Contact: albert [at] sundaillet.com
Location: Geneva, Switzerland
Spoken languages: English (fluent), French (native), Swedish (native)
I am a Research/Software Engineer at CERN, where I develop production distributed learning solutions and contribute to research about building reliable machine learning systems, balancing tradeoffs of performance, privacy, robustness and interpretability.
I completed my undergraduate in Engineering Physics at KTH Royal Institute of Technology. For my Bachelor’s thesis, I worked on cell image classification with convolutional neural networks at KTH and Karolinska Institutet, supervised by Prof. Karl Meinke (link to thesis, mirror on this website).
I obtained a Master’s degree in Machine Learning at KTH with an exchange at EPFL. For my Master’s thesis I studied self-supervised pre-training of attention-based models for 3D medical image segmentation at RaySearch Laboratories, supervised by Dr. Jonas Söderberg and Prof. Mårten Björkman (link to thesis).
During my studies I interned at CERN, Tobii and Ericsson.
I am motivated by both theoretical understanding and practical applications of machine learning.
Skills
Languages: Python, JavaScript, SQL, Shell scripting, C/C++
Frameworks: NumPy, JAX, PyTorch, Matplotlib, plotly, pandas, Flask, d3.js
Misc: Unix, Git, Docker, Podman, LaTeX
Personal Projects
An overview of my personal projects is available here.
Theses and Selected Publications
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A. S. Aillet, F. Frisk, “Assessing the Impact of Stain Normalization on a Cell Classification Model in Digital Histopathology”, 2021, Available: http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1632706, mirror
A. S. Aillet and S. Sondén, “[Re] Variational Neural Cellular Automata”, in ML Reproducibility Challenge 2022, Available: https://neurips.cc/virtual/2023/poster/74151, https://github.com/albertaillet/vnca
A. S. Aillet, “Self-supervised pre-training of an attention-based model for 3D medical image segmentation”, 2023, Available: https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1795309.
A. Protani, L. Giusti, A. S. Aillet, et al., “Federated GNNs for EEG-Based Stroke Assessment”, in UniReps: 2nd Edition of the Workshop on Unifying Representations in Neural Models, 2024, Available: https://neurips.cc/virtual/2024/102633.
D. R. Santos, A. Protani, L. Giusti, A. S. Aillet, P. Brutti, and L. Serio, “Feasibility Analysis of Federated Neural Networks for Explainable Detection of Atrial Fibrillation,” in 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom), 2024, Available: https://doi.org/10.1109/HealthCom60970.2024.10880809.
A. Protani, L. Giusti, C. Iacovelli, A. S. Aillet, D. R. Santos, G. Reale, A. Zauli, M. Moci, M. Garbuglia, P. Brutti, P. Caliandro and L. Serio, “Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals,” in 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom), 2024, Available: https://doi.org/10.1109/HealthCom60970.2024.10880717.
A. Protani, M. M. Van De Bosch, L. Giusti, H. Silva, P. Cacace, A. S. Aillet, F. Hummel and L. Serio, “Decoder-Free Supervoxel GNN for Accurate Brain-Tumor Localization in Multi-modal MRI”, in Reconstruction and Imaging Motion Estimation, and Graphs in Biomedical Image Analysis, Lina Felsner et al. (eds.), 2024, Available: https://doi.org/10.1007/978-3-032-06103-4_16.