Engineer and AI researcher with 6+ years of real-time UAS experience through a long-term commitment with a leading European unicorn (Tekever) since high school, while excelling concurrently in rigorous Computer Science & AI studies at top-tier institutions in Europe, America, and Asia.
TEKEVER.
Joined at the age of 16, contributing to the research and development of cutting-edge software solutions for unmanned aerial systems (UAS), focusing on real-time data processing, system optimization and AI; witnessing its significant growth culminating in the company achieving unicorn status in 2025. 🦄
Carnegie Mellon University (CMU), USA.
Supervisors: Dr. João Paulo Costeira (IST) and Dr. Sebastian Scherer (CMU RI).
Focus: 3D Computer Vision.
Pohang University of Science and Technology (POSTECH), South Korea.
Supervisor: Dr. Kwang In Kim, Machine Learning and Vision Group.
Focus: Improving learning-based segmentation models by leveraging depth and uncertainty for enhanced video understanding.
University of Lisbon, Portugal.
Supervisors: Dr. Luís Pinto and Dr. Alan Oliveira de Sá, LASIGE.
Focus: Improving mesh UAV-aided wireless networks QoS with clustering and learning-based power consumption estimates for topological changes.
Instituto Superior Técnico, University of Lisbon, Portugal.
Pohang University of Science and Technology (POSTECH), South Korea.
University of Lisbon, Portugal. Graduated with Academic Distinction.
International Space University, Space Studies Program.
Invited to join by Mr. Claudiu Tăiatu and Mr. Ghanim Alotaibi,
and worked under the guidance of Dr. Maria Cecília Pereira,
contributing to projects in the Department of Space Sciences.
Mechanical Engineering Institute idMEC (Instituto Superior Técnico).
Worked on assembling and controlling autonomous vehicles using cameras as part of a hands-on technical internship.
Institute of Telecommunications, Lisbon.
Completed an internship focused on foundational concepts in telecommunications systems under the guidance of Dr. Custódio Peixeiro.
Space Science & Engineering Foundation.
Promoted science and engineering to young people through industry simulation competitions (Space Design contracts), participating in GSDC (Global), EUSDC (European) and PTSDC (Portuguese) events.
Students Committee of the Department of Informatics (CADI), University of Lisbon.
Initiated projects to empower informatics students, created department clubs, and developed a department website.
Computational Physics and Chemistry Club.
Presented personal ML projects and authored a free technical book to teach programming basics to students.
Earned Academic Distinction in 2023 for remarkable academic performance in the Computer Science and Engineering Course at the Faculty of Sciences, University of Lisbon.
Earned Academic Distinction in 2022 for remarkable academic performance in the Computer Science and Engineering Course at the Faculty of Sciences, University of Lisbon.
Participated and achieved recognition in the Regional Stage of the Portuguese Physics Olympiads in 2020.
Participated and achieved recognition in the Regional Stage of the Portuguese Physics Olympiads in 2019.
A presentation for high school students that demystifies AI buzzwords by breaking them down into core mathematical concepts such as vectors, optimization, and gradient descent, illustrated through practical examples in computer vision, language modeling, and a robotic line-following case study.
An empirical investigation into whether generative AI can be creative, leveraging Boden's taxonomy and conceptual blending. Compares LLM outputs against human creativity on metrics of novelty, coherence, and emergence, revealing distinct creative profiles.
A comparative analysis of optimization techniques (Steepest Descent, SGD, Newton's Method, L-BFGS) for multi-dimensional linear least-squares regression, evaluated on high-dimensional ICVL hand tracking and low-dimensional Student Habits datasets.
A technical article exploring efficient Gaussian Process regression for inverse dynamics in robotics, using the SARCOS dataset. It compares linear and RBF-based GP models against SVR and Random Forests, detailing hyperparameter tuning, computational trade-offs, and predictive performance in a high-dimensional setting.
A presentation on the paper by Tian et al. (2022), covering adversarial attack and defense (Adversarial Training, Defensive Distillation) mechanisms for deep learning models in Unmanned Aerial Vehicles (UAVs).
A talk delivered to high school students, showcasing how foundational math concepts like matrices, vectors, and probability form the backbone of AI/ML models, illustrated through real-world applications in optimization, classification, and computer vision.
Delivered a comparative exploration of biological and artificial minds, addressing theoretical principles of cognition, the implications of AI development, and the proposed "Marcelo’s Test" to evaluate AI’s progression towards human-like intelligence.
A paper exploring the convergence of AI and Cognitive Science to define the boundaries between human and machine minds. It examines foundational principles like knowledge, cognition, and reality, proposing "Marcelo's Test" - a benchmark based on replicating human life experience - to evaluate whether AI can truly match human intelligence, while discussing the implications of such advancements.
A talk presented during the Open Day at the Department of Informatics, discussing the principles of hiding and encrypting information through steganography and cryptography, with practical examples using BMP images and ASCII encoding.