Claire Mathieu works on algorithms, particularly the design of approximation schemes for NP-hard problems from combinatorial optimization. She is employed by CNRS (Centre National de la Recherche Scientifique) at ENS (Ecole normale Superieure) in Paris, France. She is also a "professeur associé" at ENS. Her current interests include approximation algorithms for planar graphs and for Euclidean problems; probabilistic models for social networks; hierarchies of semi-definite programming relaxations; network tomography; scheduling to minimize energy; online algorithms; and occasional forays into algorithmic game theory.
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Coursera
Dive into the world of Approximation Algorithms Part I, an insightful online course designed to equip you with the skills needed to tackle NP-hard combinatorial optimization problems effectively. This course explores efficient methods for approximating solutions that balance computational efficiency with performance guarantees.
Coursera
Continue your journey into the fascinating world of theoretical computer science with 'Approximation Algorithms Part II'. This course is a continuation from Part 1, where you'll delve deeper into powerful design and analysis techniques such as linear programming duality and semidefinite programming. Master these methods to solve complex problems effectively.