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Pablo G. Morato

Engineering Risk Analysis & Artificial Intelligence

Senior Researcher · Technical University of Munich

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About

Bio

I am a Senior Researcher at the Engineering Risk Analysis (ERA) Group, School of Engineering and Design, Technical University of Munich. Previously, I held postdoctoral positions at TU Delft (2023–2025), the Technical University of Denmark (2023), and the University of Liège (2021–2023), where I also completed my PhD.

My research develops probabilistic and data-driven methods for risk-informed decision-making in engineering systems operating under uncertainty. I work at the intersection of structural reliability, Bayesian modeling, and artificial intelligence, with an emphasis on sequential decision-making, active learning, and life-cycle management of infrastructure systems.

A central theme of my work is the use of Markov decision processes, multi-agent and hierarchical reinforcement learning, and Bayesian neural networks to design adaptive inspection, monitoring, and maintenance strategies. I combine these with uncertainty quantification and reliability analysis to enable decisions under partial observability, model uncertainty, and long time horizons.

My research spans applications in offshore wind energy, critical civil infrastructure, and urban building stock, where scalability, interpretability, and robustness to real-world data are essential.

📍 Theresienstr. 90, 80333 Munich, Germany

✉️ pablo.morato@tum.de

🔗 ERA Group, TUM

Pablo G. Morato

Focus Areas

Research Interests

Risk-Based Decision Modeling

Markov decision processes and partially observable MDPs for adaptive inspection, monitoring, and maintenance planning under uncertainty. Safety calibration and risk-informed optimization of deteriorating engineering systems.

Multi-Agent & Hierarchical Reinforcement Learning

Multi-agent and hierarchical RL frameworks for scalable, coordinated management of large infrastructure networks. Emphasis on interpretability and policy robustness in safety-critical settings.

Bayesian Modeling & Active Learning

Bayesian neural networks and dynamic Bayesian networks for probabilistic inference from engineering data. Active learning methods for efficient experiment design and surrogate-model-based reliability analysis.

Structural Reliability & Safety Calibration

Risk-based inspection planning, fatigue and fracture reliability, and safety calibration for deteriorating structural components under realistic loading and environmental conditions.

Infrastructure Life-Cycle Management

Long-horizon, risk-aware optimization of networked and deteriorating systems, spanning offshore wind farms, critical civil infrastructure, and urban building stock.

Structural Integrity & Wind Energy Systems

Structural health monitoring, virtual load monitoring, and integrity management for offshore wind substructures. Application of probabilistic methods to fatigue assessment under operational and environmental uncertainty.

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Selected Work

Publications

Writing

Blog

Academic Teaching

Teaching Portfolio

I teach and supervise at the intersection of engineering risk analysis, probabilistic methods, and artificial intelligence at TUM and through international collaborations.

Updates

News

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Get in Touch

Contact

Email is the best way to reach me. I typically reply within a few days.

pablo.morato@tum.de

School of Engineering and Design
Technical University of Munich
Theresienstr. 90, 80333 Munich, Germany

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