Graduate School and Research Center in Digital Sciences


Eurecom - Data Science 
Assistant professor
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04 93 00 82 00



  • Patrick Loiseau is currently Assistant Professor within the Department of Data Science where he teaches statistics, game theory and network economics.

  • Prior to joining EURECOM, he taught in the areas of physics, computer architecture, probability and signal processing at Ecole Normale Supérieure de Lyon, University of Versailles Saint-Quentin-en-Yvelines and University of California, Santa Cruz.

My courses

  • G_Theory / Fall 2017 - Game Theory


     This course is an introduction to game theory. Game theory studies interactions of agents whose objectives depend on others actions and not only theirs. It permits to model and understand many real-world strategic interactions, e.g., in economics. The course introduces the main concepts of game theory (Nash equilibrium, etc.) and illustrates them with examples from economics, political sciences, computer science, engineering, etc.

    Teaching and Learning Methods : Each class will have approximately two hours of lecture followed by an exercise session for students to practice the material learned.

    Course Policies : Attendance to the exercise sessions is mandatory

  • NetEcon / Fall 2017 - Network Economics

    Economics and incentives consideration govern in large part the development and actual performance of networks and digital services. The objective of this course is to raise awareness of students on these questions and how to solve them. The course will introduce a number of modern research challenge in economic analysis of networks and network-related services (web, security, etc.) and show how mathematical methods based on game theory are used to address them.

    Teaching and Learning Methods : The course will contain a number of lectures as well as projects where students read and analyze a research article on a topic complementary to those presented in class

    Course Policies : Attendance to all lectures and student presentations is mandatory

  • Stat / Fall 2017 - Statistical data analysis

    This course is an introduction to statistics. The goal is to provide students with simple and efficient statistical methods to analyze data in order to answer questions such as: 'Is this performance improvement significant?', 'What is the uncertainty on that result?', 'How can I predict a new output of my system based on measurements?', 'Which factors have a significant impact on the performance of my system?', and more ; as well as to familiarize the students with statistical reasoning to mathematically analyze the methods presented.

    Teaching and Learning Methods : The course has theoretical lectures complemented by labs for students to practice statistics and data analysis (using the MATLAB software), and homeworks for students to fully assimilate the material.

    Course Policies : Attendance to the exercise session / labs is mandatory



  • Best Student Demonstration award at ACM Sigmetrics/Performance 2009, for the demo "Automated traffic measurements and analysis in Grid'5000", with Romaric Guillier, Oana Goga, Matthieu Imbert, Paulo Goncalves and Pascale Vicat-Blanc Primet.