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CIMI thematic trimester “Beyond classical regimes in statistical inference and machine learning”

CIMI thematic trimester “Beyond classical regimes in statistical inference and machine learning”

Posted on by henrique.goulart

I am thrilled to announce that with Xiaoyi Mai, Mohamed Tamaazousti and Vincent Lahoche we are organizing a CIMI thematic trimester titled “Beyond classical regimes in statistical inference and machine learning,” to be held during Fall 2024. Here is a summary of its scientific program:

  • (September 2024, precise date TBA) An opening colloquium about recent developments and challenges of high-dimensional statistical inference and machine learning.
  • (From 7 October to 11 October 2024) A one-week thematic school with the theme “Models”, featuring mini-courses on tools and techniques for the analysis of high-dimensional models in statistical inference and machine learning, including (but not limited to) tools from random matrix theory, statistical physics and spin glasses.
  • (From 14 October to 18 October 2024) A one-week thematic school with the theme “Optimization”, featuring mini-courses on the study of high-dimensional random optimization landscapes and on the dynamics of optimization algorithms in high dimensions.
  • (From 4 November to 8 November 2024) A workshop focused on recent results on high-dimensional (supervised and unsupervised) machine learning and statistical inference. This workshop will in particular involve a round-table debate with top experts on this domain about the major open problems on the field and some promising trends and recent developments.

More details (and a website) are coming soon, in particular regarding a call for poster contributions to be presented during the workshop.

Our chair on large random tensors was selected for ANITI 2.0!

Our chair on large random tensors was selected for ANITI 2.0!

Posted on by henrique.goulart

I’m excited to announce that our chair titled “LArge Tensors for daTa analysIs and maChine lEarning” (LATTICE) was selected for the second version of ANITI, the AI institute of Toulouse. It will thus be part of ANITI’s proposal for setting up an AI cluster in Toulouse, in the sense of the currently open AnR call.

This chair, held by myself (as the PI) and Xiaoyi Mai of IMT (as a co-chair), is devoted to the study of large random tensor models, their estimation, and their application to (unsupervised) machine learning. An abstract is available at ANITI’s website, along with a list of all the other selected chairs.

More info on that will be available later this year.

M2 Internship (5 to 6 months) on tensor-based machine learning methods

M2 Internship (5 to 6 months) on tensor-based machine learning methods

Posted on by henrique.goulart

We look for strongly motivated candidates with a solid background on mathematics and statistics, having good programming skills in scientific computing languages (Python, Matlab, Julia), for an M2 internship on the performance of tensor-based machine learning methods for large-scale data.

The internship shall last 5 to 6 months, starting from Spring 2022, and will take place at the ENSEEIHT site of IRIT, in Toulouse. It will be co-supervised by Henrique Goulart (Assistant Professor at Toulouse INP) and Rodrigo Cabral (Assistant Professor at Polytech Nice), and will be fully funded by a CIMI grant.

For more details on this offer, please check the detailed description at https://cloud.irit.fr/index.php/s/FyzGeSlAT9LfSkw

Invited talk at “Random tensors at CIRM”

Invited talk at “Random tensors at CIRM”

Posted on by henrique.goulart

I’m happy to announce that I’ve been invited to give a talk at a conference on random tensors, which will be held at the Centre International de Rencontres Mathématiques (CIRM), in Marseille, on March 2022!

The program of this event will be quite rich, featuring several nice talks and mini-lectures by mathematicians and physicists working on topics related to tensors and random tensors — check out the website for the detailed program: https://tensors-2022.sciencesconf.org/.