MICS - Mathematics and Computer Science Laboratory

Founded in the early 2000s, MICS is the research laboratory in Mathematics and Computer Science at CentraleSupélec. Research at MICS is concerned with the analysis and modelling of complex systems and data, whether they come from the industry, life or social sciences, financial markets, information technology or networks.

Research Axes

Biomathematics

Data-driven and Knowledge-based Mathematical Modelling, Statistical Inference and Computational to help solve major challenges in life sciences and health. Methods for Biological Systems and Data. Applications to precision medi-cine, neurosciences, molecular biology, gene-tics, plant science, epidemiology, decision-aided diagnosis.

Quantitative Finance

Microstructure, high-frequency massive data: auctions, manipulation, market making, reinforcement learning; Covariance matrix filtering and investment; Agent models: cognitive biases and investor behaviour, money markets; Robust transport, mean-field games.

Fundamental Mathematics

Harmonic analysis and geometric measure theory; Analysis of partial differential equations; Harmonic analysis and geometric measure theory; Numerical analysis; Stochastic analysis (rough paths, Fokker-Planck equation); Probabilistic Modelling and Statistics of Stochastic Processes: Regularity of stochastic processes (fractional processes).

Scientific Computing

Massively parallel computing; GPU computing; Algorithmic interface between parallel computing and the numerical analysis of partial differential equations and algebraic differential equations.

Computer Science

Formalisms and methods based on logic, probabilities, graphs, category theory, and mathematical morphology for software-based systems.

Artificial Intelligence and Decision Modelling

Deep learning; Representation learning; Few shot and continual learning; Explainable artificial intelligence; AI for computer vision; AI for NLP; Multicriteria decision making, preference learning, knowledge representation and reasoning, explaining decisions, multi-objective optimization, collective decisions.

Application Domains

Industrial systems (aerospace, construction, energy, transportation);
Environment (plants, hydrology, landscapes, acoustics);
Information technology and networks (Internet, multimedia, knowledge management);
Life sciences (medicine, molecular biology, genetics, epidemiology);
Markets and companies (finance, capital markets, business intelligence).

Academic Partners

Institut Gustave Roussy, CEA, INRA, INRIA, INSERM, AgroParisTech, Cambridge, Oxford, Georg-August Universität Göttingen, Sapienza University of Rome, Polytechnic University of Turin, RUDN University, Bar Ilan, TU München, University of Tokyo, Doshisha University (Japan), Beihang University, (China), Providence University (Taiwan), University of Washington, University of Michigan, Temple University, Berkeley Lab (USA).