Machine Learning Meets Stochastic Processes: New Trends for Understanding Complex Systems

Dear Colleagues,

Machine learning techniques have been used with great success in multiple scientific and engineering fields, that can leverage large amounts of data. Recently, the interplay between machine learning and stochastic processes has been explored through techniques such as supervised learning methods for determining equations' solutions or unsupervised learning methods for selecting system models. Such techniques can be used in numerous applications, including biological systems, finance, genomics, political analysis, migration flow analysis, and social network analysis.

This Special Issue is open to theoretical and applied contributions joining the fields of stochastic process and machine learning. Foreseen contributions include the following:

• Methods that exploit data samples and time series to improve the efficiency or accuracy of algorithms in order to solve stochastic differential equations, hidden Markov models, and partial differential equations.
• Statistical learning methodologies for diffusion processes and/or point processes.
• Techniques exploring the usage of stochastic process to improve the performance of machine learning techniques, for instance, by providing additional training data.

Dr. Gianni Pagnini
Dr. Santiago Mazuelas
Dr. Alessandro De Gregorio
Guest Editors

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