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| start [2025/10/09 12:53] – [Objectives] cxypolop | start [2025/10/23 13:44] (current) – [Contacts] director-llga |
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| ==== Motivation ==== | ==== Motivation ==== |
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| 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. | The **LLGA Master’s Program** is a two-year degree taught completely in English 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 will gain the skills to harness cutting-edge technologies and apply them across diverse fields—from healthcare and biology to finance, social networks, and beyond. |
| ==== Objectives ==== | ==== Objectives ==== |
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| 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. |
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| 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. |
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| 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. |
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| All courses will be held in English. Both French and foreign students are welcome. | |
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| | Courses are given by professors from École polytechnique, associated institutes, and industrial partners. |
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| | All courses are held in English and students for all nationalities are welcome. |
| ==== Curriculum Description ==== | ==== Curriculum Description ==== |
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| The first week consists of refresher courses in mathematics (statistical analysis, introduction to Machine Learning) and Computer Science (C++ programming, basics of 3D modeling and algorithmic geometry), to complement the existing academic background of the students. | 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. |
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| It will be followed by two training periods of 2.5 months, consisting of courses on the following topics **(detailed version [[https://msct.dix.polytechnique.fr/aivic/wiki/doku.php?id=curriculum|here]])** : | 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. |
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| * Deep Learning, Generative AI and Reinforcement learning | 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. |
| * Geometric data analysis and deep learning on such data | |
| * AI in Computer Graphics, Virtual and Augmented Reality | |
| * AI on sequence data including text (LLMs) | |
| * Computer vision (images-based 3D reconstruction, recognition in images & videos) | |
| * Robotics from motion planning to the design of soft robots for medical applications | |
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| In complement, a weekly seminar will encourage discussions of ethical issues and novel applications, while transverse projects will give some practical, real-world experience to each student in one or several domains of interest. | |
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| The last part of the school year will consist of a five- to six-month research project, conducted either in a public or private research lab. | |
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| | 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 ==== | ==== Student Applications ==== |
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| We are looking for very strong students with initial high-quality training in either Computer Science or Applied Mathematics. | 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. |
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| Please take a look at the [[https://programmes.polytechnique.edu/en/master/admissions-msct/application-deadlines-procedure | official documentation]] for application process details. | |
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| ==== Industrial & Institutional Partnership / Cercle des partenaires ==== | ==== Industrial & Institutional Partnership / Cercle des partenaires ==== |
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| Our MScT is accompanied by an active group of industrial and institutional partners contributing both student grants and transverse projects, as well as student internships. Some in the past, some currently, these partners include Inria, Google, Enedis, Idemia, Helsing as well as, younger companies such as Anatoscope, Homiwoo, InitML and Wemap. | 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. |
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| ==== Internships and PhD proposals ==== | ==== Internships and PhD proposals ==== |
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| | 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. |
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| Students have the option to either find their own internship position or to choose one of the internship proposals that we have made available **[[https://msct.dix.polytechnique.fr/aivic/wiki/doku.php?id=internships|here]]** and are regularly updating. | Since the program teaches cutting-edge methodology in a relevant field of research, pursuing a PhD after this program is perfectly possible. |
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| The academic co-directors of the MScT are: | The academic co-directors of the MScT are: |
| * Johannes Lutzeyer, Computer Science (LIX / DIX), Ecole Polytechnique [[johannes.lutzeyer@polytechnique.edu]] | * Johannes Lutzeyer, Computer Science, Ecole Polytechnique [[johannes.lutzeyer@polytechnique.edu]] |
| * Aymeric Dieuleveut, Applied Mathematics (CMAP), Ecole Polytechnique [[aymeric.dieuleveut@polytechnique.edu]] | * Aymeric Dieuleveut, Applied Mathematics, Ecole Polytechnique [[aymeric.dieuleveut@polytechnique.edu]] |
| * Michalis Vazirgiannis, Computer Science (LIX / DIX), Ecole Polytechnique [[michalis.vazirgiannis@polytechnique.edu]] | * Michalis Vazirgiannis, Computer Science, Ecole Polytechnique [[michalis.vazirgiannis@polytechnique.edu]] |
| * Ye Zhu, Computer Science (LIX / DIX), Ecole Polytechnique [[ye.zhu.lix@polytechnique.edu]] | * Ye Zhu, Computer Science, Ecole Polytechnique [[ye.zhu@polytechnique.edu]] |
| * Rémi Flamary, Applied Mathematics (CMAP), Ecole Polytechnique [[remi.flamary@polytechnique.edu]] | * Rémi Flamary, Applied Mathematics, Ecole Polytechnique [[remi.flamary@polytechnique.edu]] |
| * Luiz Chamon, Applied Mathematics (CMAP), [[luiz.chamon@polytechnique.edu]] | * Luiz Chamon, Applied Mathematics, Ecole Polytechnique [[luiz.chamon@polytechnique.edu]] |
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