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Nathanaël Perraudin
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

LEAP

In Progress
LEArning to Print – towards data-driven real-time predictions for additive manufacturing

DATSSFLOW

In Progress
Data Science and Mass Movement Seismology: Towards the Next Generation of Debris Flow Warning

4Real

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

MLATEM

Machine Learning tools for Analytical Transmission Electron Microscopy
Big Science Data

DLOC

Completed
Deep Learning for Observational Cosmology
Big Science Data

AADS

Completed
Data Science Enabled Acoustic Design
Energy, Climate & Environment

Publications

Teurtrie, A.; Perraudin, N.; Holvoet, T.; Chen, H.; Alexander, D. T.; Obozinski, G.; Hébert, C."espm: A Python library for the simulation of STEM-EDXS datasets"249113719
Perraudin, N.; Holighaus, N.; Majdak, P.; Balazs, P."Inpainting of Long Audio Segments With Similarity Graphs"266.01083-1094
Stalder, S.; Perraudin, N.; Achanta, R.; Perez-Cruz, F.; Volpi, M."What You See is What You Classify: Black Box Attributions"Neural Information Processing Systems (NeurIPS)2022

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.
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November 5, 2018

Deepsphere | A neural network architecture for spherical data

Deepsphere | A neural network architecture for spherical data

Not all datasets are images and we need architectures that adapt to other types of data, encoding both domain specific knowledge and data specific characteristics. For instance, at the SDSC, we deal with spherical data, i.e. curved images on a sphere, but without clear borders and arbitrary orientation.
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