Dr Eng at SUPSI – Department of Environment, Construction, and Design (DACD), Switzerlandđź“–
Dr. Roberto Rocchetta is a post-doctoral researcher at the Technical University of Eindhoven (TU/e), collaborating with Signify, BMW, Philips, and the Department of Mathematics and Computer Science. His multidisciplinary expertise spans uncertainty quantification, reliability engineering, machine learning, and energy systems. Dr. Rocchetta has made significant contributions to power grid resilience, optimization, and vulnerability analysis, with over 10 peer-reviewed journal articles and 15 conference papers. He has worked at NASA Langley and National Institute of Aerospace (NIA) and has held academic positions at several prestigious institutions.
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Education Background🎓
- Ph.D. in Reliability Engineering, Uncertainty Quantification, and Computer Science, University of Liverpool, UK (2015-2019)
- Master of Research in Decision-Making under Risk and Uncertainty, University of Liverpool, UK (2014-2015)
- Master’s and Bachelor’s in Energy Engineering, University of Bologna, Italy (2008-2014)
Professional Experience🌱
- Postdoc, TU/Eindhoven & Signify, Department of Mathematics and Computer Science (2021-Present): Focus on LEDs reliability, survival analysis, and experimental design.
- Postdoc, TU/Eindhoven & Philips, Department of Mathematics and Computer Science (2019-2021): Focus on AI and machine learning for MRI maintenance optimization.
- Research Scholar, NIA and NASA Langley, USA (2017-2018): Focus on data-driven reliability and robustness optimization.
- Visiting Ph.D. Candidate, ETH Zurich and Milan Polytechnic (2017-2018): Focus on energy systems and risk analysis.
- Intern, ARAMIS Start-Up, Milan (2017): Focus on reinforcement learning for maintenance optimization.
Dr. Rocchetta’s research interests lie in uncertainty quantification, resilience and reliability analysis of complex systems, stochastic optimization, machine learning, and data-driven decision-making. His work has a strong focus on power grids, energy systems, and network modeling, with an emphasis on optimizing and quantifying uncertainties in critical infrastructures.
Author Metrics
Publications Top Notes đź“„
1. A Reinforcement Learning Framework for Optimal Operation and Maintenance of Power Grids
- Authors: R. Rocchetta, L. Bellani, M. Compare, E. Zio, E. Patelli
- Journal: Applied Energy
- Volume: 241
- Pages: 291-301
- Year: 2019
- Citation Count: 240
- Focus: The paper introduces a reinforcement learning framework for the optimal operation and maintenance of power grids, addressing challenges in decision-making under uncertainty.
2. On-line Bayesian Model Updating for Structural Health Monitoring
- Authors: R. Rocchetta, M. Broggi, Q. Huchet, E. Patelli
- Journal: Mechanical Systems and Signal Processing
- Volume: 103
- Pages: 174-195
- Year: 2018
- Citation Count: 130
- Focus: This paper discusses the use of Bayesian model updating techniques for improving the reliability of structural health monitoring systems, emphasizing real-time performance adjustments.
3. Risk Assessment and Risk-Cost Optimization of Distributed Power Generation Systems Considering Extreme Weather Conditions
- Authors: R. Rocchetta, Y. Li, E. Zio
- Journal: Reliability Engineering & System Safety
- Volume: 136
- Pages: 47-61
- Year: 2015
- Citation Count: 123
- Focus: The research focuses on risk assessment methodologies and the optimization of risk-cost balances for distributed power generation systems, particularly under extreme weather conditions.
4. A Power-Flow Emulator Approach for Resilience Assessment of Repairable Power Grids Subject to Weather-Induced Failures and Data Deficiency
- Authors: R. Rocchetta, E. Zio, E. Patelli
- Journal: Applied Energy
- Volume: 210
- Pages: 339-350
- Year: 2018
- Citation Count: 96
- Focus: This paper introduces a power-flow emulator approach designed to assess the resilience of power grids, accounting for failures caused by weather events and the challenges of insufficient data.
5. Assessment of Power Grid Vulnerabilities Accounting for Stochastic Loads and Model Imprecision
- Authors: R. Rocchetta, E. Patelli
- Journal: International Journal of Electrical Power & Energy Systems
- Volume: 98
- Pages: 219-232
- Year: 2018
- Citation Count: 68
- Focus: This research assesses the vulnerabilities of power grids by incorporating stochastic load behavior and the imprecision in modeling, contributing to a more robust understanding of system reliability.
Conclusion
Dr. Roberto Rocchetta is an outstanding researcher whose work significantly contributes to the fields of uncertainty quantification, resilience analysis, and optimization of power grids. His impactful publications, strong interdisciplinary expertise, and real-world relevance of his research make him a strong contender for the Best Researcher Award. His ability to collaborate across academic and industrial boundaries, along with his recognition in the scientific community, further strengthens his candidacy. By expanding his collaborations and industry engagement, Dr. Rocchetta has the potential to elevate his impact even further, making substantial contributions to the global energy sector.