CV

CV

Damien Aupretre

Student

📞 +33 7 67 91 90 60
✉ damien.aupretre@student-cs.fr
🐙 TheTrigun99 - github


Education

Internship VILAB - HYU (08/2026 – 02/2027)

Working on reflection removal

Master of Engineering — CentraleSupĂ©lec (2024 – 2027)

Part of Université Paris-Saclay, ranked #12 worldwide (QS 2024).

CPGE PC* — LycĂ©e KlĂ©ber (2022 – 2024)

Admitted to École Polytechnique (ranked #1 in France, top 0.1% of applicants), CentraleSupĂ©lec, Mines PSL (Most prestigious engineering schools)
GPA: 4.3 / 4.3

Deutsch-Französisch Gymnasium (DFG-LFA) (2015 – 2022)

Bilingual French-German Abitur : 1.0 (maximum grade)
Specialisation: mathematics, biology, and chemistry


Projects-Experience

Research Papers — Personal Project (2025 - ??)
  • Working on diffusion/flow models
  • Getting deep understanding of algorithms by implementing everything from scratch (and reflecting about it [most important part])
  • Implemented basics deep learning architectures (DiT, Diffusion, VAE, Word2Vec,...)
Health Data Science Project — CentraleSupĂ©lec (2025)
  • Analysed high-dimensional multimodal genomic data (gene expression + DNA alteration) from pediatric brain cancer patients to identify tumour-localisation biomarkers.
  • Implemented and compared multiple sparse classification methods (LASSO logistic regression, Sparse Discriminant Analysis, SGCCA, Multiview), with a focus on the multi-block methods handling joint analysis across data types.
  • Reduced dimensionality from 15,000+ genomic variables down to a handful of robust, interpretable biomarkers per tumour class.
TIPE, Independent Physics Research Project — LycĂ©e KlĂ©ber (2023)
  • Investigated capillarity and buoyancy effects by varying liquid volumes and surface properties of ping-pong balls.
  • Modelled ejection dynamics from a water-filled cup using fluid mechanics principles (surface tension, pressure balance).

Skills

Skills
  • Python (NumPy, pandas, pytorch)
  • Machine learning/ Deep Learning
  • R (statistical computing)
  • Humor

Languages

Languagues
  • French — Native
  • German — Fluent (C1-certified)
  • English — Fluent
  • Pytorch — Fluent (C1 not certified)

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