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:
The application process is light:
More on my Github Page
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.
Course language: 🇫🇷
This is an undergraduate course on convex optimization with exercises in Python.
Details can be found here: HAX606X - Convex optimization.
Course language: 🇫🇷
This is a master level course on Software development for data science, using Python.
Details can be found here: HAX712X.
Course language: 🇬🇧
This course is mostly about supervised techniques in Machine Learning.
Details can be found here HMMA308 - Statistical Machine Learning.
Course language: 🇫🇷
This is an undergraduate course introducing statistics and data visualisation.
Details can be found here: HLMA408 - Data science for ecology.
Course language: 🇫🇷
This is an undergraduate course introducing advanced time series analysis.
Details can be found here: HMMA237 - Advanced time series.
Course language: 🇫🇷 and 🇬🇧
This is a master level course introducing scientific computing and modern software practices.
Details can be found here: HMMA238 - Scientific Software Development.
Course language: 🇬🇧
This contains Master 2 exercices on statistical learning.
Details can be found here: M2MO - Machine Learning.
Course language: 🇫🇷
This is an undergraduate course on linear models.
Details can be found here: SD204 - Linear Models.
Course language: 🇬🇧
This is a grade course on robust statistics and optimization.
Details can be found here: STAT593 - Robust statistics.
Course language: 🇬🇧
This is an undergraduate course introducing Python for scientific computing.
Details can be found here: HLMA310 - Scientific Python.
Course language: 🇫🇷
This is an undergraduate course introducing linear models.
Details can be found here MDI720 - Linear Models.
Course language: 🇫🇷
This is an undergraduate course introducing advanced linear models (ANOVA, Mixed-effects models, etc.).
Details can be found here: HMMA307 - Advanced Linear Models.
Course language: 🇫🇷 and 🇬🇧
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).
Course language: 🇬🇧
This is an undergraduate course on descriptive statistics.
Details can be found here: SD3 - Descriptive Statistics.
Course language: 🇫🇷
This course is mostly about linear models in econometrics.
Details can be found here: M53010 - Econometrics.
Course language: 🇫🇷