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 ==== 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]]