Gradient flow methods have emerged as a powerful tool for solving problems of sampling, inference and learning within Statistics and Machine Learning. This one-day workshop will provide an overview of existing and developing techniques based on continuous dynamics and gradient flows, including Langevin dynamics and Wasserstein gradient flows. Six invited speakers will present recent work in this field, which will cover the theoretical foundations of these methods as well as practical implementation details. Applications to be discussed include generative modelling, Bayesian posterior sampling, parameter estimation in statistical models, variational inference, and optimisation. The workshop will appeal to researchers and practitioners interested in the intersections of probability, statistics, machine learning, and applied mathematics. Participants will gain an understanding of how gradient flow methods can enable efficient algorithms for sampling and optimization to solve general inference problems in Statistics and Machine Learning.