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Nathanaël Perraudin
Nathanaël Perraudin

Nathanaël Perraudin

Sr. Data Scientist
Research
(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 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). In 2017, Nathanaël Perraudin joined the Swiss Data Science Center in the ETH Zurich team as a Research Data Scientist. He focused 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 in tango dancing, tandem bike touring, skiing and rock climbing.

Projects

LEAP

Completed
LEArning to Print – towards data-driven real-time predictions for additive manufacturing
Academia

DATSSFLOW

Completed
Data Science and Mass Movement Seismology: Towards the Next Generation of Debris Flow Warning
Academia

4Real

Real-time urban pluvial flood forecasting
Academia

MLATEM

Machine Learning tools for Analytical Transmission Electron Microscopy
Academia

DLOC

Completed
Deep Learning for Observational Cosmology
Academia

AADS

Completed
Data Science Enabled Acoustic Design
Academia

Publications

Bergmeister, A.; Palgunadi, K. H.; Bosisio, A.; Ermert, L.; Koroni, M.; Perraudin, N.; Dirmeier, S.; Meier, M. "High Resolution Seismic Waveform Generation using Denoising Diffusion" Preprint 2024 View publication
Bergmeister, A.; Martinkus, K.; Perraudin, N.; Wattenhofer, R. "Efficient and Scalable Graph Generation through Iterative Local Expansion" The Twelfth International Conference on Learning Representations 2024 View publication
Xydis, A.; Perraudin, N.; Rust, R.; Heutschi, K.; Casas, G.; Grognuz, O. R.; Eggenschwiler, K.; Kohler, M.; Perez-Cruz, F. "GIR dataset: A geometry and real impulse response dataset for machine learning research in acoustics" Applied Acoustics 208 109333 2023 View publication
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" Ultramicroscopy 249 113719 2023 View publication
Haefeli, K. K.; Martinkus, K.; Perraudin, N.; Wattenhofer, R. "Diffusion Models for Graphs Benefit From Discrete State Spaces" The First Learning on Graphs Conference 2022 View publication
Chaudhary, P.; Leitão, J. P.; Donauer, T.; D’Aronco, S.; Perraudin, N.; Obozinski, G.; Perez-Cruz, F.; Schindler, K.; Wegner, J. D.; Russo, S. "Flood Uncertainty Estimation Using Deep Ensembles" Water 14 19 2980 2022 View publication
Teurtrie, A.; Perraudin, N.; Holvoet, T.; Chen, H.; Alexander, D. T. L.; Obozinski, G.; Hébert, C. "Physics-Guided Machine Learning for the Analysis of Low SNR STEM-EDXS Data" Microscopy and Microanalysis 28 S1 2978-2979 2022 View publication
Martinkus, K.; Loukas, A.; Perraudin, N.; Wattenhofer, R. "SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators" International Conference on Machine Learning 2022 View publication
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 View publication
Rust, R.; Xydis, A.; Heutschi, K.; Perraudin, N.; Casas, G.; Du, C.; Strauss, J.; Eggenschwiler, K.; Perez-Cruz, F.; Gramazio, F.; et al. "A data acquisition setup for data driven acoustic design" Building Acoustics 28 4 345-360 2021 View publication
Marafioti, A.; Majdak, P.; Holighaus, N.; Perraudin, N. "GACELA: A Generative Adversarial Context Encoder for Long Audio Inpainting of Music" IEEE Journal of Selected Topics in Signal Processing 15 1 120-131 2021 View publication
Martinkus, K.; Lucchi, A.; Perraudin, N. "Scalable Graph Networks for Particle Simulations" Proceedings of the AAAI Conference on Artificial Intelligence 35 8912–8920 2021 View publication
Xydis, A.; Perraudin, N.; Rust, R.; Lytle, B. A.; Gramazio, F.; Kohler, M. "Data-Driven Acoustic Design of Diffuse Soundfields" ACADIA 2021: Realignments: Toward Critical Computation 170-181 2021 View publication
Perraudin, N.; Marcon, S.; Lucchi, A.; Kacprzak, T. "Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks" Frontiers in Artificial Intelligence 4 673062 2021 View publication
Defferrard, M.; Milani, M.; Gusset, F.; Perraudin, N. "DeepSphere: a graph-based spherical CNN" International Conference on Learning Representations 2020 View publication
Kalofolias, V.; Perraudin, N. "Large Scale Graph Learning From Smooth Signals" International Conference on Learning Representations 2019 View publication
Grnarova, P.; Levy, K. Y.; Lucchi, A.; Perraudin, N.; Goodfellow, I.; Hofmann, T.; Krause, A.; Wallach, H.; Larochelle, H.; Beygelzimer, A.; et al. "A Domain Agnostic Measure for Monitoring and Evaluating GANs" Advances in Neural Information Processing Systems 32 2019 View publication
Perraudin, N.; Defferrard, M.; Kacprzak, T.; Sgier, R. "DeepSphere: Efficient spherical convolutional neural network with HEALPix sampling for cosmological applications" Astronomy and Computing 27 130-146 2019 View publication
Isufi, E.; Loukas, A.; Perraudin, N.; Leus, G. "Forecasting Time Series With VARMA Recursions on Graphs" IEEE Transactions on Signal Processing 67 18 4870-4885 2019 View publication
Marafioti, A.; Perraudin, N.; Holighaus, N.; Majdak, P. "A Context Encoder For Audio Inpainting" IEEE/ACM Transactions on Audio, Speech, and Language Processing 27 12 2362-2372 2019 View publication
Loukas, A.; Perraudin, N. "Stationary time-vertex signal processing" EURASIP Journal on Advances in Signal Processing 2019 1 36 2019 View publication
Perraudin, N.; Srivastava, A.; Lucchi, A.; Kacprzak, T.; Hofmann, T.; Réfrégier, A. "Cosmological N-body simulations: a challenge for scalable generative models" Computational Astrophysics and Cosmology 6 1 5 2019 View publication
Marafioti, A.; Holighaus, N.; Perraudin, N.; Majdak, P. "Adversarial Generation of Time-Frequency Features with application in audio synthesis" International Conference on Machine Leaerning (ICML) 2019 View publication
Grassi, F.; Loukas, A.; Perraudin, N.; Ricaud, B. "A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs" IEEE Transactions on Signal Processing 66 3 817-829 2018 View publication
Perraudin, N.; Ricaud, B.; Shuman, D. I.; Vandergheynst, P. "Global and local uncertainty principles for signals on graphs" APSIPA Transactions on Signal and Information Processing 7 1 2018 View publication
Perraudin, N.; Holighaus, N.; Søndergaard, P. L.; Balazs, P. "Designing Gabor windows using convex optimization" Applied Mathematics and Computation 330 266-287 2018 View publication
Perraudin, N.; Holighaus, N.; Majdak, P.; Balazs, P. "Inpainting of Long Audio Segments With Similarity Graphs" IEEE/ACM Transactions on Audio, Speech, and Language Processing 26 6 1083-1094 2018 View publication

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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