**Email:** joseph.salmon@inria.fr

**Address:**
IMAG, c.c. 051

Université de Montpellier

Place Eugène Bataillon

34095 Montpellier Cedex 5

(office 415, building 9)

**Email:** joseph.salmon@inria.fr

**Address:**
IMAG, c.c. 051

Université de Montpellier

Place Eugène Bataillon

34095 Montpellier Cedex 5

(office 415, building 9)

I am looking for outstanding and highly motivated people to work (as intern, Ph.D. student, post-doctorate or research engineer) on **machine learning**, and more precisely on:

**citizen science**and**crowdsourcing****high dimensional**/**robust**statistics,**variable selection**,**sparsity****optimization**for**machine learning**(including federated learning, privacy, etc.)

The application process is light:

- Email a CV, a transcript of recent grades, and explain in a short paragraph why you are interested to join.
- Upon interest, I will ask two reference letters (one only for interns or Ph.D. students) to be sent directly to me.
- At this stage an interview (possibly online) will be arranged to double-check your skills and profile compatibility.

- Sept. 2024: I've joined Inria as a senior researcher (Team: Iroko, Montpellier)
- Sept. 2023: ANR VITE (PI: B. Thirion, theme: variable importance / explainability) accepted.
- March 2022: visitor at the Simons Institute for the Theory of Computing
- July 2021:
**IUF**Nomination (junior member): https://www.iufrance.fr/detail-de-lactualite/247.html - Dec. 2019: The ANR AI chair proposal CaMeLOt (CooperAtive MachinE Learning and OpTimization) has been selected.

- April 2023: STATLEARN 2023 in Montpellier
- November 2022: https://ml4lifesciences.sciencesconf.org/
- November 2020: Launching ML-MTP Machine Learning in Montpellier, Theory & Practice.
- May 2019 : Workshop in Montpellier Graph signals : learning and optimization perspectives

- OrganizationFiles: This repository provides some tools, advice and guidelines for researchers working in applied mathematics, statistics or machine learning.
- BenchOpt: a package to make transparent and reproducible comparisons between optimization algorithms
- PlantNet-300K: a subset of the Pl@ntNet database, with about 300k labeled images (plant species) and 1k classes. The dataset is available on Zenodo.
- Celer: a fast Lasso solver (associated ICML2018 paper "Dual Extrapolation for Faster Lasso Solvers"), pdf, slides
- sparse-ho: a fast hyper-parameter package to select the best Lasso parameter efficiently (associated to ICML2020 paper "Implicit differentiation of Lasso-type models for hyperparameter optimization", pdf)
- matlab toolboxes for statistics and image processing (this is legacy), I don't use Matlab anymore.

More on my Github Page

- Antoine Simoes: co-supervised by Yohann de Castro 2022-2025?

- Tanguy Lefort [Ph.D. 2021-2024] co-supervised with Benjamin Charlier and Alexis Joly (now post-doctorate at Inria Lille)
- Axel Dubar 2023-2024, now Ph.D. student at Univ. Montpellier.
- Camille Garcin [Ph.D. 2020-2023] co-supervised by Alexis Joly and Maximilien Servajean (now post-doctorate at Inria Montpellier)
- Emmanuel Pilliat [Ph.D. 2020-2023] co-supervised by Nicolas Verzelen and Alexandra Carpentier (now assistant professor at ENSAI)
- Hashem Ghanem [Ph.D. 2020-2023] co-supervised by Samuel Vaiter and Nicolas Keriven (now datascientist at Expleo)
- Damien Blanc [Ph.D. 2019-2022], co-supervised by Benjamin Charlier and funded by Quantacell
- Cassio Fraga Dantas Post-doctorate associate: 2022, (now at Researcher at INRAE)
- Florent Bascou [Ph.D. 2019-2022], co-supervised by Sophie Lèbre,

Manuscript: "Sparse linear model with quadratic interactions" - Quentin Bertrand [Ph.D. 2018-2021], co-supervised by Alexandre Gramfort (now at Mila),

Manuscript: "Hyperparameter selection for high dimensional sparse learning : application to neuroimaging" - Nidham Gazagnadou [Ph.D. 2018-2021] co-supervised by Robert Gower (now at Sony AI),

