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Researcher with industrial experience in machine learning theory, optimisation problems and artificial intelligence.
I also have a Master of Science in Computer Science from EPFL and a Master of Engineering in applied Mathematics and a Minor in Financial Engineering.
Publications
Publications
Random matrix methods for high-dimensional machine learning models, 2024 EPFL Thesis
https://doi.org/10.5075/epfl-thesis-10524Gradient flow on extensive-rank positive semi-definite matrix denoising, 2023 IEEE Information Theory Workshop (ITW)
https://arxiv.org/abs/2303.09474 / https://ieeexplore.ieee.org/document/10161669Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures, preprint arxiv
https://arxiv.org/abs/2212.06757Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model, 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
https://arxiv.org/abs/2110.11805 / https://papers.nips.cc/paper/2021/hash/b4f8e5c5fb53f5ba81072451531d5460-Abstract.htmlRank-one matrix estimation: analytic time evolution of gradient descent dynamics, Conference of Learning Theory (COLT) 2021
https://arxiv.org/abs/2105.12257 / http://proceedings.mlr.press/v134/bodin21a.htmlSystems and methods for integration of disparate data feeds for unified data monitoring, US Patent (Inventor) , 2019
https://patents.google.com/patent/US11227288
Work Experience
Work Experience
Quantitative Researcher, Qube Research & Technologies (Zürich, Switzerland)
Quantitative Researcher, Qube Research & Technologies (Zürich, Switzerland)
Curent (March 2024)
Hedge Hedge Quantitative Research
PhD In Computer science, EPFL (Lausanne, Switzerland)
PhD In Computer science, EPFL (Lausanne, Switzerland)
4.5 years (September 2019 - February 2024)
Research in machine learning theory and optimization with high-dimensional statistical methods with Prof. Nicolas Macris.
Visiting Scholar, Harvard University (Boston MA, USA)
Visiting Scholar, Harvard University (Boston MA, USA)
2 months (May 2023 - July 2023)
Conducted research on the theoretical aspects of gradient descent optimization in high-dimensional spaces with Prof. Yue Lu.
Data Scientist, Credit-Suisse (Lausanne & Zurich, Switzerland)
Data Scientist, Credit-Suisse (Lausanne & Zurich, Switzerland)
3 years, 6 months (Feb 2016 - July 2019)
Mathematical modelling, analysis and model development in transaction surveillance for fraud detection and monitoring money laundering activity
Design and implementation of a holistic client analysis framework
Software Engineer Intern, Google (Mountain View CA, USA)
Software Engineer Intern, Google (Mountain View CA, USA)
3 months (July 2015 - Sept. 2015)
Deep Learning and Natural Language Processing at Google News
Education
Education
(2019 - Today) PhD of Computer Science - EPFL, Lausanne, Switzerland
(2014 - 2016) Master of Computer Science - EPFL, Lausanne, Switzerland
(2012 - 2014) Master of Engineering in Applied Mathematics - Centrale Paris, Paris, France
(2015 - 2016) Minor of Financial Engineering - EPFL, Lausanne, Switzerland
(2010 - 2012) Preparatory Classes MPSI/MP* - Pasteur Neuilly sur Seine, Paris, France
Dev. Skills
Dev. Skills
Python (Expert, >10years) (Tensorflow, PyTorch, Matplotlib, Django, Pandas, Numpy, Scipy, Requests, Jupyter, etc)
OCaml (>10years, occasional) (yacc, lex)
C/C++ (>10years, occasional) (OpenCL, OpenGL, GTK)
Javascript, HTML, CSS (occasional) (React, MongoDB, SQL, etc)
Contact
Contact
University: https://people.epfl.ch/antoine.bodin
GitHub: https://github.com/antoinexp
Google Scholar: https://scholar.google.com/citations?user=6PRe79MAAAAJ
Email: antoine.bodin@epfl.ch
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