Statistics, Machine Learning and Simulation (SMLS) Group at EURECOM

Objective: Simulation is a fundamental methodology for understanding complex systems. Examples of such systems appear in a variety of fields, such as geophysical and environmental sciences (e.g., climate, weather, and natural disasters), social sciences (e.g., economics, finance, and insurance), and engineering (e.g., aviation engineering, traffic engineering, and architectural engineering), where simulation has been widely used. However, the reliability of a simulation depends on several factors, such as how accurately the underlying model (e.g., differential equations) can approximate the system of interest, and how accurately the execution of the simulation (e.g., a numerical solution to the differential equations) can approximate the model. For a simulation to be reliable, these factors must be systematically and objectively validated, but doing so manually is challenging. 


Our objective is to develop statistical and machine learning methodologies for enhancing the reliability of simulation. As such methodologies themselves must also be reliable, we study mathematical theories to back the methodologies. Moreover, by cooperating with researchers and engineers from applied fields, we identify the needs in practice and develop tools that practitioners can easily use.


Ph.D. students:

Research Engineers:


Past Postdocs and Research Engineers:

Past interns: