About me:
PhD in Physics with deep expertise in Bayesian inference, real-time estimation, and mathematical modeling, applied across domains such as electromechanical systems, robotics, and biomedical data analysis.
Embedded Files
What I have been working on the past 2 years:
- Software Engineer – Algorithm Specialist at Guidance Marine. Developing sensor fusion algorithms, with a focus on real time embedded software development.
- Senior Battery Controls Engineer at Brill Power. Developing novel techniques for the estimation of batteries' state of charge, state of health, state of energy, and state of power via real-time estimation algorithms and multi-objective optimisation tools.
- Estimation theory and decision making algorithms
- My YT channel
Short Bio:
Adrian-Josue Guel-Cortez received the B.Eng. degree in mechatronics engineering and the M.Eng. degree in electrical engineering, with a specialization in control, from the Autonomous University of San Luis Potosí, San Luis Potosí, Mexico, in 2016 and 2018, respectively, and the Ph.D. degree in physics from Coventry University, Coventry, U.K., in 2023. His doctoral research focused on information geometry and information theory for the analysis and control of stochastic dynamical systems, integrating model predictive control and advanced numerical methods.
In 2023, he was a Research Fellow with the Living Systems Institute, University of Exeter, Exeter, U.K., where he investigated statistical and computational methods for biomedical time series analysis and single-cell RNA sequencing. From 2023 to 2025, he was a Senior Battery Controls Engineer with Brill Power, Oxford, U.K., developing real-time state estimation and optimization algorithms for battery management systems, including state-of-charge and state-of-health estimation. Since June 2025, he has been an Embedded Software Engineer specializing in algorithms with Guidance Marine, Leicester, U.K., where he develops sensor fusion, object tracking, and navigation algorithms for marine applications. His work integrates radar and satellite positioning data with simultaneous localization and mapping, supported by simulation and reproducible computational workflows. His research interests include Bayesian inference, real-time state estimation, mathematical modeling, stochastic systems, information geometry, model predictive control, optimization, and fractional-order systems. He develops scientific and embedded software using Python, C++, and MATLAB.
My PhD thesis aimed to develop a unified framework for understanding and controlling complex systems by integrating concepts from information geometry, stochastic thermodynamics, and control engineering. From information geometry, I established a metric structure to quantify the variability and sensitivity of complex systems subject to randomness. From control engineering, I incorporated the notion of feedback to design suitable control algorithms capable of organizing and stabilizing system dynamics. Since efficiency is fundamentally a thermodynamic concept, stochastic thermodynamics was used to analyze the regulator’s influence on entropy production and its relation to Helmholtz free energy. Moreover, the combined use of information geometry and stochastic thermodynamics provided tools to detect abrupt transitions and identify correlations among variables in stochastic dynamical systems.
My Master’s thesis aimed to develop a theoretical and practical framework for modeling and controlling fractional-order systems within the context of linear control theory. From fractional calculus, I introduced operators capable of describing system dynamics with memory and hereditary effects, extending the classical notion of differentiation to non-integer orders. From linear systems theory, I recovered stability and frequency-domain analysis tools to characterize the behavior of fractional-order transfer functions and to construct equivalent representations for infinite-dimensional systems. From control engineering, I designed and tuned fractional-order controllers, such as PIλ and PDμ, demonstrating their ability to improve robustness, noise filtering, and performance compared to conventional integer-order schemes. Moreover, the integration of fractional modeling and control revealed new insights into the organization of dynamic behavior across scales and provided a foundation for implementing fractional controllers in practical systems, such as power converters.
My Podcast (in Spanish)
My Podcast (in Spanish)
Google Sites
Report abuse