Nathanaël Perraudin

Nathanaël Perraudin

Sr. Data Scientist
Academia
(Alumni)

After finishing his Master in electrical engineering at the Ecole Fédérale de Lausanne (EPFL), Nathanaël worked as a researcher in the Acoustic Research Institute (ARI) in Vienna. In 2013, he returned to EPFL for a PhD, where he specialized himself in different fields of data science: signal processing, machine learning, graph theory and optimization. Furthermore, he created two open source libraries for optimization (UNLocBoX) and graph signal processing (GSPBOX). Since 2017, Nathanaël Perraudin is a Research Data Scientist at the Swiss Data Science Center in the ETH Zurich. He focuses on different aspects of deep learning in the area of generative models (VAE and GAN), recursive architectures and convolutional neural network for irregular domains. Outside office hours, he is passionate by tango dancing, tandem bike touring, skiing and rock climbing.

Projects

4Real

In Progress
Real-time urban pluvial flood forecasting
Energy, Climate & Environment

MLATEM

In Progress
Machine Learning tools for Analytical Transmission Electron Microscopy
Big Science Data

DLOC

Completed
Deep Learning for Observational Cosmology
Big Science Data

AADS

In Progress
Data Science Enabled Acoustic Design
Energy, Climate & Environment

Publications

Mentioned in

September 23, 2022

What you see is what you classify: black box attributions

What you see is what you classify: black box attributions

The lack of transparency of black-box models is a fundamental problem in modern Artificial Intelligence and Machine Learning. This work focuses on how to unbox deep learning models for image classification problems.

Case Studies

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