Simone Carlo Surace

University of Bern, University of Zurich

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About

Simone Carlo Surace is a researcher specializing in stochastic processes and statistical inference, with a particular focus on the challenging problem of parameter estimation for partially observed diffusion processes. His major contribution lies in advancing online maximum-likelihood estimation methods for these complex systems, where both a hidden state process and an observed process evolve in continuous time. Surace’s work addresses the critical need for recursive, real-time parameter updates as new data streams in, a problem of significant practical importance in fields such as finance, biology, and engineering. His most-cited paper, "Online Maximum-Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes" (2018), has garnered 20 citations, reflecting its impact on the statistical and applied mathematics communities. An earlier version of this work (2017) also contributes to the foundation of this research. By revisiting and refining these estimation techniques, Surace has provided valuable tools for researchers and practitioners who must infer unknown parameters from incomplete, noisy observations, enabling more accurate modeling and prediction in dynamic, real-world systems.