Camilo A. Hurtado E. received his Mechatronics Engineering degree in 2013 (magna cum laude) and also a Telecommunications Engineering degree in 2017 from Universidad Militar Nueva Granada (UMNG) in Bogotá, Colombia. He also holds a Master’s degree in Applied Math in 2021 (cum laude) from Universidad Sergio Arboleda (USA), also in Bogotá, Colombia. Before joining Royal Millitary Academy in the Fall of 2023, he worked as an assistant teacher at Pontificia Universidad Javeriana, Universidad Militar Nueva Granada and Universidad Sergio Arboleda, in Bogotá Colombia, where he taught a variety of subjects related to engineering, data analysis, robotics and AI. In recognition of his academic excellence, Camilo was awarded the “Mario Galán Gómez” scholarship from Ecopetrol in 2008. Additionally, he received several tuition-free semesters between 2008 and 2012.

During 2014 he worked as a research assistant in the Davinci research group at Universidad Militar Nueva Granada. Later that year, he founded his own 3D printing company called “Kóndoro” which operated from 2014 to 2021. In 2015 he was part of the Huawei academic program “Seeds for the future”. Between 2015 and 2016 he participated in an academic exchange program with Universidad Politécnica de Madrid (UPM). Between 2017 and 2018 he worked as project manager in the non-profit organization “Materialización 3D” (M3D), whose objective was to train people, with limited resources and/or functional diversities, in emerging technologies. In 2018 he started his Master’s degree, and between 2019 and 2023 he worked as university teacher.

Camilo’s research interests have been around modular robotics, 3D printing and bioinformatics, during his bachelor degree, his own company and his master’s degree, respectively. During his master’s degree he worked on generating pseudoknotted RNA skeleta using the combinatorics of Catalan objects and irreducible chord diagrams. In his PhD, he is working on the detection and classification of buried man-made objects in an underwater environment using low-frequency SONAR data and Physics-Informed Machine Learning