Head Data Scientist • Professor • ML Engineer
10+ years combining PhD-level problem-solving with MLOps engineering. Leading AI at DigestAID and teaching at University of Porto.
View My Work
My research and engineering work spans the intersection of mechanics, data science, and biomedical innovation.
Investigated the mechanical properties of biological tissues under complex loading conditions. Developed experimental protocols for tissue testing and advanced curve fitting algorithms for data analysis.
Analyzed cell mechanics using Atomic Force Microscopy (AFM). Created a robust MATLAB library for processing force-distance curves and extracting mechanical properties from indentation data.
Modeled force transmission in living matter using Finite Element Analysis. Developed custom User Material Subroutines (UMAT) in ABAQUS to simulate complex biological material behavior.
Leveraged machine learning to predict the mechanical behavior of composite laminates. Replaced computationally expensive simulations with fast, accurate surrogate models.
Applied deep learning techniques to solve elasticity problems. Trained neural networks to approximate stress-strain fields, significantly accelerating the design process.
Developed a synthetic data generator to create large datasets of material properties. Enabled the training of robust ML models for material science applications.
Engineered AI algorithms for detecting anomalies in capsule endoscopy video feeds. Improved diagnostic accuracy and reduced review time for gastroenterologists.
Developed signal processing pipelines for high-resolution manometry. Automated the classification of esophageal motility disorders using advanced time-series analysis.
Implemented real-time computer vision systems for standard endoscopy. Assisted clinicians in identifying polyps and other lesions during procedures.
Explored evolutionary strategies for solving complex optimization problems. Implemented genetic algorithms to find optimal solutions in high-dimensional spaces.
A comprehensive collection of algorithms implemented in Python and MATLAB. Covers topics from numerical methods to data structures and sorting.
Applied pose estimation models to analyze climber movements. Extracted kinematic data to improve technique and performance in rock climbing.
I'm currently Head Data Scientist at DigestAID, where I built and lead a team of 8 data scientists, spearheading technical architecture that secured $10M in funding. I co-authored 7 patents for computer vision algorithms in medical imaging and navigated FDA/EMA submissions with ISO13485 compliance for Software as a Medical Device (SaMD), delivering 4 products to regulatory approval.
I also serve as an Invited Auxiliary Professor at the University of Porto, teaching computer programming, calculus, and mechanical design while supervising 30+ theses and 2 PhD candidates.
My expertise spans AI/ML engineering, MLOps, and regulatory compliance in healthcare. I combine PhD-level research (Mechanical Engineering, University of Porto) with practical engineering, having published 70+ peer-reviewed papers, secured €1M+ in research grants, and completed a Fulbright research fellowship at Cleveland Clinic.
Tech Stack: Python, PyTorch, scikit-learn, PySpark, SQL, Docker, Terraform, GCP, AWS, VertexAI