Harrish Joseph
PhD Researcher bridging the frontiers of Scientific Machine Learning, Spacecraft Dynamics, and Complex Systems
I'm a PhD researcher at Sapienza University of Rome, specializing in Scientific Machine Learning for nonlinear dynamical systems. My work focuses on developing novel deep learning architectures—Neural Operators, Neural ODEs, and Temporal Fusion Transformers—for parameter identification and structural health monitoring.
With a background in Aerospace Engineering and space missions, I bring an interdisciplinary perspective to understanding complex systems, from spacecraft dynamics to evolutionary biology, always asking the fundamental questions about emergence, chaos, and the meaning encoded in dynamic patterns.
Journey Through Space & Time
Research Impact
Expertise Areas
Scientific ML
Neural Operators, Physics-Informed Neural Networks, Autoencoders, GANs, and Temporal Fusion Transformers for dynamical system analysis.
Space Systems
Spacecraft dynamics, orbital mechanics, mission analysis, and space navigation systems. Experience with JUICE mission and AGI STK.
Nonlinear Dynamics
Parameter identification, structural health monitoring, damage detection in complex hysteretic systems like Duffing oscillators.
Robotics & Control
Dynamic modeling, trajectory planning, Extended Kalman Filtering, and closed-loop control for manipulators and autonomous systems.
Skills Constellation
Machine Learning
Programming
Scientific Computing
Aerospace Tools
Engineering Design
Version Control
Publications
Deep learning architectures for data-driven damage detection in nonlinear dynamic systems
H. Joseph, G. Quaranta, B. Carboni, and W. Lacarbonara
Nonlinear Dynamics, vol. 112, no. 23, pp. 20 611–20 636, Dec. 2024
View Publication →+ Conference proceedings and collaborative research papers
Let's Explore the Cosmos Together
Interested in collaborating on research at the intersection of machine learning, dynamical systems, and space exploration? Let's connect and push the boundaries of what's possible.
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