Stochastic Diffusion

Stochastic diffusion models are probabilistic frameworks used to generate and analyze data by modeling it as a continuous-time process evolving through random fluctuations. Current research focuses on developing efficient algorithms for learning and optimizing these models, including advancements in diffusion probabilistic models, piecewise deterministic Markov processes, and kernel mean embeddings for optimal control. These methods are finding applications in diverse fields, such as time series forecasting, generative modeling of complex data (images, audio, text), and even guiding complex tasks like surgical procedures, by leveraging the ability to capture inherent uncertainty and complex temporal dynamics. The development of robust and scalable stochastic diffusion models is significantly impacting various scientific domains and enabling new possibilities in data analysis and generation.