Mechatronics
Mechanical, electrical, and automation engineering for integrated systems.
PORTFOLIO // 2026
ABOUT
I hold a B.S. in Mechatronics Engineering from Universidad Internacional del Ecuador and an M.S. in Biomedical Engineering from the University of New Haven. My multidisciplinary background spans system design, controls, prototyping, 3D printing, testing, and signal processing. Through biomedical research, I work across the full development cycle from concept and experimentation to implementation turning complex challenges into thoughtful, practical technologies. I am seeking opportunities in medical devices, biomedical R&D, product development, and engineering innovation where I can help create systems that improve patient care and quality of life.
Areas of focus
Mechanical, electrical, and automation engineering for integrated systems.
Instrumentation, device testing, biosensors, and applied biomedical research.
Signal acquisition, electromechanical control, and experimental prototypes.
Background
PROFESSIONAL EXPERIENCE
University of New Haven · West Haven, CT
University of New Haven · West Haven, CT
University of New Haven · West Haven, CT
Coeconst S.A. · Quito, Ecuador
Codetec · Quito, Ecuador
Selected work
FEATURED PROJECTS

Co-authored ASAIO Journal research showing that a 3 wt% silica nanoparticle coating reduced feeding-tube surface fouling by 78% after formula flow.
View case studyAn active research project developing a resin-printed microfluidic platform for controlled nanoparticle formulation and experimental testing.
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A low-cost, tendon-driven robotic hand that uses surface EMG, Arduino acquisition, and MATLAB signal processing to detect muscle contractions and control five servo-actuated fingers.
View case study
A four-degree-of-freedom robotic cocktail platform combining mechanical design, coordinated motion control, and a custom Bluetooth mobile interface.
View case study
My undergraduate mechatronics thesis: a low-cost automated storage system that classified red, blue, and white objects with up to 98% accuracy under controlled lighting.
View case study