Software Engineer · Data Engineering · Scientific Computing · ADAS Validation
Software engineer with 8+ years of combined experience in data-intensive software development, scientific computing, and computational modeling. I build Python and C++ tools, automated data-processing workflows, and analytical solutions for complex engineering systems. I hold a PhD in Mathematical Sciences and am growing my specialization in machine learning, computer vision, and MLOps.
Querétaro, Mexico · Open to remote and hybrid opportunities
Portfolio · LinkedIn · Google Scholar · Email
- Software and data engineering: Python, C++, SQL, data processing, ETL concepts, data integration, data quality, and validation
- ADAS validation and testing: Radar, camera, LiDAR, DGPS, ground-truth correlation, event analysis, and test automation
- Scientific computing: Mathematical modeling, numerical methods, computational simulation, NumPy, and SciPy
- Machine learning: Data analysis, computer vision, model evaluation, and reproducible workflows
- Tools and infrastructure: Git, Linux, Docker, Kubernetes, Jenkins, and CI/CD
May 2022–Present · Querétaro, Mexico
- Designed Python data-processing pipelines integrating radar, camera, LiDAR, and DGPS data for large-scale ADAS validation.
- Built workflows to correlate sensor outputs with ground truth and identify data inconsistencies and performance issues.
- Developed reusable root-cause analysis pipelines for false-positive and false-negative events across ADAS functions.
- Applied computer vision and machine learning to automate event detection, image labeling, and behavioral analysis.
- Reduced manual image data-preparation effort by approximately 95%.
- Replaced an external validation software solution, eliminating approximately $140,000 USD in licensing costs.
CONACYT-SENER Hydrocarbon Sector Fund, Project 168638 · 2012–2014 and Nov 2016–Feb 2018
- Developed C/C++ scientific software with WinAPI/OpenGL interfaces and real-time visualization for fractured and vuggy reservoir simulation.
- Implemented analytical and numerical methods for triple-porosity and triple-permeability models.
- Applied topological and geometric analysis to derive porosity and permeability properties from pore-network characteristics.
- Collaborated with petrophysicists, physicists, and mathematicians to formulate and validate computational models.
- ADAS Perception — Experimental ADAS object-detection work using YOLO11n and automotive datasets, with attention to reproducible experiments, evaluation, and error analysis.
- SimuCaS-V1 — C++ application for robotic manipulator kinematics and 3D visualization with OpenGL.
Selected articles and research outputs:
- Spatial Fractional Darcy’s Law on the Diffusion Equation with a Fractional Time Derivative in Single-Porosity Naturally Fractured Reservoirs — Energies, 2022
- Fractional Growth Model with Delay for Recurrent Outbreaks Applied to COVID-19 Data — Mathematics, 2022
- Fractional Growth Model Applied to COVID-19 Data — Mathematics, 2021
- Fractional Vertical Infiltration — Mathematics, 2021
- Quasi-Analytical Model of the Transient Behavior Pressure in an Oil Reservoir Made Up of Three Porous Media Considering the Fractional Time Derivative — Mathematical and Computational Applications, 2020
- Doctoral thesis: Acoplamiento de la derivada fraccionaria espacial y temporal en la ecuación de flujo con triple porosidad y triple permeabilidad — UNAM, 2023
- Bachelor’s thesis: GPU Math: Biblioteca para el uso computacional de la GPU — UAQ, 2013
- Conference paper: Infiltración del agua hacia el suelo desde un tubo enterrado con pared porosa — COMEII, 2021
See my Google Scholar profile for the broader publication list.
- PhD in Mathematical Sciences — National Autonomous University of Mexico (UNAM), 2018–2023
- MSc in Mathematical Engineering — Autonomous University of Querétaro (UAQ), 2013–2015
- BSc in Applied Mathematics — Autonomous University of Querétaro (UAQ), 2007–2011
- Spanish — Native
- English — Upper-intermediate (B2; EF SET 58/100)