Algorithms, Learning and Computation

The main research axes of the department concern computational models and their robustness (from high-performance computing to quantum computing, including neural networks and distributed algorithms), processing architectures (graphs, distributed, synchronous or asynchronous processing), and methods (e.g., continuous, combinatorial, stochastic optimization; statistical learning and information theory). By construction, these research axes are the subject of collaborations with other departments, in particular Data Science and Fluid Mechanics-Energetics. The analysis and design of models and processes rely heavily on mathematical approaches (discrete and continuous, including probability, statistics and combinatorics) and statistical physics (in particular on the phase transition phenomena of complex systems), in conjunction with the LMO and CMAP (Maths and Maths. Appli), IJCLab (Physics), L2S (Signal Processing), as well as the LIX and the future LMF (Formal Methods) teams

Application areas include scientific computing (e.g., linear algebra, tensor calculus, numerical optimization, dynamical systems, simulation of quantum algorithms, computational mathematics and physics, systems of differential equations), distributed computing (e.g., cloud, scheduling, virtual currency, ubiquitous computing, autonomous robots, microbiological circuits), and data analysis.