongfa Chen, David Bassir

Journal of Water Resources Planning and Management - 2026

Engineering Sciences

SaurKshetra: A curated dataset and ML-based classification for solar energy site selection

Yash Shivgan, Chandan Bhirud, Anshul Jamgade, Atharva Kulkarni, Rutuja Patil, Gagandeep Kaur, David Bassir, Ghanshyam Tejani

Data in Brief - 2026

Machine Learning

Unbiased Open World Regularization for Fair Self-Supervised Learning

Léo Nicollier, Marc Pic, Pablo Musé, Enric Meinhardt-Llopis, Gabriele Facciolo

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News & events

The MVA Master’s degree is 30 years old

Véronique ALMADOVAR : médaille de cristal 2025 du CNRS

Gabriele Facciolo nommé membre senior de l'IUF

Frédéric Dias nommé membre senior de l'IUF

Publications

Failure prognostics under sensor degradation: A comparative study of online and batch Bayesian inference

Semiautomated Planning of Urban Drainage System by CNN-Based Land Use Segmentation and GIS-Based Drainage Generation Algorithm

SaurKshetra: A curated dataset and ML-based classification for solar energy site selection

Unbiased Open World Regularization for Fair Self-Supervised Learning

Supporting institutions