Manuscript: "Expected smoothness for stochastic variance-reduced methods and sketch-and-project methods for structured linear systems" - Pierre-Antoine Bannier [Intern 2021], co-supervised by Alexandre Gramfort
- Jérôme-Alexis Chevalier [Ph.D. 2017-2020], co-supervised by Bertrand Thirion (Senior Data Scientist at Emerton Data),

Manuscript: "Statistical control of sparse models in high dimension" - Mathurin Massias [Ph.D. 2016-2019], co-supervised by Alexandre Gramfort (now CR INRIA, Lyon),

Manuscript: "Sparse high dimensional regression in the presence of colored heteroscedastic noise : application to M/EEG source imaging" - Evgenii Chzhen [Ph.D. 2016-2019], co-supervised by Mohamed Hebiri (now CR CNRS, Saclay),

Manuscript: "Plug-in methods in classification" - Eugene Ndiaye [Ph.D., 2015-2018], co-supervised by Olivier Fercoq (now post-doctorate at GeorgiaTech),

Manuscript: "Safe optimization algorithms for variable selection and hyperparameter tuning" - Jean Lafond [Ph.D., 2013-2016] co-supervised by Éric Moulines (now at Cubist Systematic, UK),

Manuscript: "Complétion de matrice : aspects statistiques et computationnels" - Igor Colin [Ph.D., 2013-2016] co-supervised by Stéphan Clémençon and funded by Streamwide (now at Huawei),

Manuscript: "Adapting machine learning methods to U-statistics" - Jair Montoya [Post Doc, 2016-2017], co-supervised by Olivier Fercoq
- Thierry Guillemot (now at ARIADNEXT) Engineer, co-supervised by Alexandre Gramfort, 2016

I am a statistician and an applied mathematician at Inria, with a strong interest in machine learning, optimization and data science. In terms of applications, I focus on **citizen science**, **crowdsourcing** and **high dimensional statistics**.

From 2018 to 2024 I was a full professor at Université de Montpellier and a Junior member of the Institut Universitaire de France (IUF), from 2021 to 2024. For the spring and summer quarters 2018, I was a visiting assistant professor at UW, Statistics departement. From 2012 to 2018 I was an assistant professor at Telecom ParisTech and an associate member at INRIA Parietal Team. Back in 2011-2012, I was a post-doctoral Associate at Duke university working with Rebecca Willett.

In 2010, I finished my Ph.D. in statistics and image processing under the supervision of Dominique Picard and Erwan Le Pennec at the Laboratoire de Probabilités et de Modélisation Aléatoire, now LPSM, in Université Paris Diderot.

This is an undergraduate course on stochastic modeling and Monte Carlo methods, with exercises in Python.

Details can be found here: HAX603X - Stochastic Modeling.

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This is an undergraduate course on convex optimization with exercises in Python.

Details can be found here: HAX606X - Convex optimization.

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This is a master level course on Software development for data science, using Python.

Details can be found here: HAX712X.

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This course is mostly about supervised techniques in Machine Learning.

Details can be found here HMMA308 - Statistical Machine Learning.

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This is an undergraduate course introducing statistics and data visualisation.

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This is an undergraduate course introducing advanced time series analysis.

Details can be found here: HMMA237 - Advanced time series.

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This is a master level course introducing scientific computing and modern software practices.

Details can be found here: HMMA238 - Scientific Software Development.

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This contains Master 2 exercices on statistical learning.

Details can be found here: M2MO - Machine Learning.

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This is an undergraduate course on linear models.

Details can be found here: SD204 - Linear Models.

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This is a grade course on robust statistics and optimization.

Details can be found here: STAT593 - Robust statistics.

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This is an undergraduate course introducing Python for scientific computing.

Details can be found here: HLMA310 - Scientific Python.

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This is an undergraduate course introducing linear models.

Details can be found here MDI720 - Linear Models.

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This is an undergraduate course introducing advanced linear models (ANOVA, Mixed-effects models, etc.).

Details can be found here: HMMA307 - Advanced Linear Models.

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This is a master Master 2 course on Machine learning (with Z. Harchaoui, J. Mairal and L. Jacob).

Details can be found here:

CR12 - Machine Learning (2014-2015)

CR12 - Machine Learning (2013-2014).

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This is an undergraduate course on descriptive statistics.

Details can be found here: SD3 - Descriptive Statistics.

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This course is mostly about linear models in econometrics.

Details can be found here: M53010 - Econometrics.

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