Recent Publications

Full list of publications

Contact

Email: joseph"dot"salmon "dot"taff@gmail"dot"com

Address: IMAG, c.c. 051
Université de Montpellier
Place Eugène Bataillon
34095 Montpellier Cedex 5
(office 415, building 9)

News

Team

Ph.D. Students


Alumni

Short Bio

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

Since 2018, I am a full professor at Université de Montpellier and an associate member at INRIA Parietal Team. 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. Back in 2011 and 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.

Software

  • BenchOpt: package to simplify, make more transparent and more reproducible comparisons between optimization algorithms

  • Celer: a fast Lasso solver, code of the 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, code of the associated ICML2020 paper "Implicit differentiation of Lasso-type models for hyperparameter optimization" (pdf)
  • matlab toolboxes for statistics and image processing (this is legacy)

More on my Github Page

Joining? Open positions in my lab

I am always looking for outstanding and highly motivated people to join my team as interns, Ph.D. students, post-doctorates or research engineers in the following areas:

  • citizen science and crowdsourcing
  • optimization for machine learning (including federated learning, privacy, etc.)
  • high dimensional and robust statistics

I always have open positions for outstanding applicants (post-doc, Ph.D. thesis, internship). The application process is light:

  1. Email me your CV, transcript of most recent grades (for interns and Ph.D. students) and explain in a paragraph why you are interested to join my group.
  2. After preliminary feedback on my side, I will ask you to secure two reference letters (one is enough for interns or Ph.D. students) to be sent directly to me.
  3. At this stage an interview (possibly online) will be arranged to double-check your skills and profile compatibility.


Teaching: list of courses

HAX606X - Convex optimization (2020-2023)

This is an undergraduate course on convex optimization with exercises in Python.
Details can be found here: HAX606X - Convex optimization.
Course language: 🇫🇷

HAX712X - Software development for data science (2020-2023)

This is a master level course on Software development for data science, using Python.
Details can be found here: HAX712X.
Course language: 🇬🇧

HMMA308 - Statistical Machine Learning (2018-2021)

This course is mostly about supervised techniques in Machine Learning.
Details can be found here HMMA308 - Statistical Machine Learning.
Course language: 🇫🇷

HLMA408 - Data science for ecology (2018 - 2021)

This is an undergraduate course introducing statistics and data visualisation.
Details can be found here: HLMA408 - Data science for ecology.
Course language: 🇫🇷

HMMA237 - Advanced time series (2018-2019)

This is an undergraduate course introducing advanced time series analysis.
Details can be found here: HMMA237 - Advanced time series.
Course language: 🇫🇷 and 🇬🇧

HMMA238 - Scientific Software Development (2018-2019)

This is a master level course introducing scientific computing and modern software practices.
Details can be found here: HMMA238 - Scientific Software Development.
Course language: 🇬🇧

M2MO - Statistical Learning (2013-2018)

This contains Master 2 exercices on statistical learning.
Details can be found here: M2MO - Machine Learning.
Course language: 🇫🇷

SD204 - Linear Models (2016-2018)

This is an undergraduate course on linear models.
Details can be found here: SD204 - Linear Models.
Course language: 🇬🇧

STAT593 - Robust statistics (2018-2019)

This is a grade course on robust statistics and optimization.
Details can be found here: STAT593 - Robust statistics.
Course language: 🇬🇧

HLMA310 - Scientific Python (2018-2020)

This is an undergraduate course introducing Python for scientific computing.
Details can be found here: HLMA310 - Scientific Python.
Course language: 🇫🇷

MDI720 - Linear Models (2013-2018)

This is an undergraduate course introducing linear models.
Details can be found here MDI720 - Linear Models.
Course language: 🇫🇷

HMMA307 - Advanced Linear Models (2019-2021)

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 🇬🇧

CR12 - Machine Learning (2013-2015)

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: 🇬🇧

SD3 - Descriptive Statistics (2010-2011)

This is an undergraduate course on descriptive statistics.
Details can be found here: SD3 - Descriptive Statistics.
Course language: 🇫🇷

M53010 - Econometrics (2009-2010)

This course is mostly about linear models in econometrics.
Details can be found here: M53010 - Econometrics.
Course language: 🇫🇷




Talks

Full list of talks, with slides and possibly videos when recorded.

Miscellaneous

Here (Miscellaneous), you will find some (math)art and other distractions.