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Research Engineer @ ETH AI Center Apertus Core Team Previously: ML Research @ Cognex, Postdoc @ Inria & Ph.D. @ EPFL
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ETH AI Center
Data Scientist
June 26, 2026 – Present
apertus-tokenizer-development
June 16, 2026 – Present
apertus-tokenizer-development — GitHub repository
View Projectkassiope_algorithms
April 28, 2024 – April 28, 2024
kassiope_algorithms — GitHub repository
View Projectyarn-mistral-flax
November 10, 2023 – November 13, 2023
An implementation of yarn-mistral-7B in flax. This implementation is based on the pytorch version uploaded to huggingface.
View Projectsomething_for_almost_nothing
October 12, 2023 – October 16, 2023
Code for the paper "Something for (almost) nothing: improving deep ensemble calibration using unlabeled data"
View Projectwide_resnet_wfixup_jax
July 19, 2023 – July 19, 2023
An implementation of WideResNets with Fixup initialization in Jax/Flax. This can be useful for use cases where Batch Normalization should be avoided (for example when using the Laplace approximation to the Bayesian posterior).
View ProjectBayesian-Neural-Networks-Reading-List
January 16, 2023 – November 1, 2023
A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"
View Projectcold_posteriors_pac_bayes
September 3, 2022 – November 15, 2022
The code for the paper "Cold Posteriors through PAC-Bayes". https://arxiv.org/pdf/2206.11173.pdf
View Projectimage_classification_SOTA
November 23, 2021 – November 23, 2021
This repository aims to understand and implement the state of the art in image classification (as of November 2021) in PyTorch.
View ProjectPAC_Bayes_Invariance
September 7, 2018 – September 8, 2022
Code from the paper "PAC-Bayesian Margin Bounds for convolutional neural networks". https://arxiv.org/pdf/1905.09677.pdf
View Projectnon-linear-estimation-mcmc
November 27, 2016 – May 21, 2017
Implementation of a Markov-Chain Monte Carlo (MCMC) method for a non-linear estimation problem.
View ProjectCultural Fit Analysis
The candidate's projects are heavily research-focused and academic, indicating a strong fit for roles requiring deep theoretical exploration and implementation of novel algorithms. However, the lack of team projects or diverse industry applications might suggest a need to assess collaboration and broader business context understanding for a typical Data Scientist role. The current experience at ETH AI Center aligns well with a research-heavy environment.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's projects indicate a strong research-oriented and independent work style.