Chemical engineering · Applied mathematics · Systems biology · Turing patterns
I work at the intersection of chemical engineering, applied mathematics, and computational science — focusing on the modeling and analysis of nonlinear dynamical systems. My research integrates mathematical theory with numerical simulation and machine learning.
| Degree | Institution | Year | |
|---|---|---|---|
| 🔬 | Ph.D. in Chemical Engineering (in progress) | Universidad Autónoma de San Luis Potosí, México 🇲🇽 | 2024 — present |
| ⚗️ | M.Sc. in Chemical Engineering — Graduated with Honors | Universidad Autónoma de San Luis Potosí, México 🇲🇽 | 2024 |
| 📐 | M.Sc. in Applied Mathematics | Universidad de Cienfuegos "Carlos Rafael Rodríguez", Cuba 🇨🇺 | 2020 |
| 🧪 | B.Sc. in Chemical Engineering — Most Outstanding Graduate, Faculty of Engineering | Universidad de Cienfuegos "Carlos Rafael Rodríguez", Cuba 🇨🇺 | 2017 |
| # | Problem | Focus |
|---|---|---|
| 01 | Coupled intracellular–extracellular pattern formation | Turing instabilities, multi-domain coupling |
| 02 | Anomalous diffusion & bifurcation structure | Fractional operators, stability shifts |
| 03 | Efficient simulation of stiff nonlinear PDEs | FEM, FVM, explicit/implicit schemes, method of lines, GPU computing |
| 04 | Bifurcation analysis & dynamical behavior | Limit cycles, chaos detection, numerical continuation (MatCont) |
| 05 | ML for scientific model calibration | AutoML, optimization under uncertainty |
Chemical process simulation
Aspen Plus Aspen Hysys DWSIM
Modeling & programming
COMSOL Multiphysics Python MATLAB Julia GPU/CUDA
Research tools
Zotero VOSviewer ResearchRabbit MatCont COPASI LaTeX Git
Expertise
Mathematical modeling Process simulation CFD Machine learning Bifurcation analysis Numerical methods for PDEs
Languages 🇪🇸 Spanish — native | 🇺🇸 English — B2 (TOEFL)
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Recovering reaction kinetics from plant data: application to the alkaline desulfurization of unstable naphtha Reaction Kinetics, Mechanisms and Catalysis, 2026 · doi:10.1007/s11144-026-03051-x
reaction kineticsparameter estimationprocess datachemical engineeringDevelopment of a methodology to infer kinetic models directly from industrial plant data, applied to desulfurization processes. -
Modeling and prediction of the thermal conductivity of ionic liquids using FLAML as an automated machine learning approach Afinidad. Journal of Chemical Engineering Theoretical and Applied Chemistry, 2025 · doi:10.55815/432728
machine learningionic liquidsthermal conductivityAutoMLApplication of automated machine learning (FLAML) to accurately predict thermophysical properties of ionic liquids. -
Boundary conditions influence on Turing patterns under anomalous diffusion: A numerical exploration Physica D: Nonlinear Phenomena, 2024 · doi:10.1016/j.physd.2024.134353
Turing patternsanomalous diffusionboundary conditionsnonlinear PDEsNumerical investigation of how boundary conditions alter pattern selection and stability in reaction–diffusion systems with anomalous transport.