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--- start [2025/10/23 13:24] – [Motivation] director-llga
+++ start [2025/10/23 13:44] (current) – [Contacts] director-llga
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 ==== Objectives ====
-In recent years, Generative AI has seen explosive growth, with Large Language Models (LLMs) reshaping how society and the economy interact with the digital world. These technologies enable tasks that were unimaginable just a few years ago—from machine translation and code generation to creative content generation and advanced reasoning —at unprecedented scale. The objective of the LLGA Master’s Program is to prepare students to become experts in this transformative field, with the skills to advance both theory and application. Equally important are graph-based data and models, which capture the structure of complex relationships in social networks, molecular structures, or logistics chains. Advances in Graph Neural Networks (GNNs) and Graph Representation Learning are unlocking insights and applications previously out of reach. Combining LLMs with graph-based methods enables multimodal generative AI, opening new opportunities in healthcare, finance, recommendation systems, and beyond.
+In recent years, Generative AI has seen explosive growth, with Large Language Models (LLMs) reshaping how society and the economy interact with the digital world. These technologies enable tasks that were unimaginable just a few years ago—from machine translation and code generation to creative content generation and advanced reasoning—at unprecedented scale. Simultaneously, we face an increasing amounts of data that only make sense in the context of their networks, from large-scale social networks and power grids to microscopic proteins and molecules. Advances in graph-based models, such as Graph Neural Networks (GNNs) and graph representation learning, are unlocking insights and applications previously out of reach by capturing these complex structures. Combining LLMs with graph methods enables multimodal generative AI, opening new opportunities in healthcare, finance, recommendation systems, and beyond.
-The goal of the LLGA program is to provide students with a solid theoretical foundation in machine and deep learning, alongside hands-on experience with state-of-the-art generative techniques in the context of LLMs and graph generation. Through projects, case studies, and industrial applications, graduates acquire rare and highly sought-after expertise, positioning them to innovate and lead in the next generation of AI technologies.
+The objective of the LLGA Master’s Program is to prepare students to become experts in this transformative field, with the skills to advance both theory and application.
-Courses are given by professors from Ecole Polytechnique and partner institutes and companies.
+The goal of the LLGA program is to provide students with a solid theoretical foundation in machine learning and deep learning, alongside hands-on experience with state-of-the-art generative techniques in the context of LLMs and graphs. By means of projects, case studies, and industrial applications, graduates acquire rare and highly sought-after expertise, positioning them to innovate and lead in the next generation of AI technologies.
-All courses will be held in English. Both French and foreign students are welcome.
+Courses are given by professors from École polytechnique, associated institutes, and industrial partners.
+All courses are held in English and students for all nationalities are welcome.
 ==== Curriculum Description  ====
-The LLGA Master is a two-year program that combines a rigorous foundation with advanced specialization in Generative AI, focusing on both Large Language Models (LLMs) and graph-structured data.
+The LLGA Master is a two-year program that combines a rigorous foundation with advanced specialization in Generative AI, focusing both on Large Language Models (LLMs) and graph-structured data.
-During the first year (M1), students build a strong academic basis through courses shared with engineering and other master’s programs at École Polytechnique. This year develops solid skills in mathematics, machine learning, and deep learning, while also introducing key domains such as Natural Language Processing and Graph Representation Learning. By the end of M1, students are equipped with the theoretical and methodological tools essential to pursue advanced AI studies.
+During the first year (M1), students build a strong academic basis through courses shared with engineering and other master’s programs at École polytechnique. This year develops solid skills in mathematics, machine learning, and deep learning, while also introducing key domains such as Natural Language Processing and Graph representation learning. By the end of the M1, students are equipped with the theoretical and methodological tools essential to pursue advanced AI studies.
 The second year (M2) is dedicated to specialization and hands-on learning. Bespoke courses focus on the latest industrial and scientific advances, including DevOps and LLM Engineering Principles, Data Engineering for LLMs, Graph Generative AI with Applications in Bio and Medicine, Small-Scale and Specialized LLMs, AI Strategy, Ethics, and Socioeconomic Challenges, and Generative AI for Entrepreneurship. Students engage in projects and case studies that connect theory with real-world applications, gaining practical experience with state-of-the-art techniques.
 This unique structure—broad foundations followed by tailored specialization—ensures graduates acquire both depth and versatility, preparing them to become leaders in the rapidly evolving field of Generative AI.
 ==== Student Applications ====
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   * Aymeric Dieuleveut, Applied Mathematics, Ecole Polytechnique [[aymeric.dieuleveut@polytechnique.edu]]
   * Michalis Vazirgiannis, Computer Science, Ecole Polytechnique [[michalis.vazirgiannis@polytechnique.edu]]
-  * Ye Zhu, Computer Science, Ecole Polytechnique [[ye.zhu.lix@polytechnique.edu]]
+  * Ye Zhu, Computer Science, Ecole Polytechnique [[ye.zhu@polytechnique.edu]]
   * Rémi Flamary, Applied Mathematics, Ecole Polytechnique  [[remi.flamary@polytechnique.edu]]
   * Luiz Chamon, Applied Mathematics, Ecole Polytechnique [[luiz.chamon@polytechnique.edu]]