MScT LLGA

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Programme of the Master of Science and Technology (MScT) of Ecole Polytechnique

Link to the official page on the Ecole Polytechnique website.

The LLGA Master’s Program is a two-year, fully English-taught degree with a strong international outlook. Designed for ambitious students, the program equips graduates with both a solid theoretical foundation and hands-on industry expertise in Generative AI. With a special emphasis on Large Language Models (LLMs) and graph-structured data, students gain the skills to harness cutting-edge technologies and apply them across diverse fields—from healthcare and biology to finance, social networks, 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. 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.

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.

Courses are given by professors from Ecole Polytechnique and partner institutes and companies.

All courses will be held in English. Both French and foreign students are welcome.

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.

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.

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.

Students are required to have a suitable background in mathematics and computer science. In practice, this means knowledge of linear algebra, statistics, as well as Python programming, algorithms and data structures. Some exposure to natural language processing and graph theory is desirable, but not required. Candidates with Bachelor degrees that are not in Mathematics or Computer Science are also admissible if the required knowledge domains are covered in their degree programs.

Students will have highly suitable profiles for data science or AI roles in industry. While the LLM aspect of the program allows students to apply to companies in most digital companies, the graphs and applications aspect of the program prepares the students particularly well for roles in the pharma, logistics and communication industries. Graduates of the LLGA Master’s Program are prepared for careers as AI/ML Engineers, Data Scientists, NLP Specialists, Graph AI Experts, and AI Research Scientists, as well as strategic roles such as AI Product Managers, Solution Architect. Opportunities span industries including tech, healthcare, finance, consulting, social media, e-commerce, and public institutions.

The program includes an M1 as well as an M2 internship of several months each. There will furthermore be regular interaction with professionals in industry in academia in the weekly M2 seminar series on “ethical issues, law and novel applications of AI”. In addition, many of the taught courses in the MScT program have project-based assessments. The students will therefore graduate from this program with ample practical experience and a profound insight into the different real-world applications of their academic knowledge. We also expect important exchanges with the generative AI industry in terms of focused internships, invited keynotes and meetups. We expect that the placement of our graduates in the relevant industry will be natural and swift.

Since the program teaches cutting-edge methodology in a relevant field of research, pursuing a PhD after this program is perfectly possible.

The academic co-directors of the MScT are:

• Johannes Lutzeyer, Computer Science (LIX / DIX), Ecole Polytechnique johannes.lutzeyer@polytechnique.edu
• Aymeric Dieuleveut, Applied Mathematics (CMAP), Ecole Polytechnique aymeric.dieuleveut@polytechnique.edu
• Michalis Vazirgiannis, Computer Science (LIX / DIX), Ecole Polytechnique michalis.vazirgiannis@polytechnique.edu
• Ye Zhu, Computer Science (LIX / DIX), Ecole Polytechnique ye.zhu.lix@polytechnique.edu
• Rémi Flamary, Applied Mathematics (CMAP), Ecole Polytechnique remi.flamary@polytechnique.edu
• Luiz Chamon, Applied Mathematics (CMAP), luiz.chamon@polytechnique.edu

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