Marcelo A. Pereyra

Marcelo A. Pereyra is a Professor at the School of Mathematical & Computer Sciences, specifically within the Department of Actuarial Mathematics & Statistics at Heriot-Watt University. His research focuses on advanced computational methods including Bayesian inference, optimization algorithms, and their applications in imaging and inverse problems. He has maintained consistent research output from 2010 to 2025, with notable contributions in machine learning, signal processing, and statistical methodologies.

His work aligns with UN Sustainable Development Goals, particularly in advancing innovation and infrastructure through mathematical and computational advancements. Collaborations span global institutions, addressing challenges in computational imaging, data analysis, and engineering systems. Key methodologies include diffusion models, Markov chain Monte Carlo methods, and proximal algorithms.

Pereyra’s publications emphasize practical applications of theoretical frameworks, such as defect detection in subsea pipelines and photon-starved image restoration. His research bridges statistical theory with computational efficiency, often focusing on Bayesian approaches for complex problem-solving. Grants and advisory roles are not explicitly detailed in the provided text.

He leads interdisciplinary efforts in generative models and empirical Bayesian methods, contributing to both foundational and applied aspects of modern data science. His work frequently involves international collaborations, reflecting a commitment to advancing global research networks in mathematical sciences.