Profesor Ayudante, Universidad Politécnica de Madrid, Spain
Francisco Pérez Moreno is a dedicated researcher and professor in the field of Aerospace Engineering at the Universidad Politécnica de Madrid (UPM). With a passion for advancing air traffic management and aerospace systems, he has contributed significantly to both academia and industry through his teaching, research, and professional experiences. 🚀📚
Profile
Scopus
Education
Francisco holds a PhD in Aerospace Engineering from the Universidad Politécnica de Madrid (ETSIAE), awarded in November 2023 with the distinction of “Sobresaliente Cum Laude.” He also earned a Master’s degree in Aeronautical Engineering specializing in Aerospace Systems and Air Transport from UPM in July 2021, and a Bachelor’s degree in Aerospace Engineering focusing on Aerospace Science and Technology in September 2019. 🎓✈️
Experience
Francisco is currently a Lecturer (L.d. Ayudante) at the Universidad Politécnica de Madrid, where he teaches courses in Air Traffic Management and Aerospace Systems at the Escuela Técnica Superior de Ingeniería Aeronáutica y del Espacio (ETSIAE). He has been actively involved in multiple research projects aimed at improving air traffic safety and efficiency. 👨🏫🛫
Research Interests
Francisco’s research interests include air traffic management, aerospace systems, machine learning applications in aviation, and the development of predictive models for air traffic safety and efficiency. His work often focuses on the integration of advanced technologies to enhance operational capabilities and safety in aerospace environments. 🔍🧠
Awards
Throughout his academic journey, Francisco has received several scholarships from the Ministry of Education and Vocational Training in Spain, supporting his studies at both the Master’s and Doctoral levels. 🎖️🏅
Publications
Prediction of Capacity Regulations in Airspace Based on Timing and Air Traffic Situation
The Variables with the Greatest Influence on ATM Safety Barriers
Methodology of Air Traffic Flow Clustering and 3-D Prediction of Air Traffic Density in ATC Sectors Based on Machine Learning Models
Determination of Air Traffic Complexity Most Influential Parameters Based on Machine Learning Models
Methodology for Determining the Event-Based Taskload of an Air Traffic Controller Using Real-Time Simulations