Summary
- ●Scientific Researcher: Specializing in Neural Operators, Neural ODEs, and deep learning for nonlinear dynamics.
- ●Space Systems Engineer: Background in orbital mechanics, mission analysis (JUICE), and spacecraft GNC.
- ●Builder: Passionate about creating interactive tools that make complex physics accessible and actionable.
What I Build
Scientific ML
Neural Operators and ODEs for modeling complex dynamical systems faster than traditional solvers.
Space Systems
Real-time ephemeris visualization and trajectory analysis tools for mission planning.
Robotics & Control
Inverse kinematics solvers and PID control simulations for robotic manipulators.
Experience
PhD Researcher
2022-PresentSapienza University of Rome. Specializing in Scientific Machine Learning for nonlinear dynamical systems.
- ▹Developed novel Temporal Fusion Transformer architecture for structural health monitoring.
- ▹Published in Nonlinear Dynamics (Impact Factor: 5.6).
Research Engineer
2022Focused on deep learning optimization for mechanical systems.
- ▹Reduced Bouc-Wen parameter identification error by 25% using evolutionary algorithms.
- ▹Built metamaterial vibration prediction models with 90% accuracy.
Masters in Space Engineering
2017-2021Sapienza University of Rome. Thesis on JUICE mission range delay.
- ▹Modeled range delay for ESA's JUICE mission tracking systems.
- ▹Specialized in Space Robotics and Space Flight Mechanics.
Selected Research
Deep learning architectures for data-driven damage detection
H. Joseph, G. Quaranta, B. Carboni, W. Lacarbonara • Nonlinear Dynamics (2024)
Proposed a novel deep learning framework for identifying structural damage in hysteretic systems, consistently outperforming traditional methods in noise robustness.
Read PaperLet's Connect
I'm always open to discussing research collaborations, engineering challenges, or new opportunities in scientific AI.