Professional Summary

Automation and Control Engineer, my current objective is to gain experience in developing solutions that integrate machine learning with control techniques. I am particularly interested in applying these skills to optimize control methods for complex systems while improving their reliability and safety.

Education

MSc in Automation and Control Engineering

Politecnico di Milano (09/2021 - 11/2024)

  • Focus: Advanced system modeling and control, electrical drives, industrial automation, system safety.
  • Thesis: Enhancing Energy Flexibility in Electric Vehicle Charging Stations using Reinforcement Learning. Developed an RL framework to reduce charging costs and maximize grid flexibility.
  • GPA: 96/110

BSc in Electronics and Automation Engineering

Valencia Polytechnic University (09/2017 - 07/2021)
Graduated as part of the High Academic Performance group.

  • Focus: Analog and digital circuit design, power electronics, PCB design, electronic instrumentation.
  • GPA: 8.1/10

Skills

  • Programming & Software: Python, C, C++, Git, MATLAB, Octave, Simulink, Parallel Computing
  • Embedded Systems: PCB Design, AVR, STM32, KiCAD, Eagle
  • Machine Learning: Reinforcement Learning, Pytorch, Neural Networks, System Modeling, Electrical Drives, System Safety, Optimization Techniques
  • Control: System Modeling, Electrical Drives, System Safety, Optimization Techniques, advanced control techniques, MPC, Kalman Filtering.
  • Electronics: Analog Electronics, Digital Electronics, Electronic Instrumentation, PCB Design, AVR, STM32, KiCAD, Eagle.

Work Experience

Self-employed - College Prep Tutor

(11/2017 - 05/2020)

  • Tutored high school students for college entrance exams in physics, chemistry, and mathematics.
  • Demonstrated strong time management skills while balancing tutoring with Bachelor’s degree studies.

Valencia Polytechnic University - Research Intern

(09/2020 - 09/2021)

  • Applied clustering and machine learning algorithms using MATLAB to predict blood glucose concentrations in Type I diabetes patients.
  • Integrated predictions into a larger system to prevent hyperglycemic and hypoglycemic episodes, contributing to improved patient care and safety.

Publications

  • “On the Use of Population Data for Training Seasonal Local Models-Based Glucose Predictors: An In Silico Study”
    Aslan A, Díez J-L, Laguna Sanz A J, Bondia J.
    Applied Sciences, MDPI, Vol. 13, No. 9, Article 5348, 25 April 2023.

Languages

  • Spanish (Native)
  • English (B2 – Upper Intermediate Proficiency)
  • Italian (B2 – Upper Intermediate Proficiency)