Explore coarse-graining techniques for stochastic dynamics in this 39-minute lecture by Tony Lelievre at the Alan Turing Institute. Delve into the challenges of reducing complex models across various scientific disciplines to a manageable number of variables for practical computation and accurate prediction. Learn about emerging statistical approaches that leverage large-scale data analysis to identify and parameterize crucial model features. Examine the evolving modeling paradigm that combines statistical inference, high-throughput computation, and physical laws. Gain insights into the mathematical foundations being developed to integrate these methods effectively. Discover applications in collective dynamics, molecular modeling, cell biology, and fluid dynamics as part of a broader workshop on data-driven modeling of complex systems.