Roberto Rocchetta | Network Resilience | Best Researcher Award

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Dr. Roberto Rocchetta | Network Resilience | Best Researcher Award

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.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

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.
Research Interests🔬

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

Dr. Roberto Rocchetta is an accomplished researcher with an extensive publication record. He is the first author of more than 10 peer-reviewed journal articles and 15 conference papers, with his works collectively receiving over 450 citations on Google Scholar. His research has achieved significant recognition within the scientific community, leading to an h-index of 8 on Scopus. These metrics reflect his impactful contributions to the fields of uncertainty quantification, reliability engineering, and complex systems analysis. His research continues to influence both academic and practical advancements, demonstrating the broad applicability and importance of his work.

Key Contributions

  • Developed computational frameworks for power grid reliability, vulnerability, and resilience analysis.
  • Contributed to hybrid decision-making methods combining model-based and data-driven approaches.
  • Active reviewer for top journals and technical committees in reliability and risk management fields.

Honours and Awards

  • Humboldt Research Fellowship Award (pending)
  • Best Poster Award, ISIPTA Conference 2021
  • First Prize, Math. Competitive Game 2017 (Monte Carlo approach)
  • Best Paper Award, TU/e Postdoc Best Paper Ceremony

Skills and Software Proficiency

  • Data Analysis & Simulation: MATLAB, Python, Julia, R
  • Energy Systems & Multi-Physics: COMSOL, MatPower
  • Writing & Visualization: LaTeX, JabRef, Mendeley, Office Suite
  • Database Management: Vertica, SQL
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.

Mohammad Ali Saniee Monfared | Robustness | Lifetime Achievement Award

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Assoc. Prof. Dr. Mohammad Ali Saniee Monfared | Robustness | Lifetime Achievement Award

Associate Professor at Alzahra University, Iranđź“–

Dr. Mohammadali Saniee Monfared is a distinguished academic and industry expert with over 20 years of experience across diverse sectors, including manufacturing, automotive, electronics, and cosmetics. His expertise lies in reliability engineering, maintenance planning, and predictive analytics, with a strong focus on turning complex engineering challenges into structured statistical models validated through machine learning techniques. In addition to his extensive industrial background, Dr. Monfared has held esteemed teaching positions at top universities such as Sharif University of Technology, Amirkabir Polytechnic University, K.N. Toosi University, and Alzahra University, where he has guided graduate and undergraduate students across multiple engineering disciplines.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

Dr. Mohammadali Saniee Monfared holds a Ph.D. in Manufacturing and Mechanical Engineering from the University of Birmingham, UK (1997), where he developed advanced methodologies in reliability and system analysis. He earned his first M.Sc. in Industrial Engineering & Operations Research from Sharif University of Technology, Iran (1991), gaining expertise in systems optimization and decision-making models. He further pursued a second M.Sc. in Systems Engineering at the University of Regina, Canada (1994), specializing in system-level design and analysis. This strong academic foundation equipped Dr. Monfared with multidisciplinary knowledge and skills to address complex engineering challenges across industries.

Professional Experience🌱

Dr. Monfared brings over 20 years of professional experience spanning diverse industries, including General Tire and Rubber Manufacturing (8 years), automotive (2 years), electronics manufacturing (2 years), and cosmetic and soap manufacturing (2 years). His industrial work involved solving challenging engineering problems, optimizing production systems, and enhancing operational efficiencies. Notably, his expertise in reliability engineering and predictive analytics has enabled industries to improve system performance, mitigate risks, and ensure process safety. Alongside his industry roles, Dr. Monfared has actively collaborated with organizations, including Iran’s National Gas Company and municipal authorities, on projects such as multi-stakeholder risk assessments, robust maintenance planning, and network vulnerability analyses. His dual experience in academia and industry uniquely positions him to deliver innovative, real-world solutions to complex engineering problems.

Research Interests🔬

Dr. Monfared’s research focuses on:

  • Reliability Engineering
  • Maintenance Planning
  • Complex Networks and System Vulnerability Analysis
  • Predictive Analytics and Machine Learning Applications

Author Metrics

Dr. Monfared has authored impactful papers in renowned journals such as Reliability Engineering & System Safety, Physica A, and Soft Computing. Notable works include:

  • “Topology and vulnerability of the Iranian power grid” (Physica A).
  • “Investigating conflicts in blood supply chains at emergencies” (Soft Computing).
  • “Reliability analysis and optimization of road networks” (Reliability Engineering and System Safety).

His work has garnered significant recognition for its innovative, data-driven solutions addressing real-world challenges in reliability and risk engineering.

Expertise and Skills

  • Data Science and Machine Learning: Neural Networks, Support Vector Machines (SVM)
  • Mathematical Programming
  • Statistical Time Series Analysis
  • State-Space Modeling: Kalman Filters
Publications Top Notes đź“„

1. Network DEA: An Application to Analysis of Academic Performance

  • Authors: M.A. Monfared Saniee, M. Safi
  • Journal: Journal of Industrial Engineering International
  • Volume/Issue: 9 (1), Page 15
  • Year: 2013
  • Citations: 73
  • Summary: This paper applies Network Data Envelopment Analysis (DEA) to evaluate and compare academic performance, providing a systematic approach to assess efficiency in educational settings.

2. Topology and Vulnerability of the Iranian Power Grid

  • Authors: M.A.S. Monfared, M. Jalili, Z. Alipour
  • Journal: Physica A: Statistical Mechanics and its Applications
  • Volume/Issue: 406, Pages 24–33
  • Year: 2014
  • Citations: 55
  • Summary: The study examines the topology of the Iranian power grid using complex network theory, analyzing its vulnerability and critical nodes to improve resilience against disruptions.

3. A Complex Network Theory Approach for Optimizing Contamination Warning Sensor Location in Water Distribution Networks

  • Authors: R. Nazempour, M.A.S. Monfared, E. Zio
  • Journal: International Journal of Disaster Risk Reduction
  • Volume/Issue: 30, Pages 225–234
  • Year: 2018
  • Citations: 43
  • Summary: This research optimizes sensor placement in water distribution networks using complex network theory, enhancing contamination warning systems to mitigate disaster risks.

4. Comparing Topological and Reliability-Based Vulnerability Analysis of Iran Power Transmission Network

  • Authors: Z. Alipour, M.A.S. Monfared, E. Zio
  • Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
  • Year: 2014
  • Citations: 35
  • Summary: The paper compares topological and reliability-based methods for analyzing the vulnerability of Iran’s power transmission network, identifying critical areas to improve reliability.

5. Controlling the Multi-Electron Dynamics in the High Harmonic Spectrum from N2O Molecule Using TDDFT

  • Authors: M. Monfared, E. Irani, R. Sadighi-Bonabi
  • Journal: The Journal of Chemical Physics
  • Volume/Issue: 148 (23)
  • Year: 2018
  • Citations: 33
  • Summary: This study utilizes time-dependent density functional theory (TDDFT) to investigate multi-electron dynamics in high harmonic generation from N2O molecules, offering insights into electron control mechanisms.

Conclusion

Assoc. Prof. Dr. Mohammad Ali Saniee Monfared is highly deserving of the Lifetime Achievement Award due to his exemplary career in both academia and industry. His ability to address real-world engineering problems through a combination of theoretical innovation and practical application has made a substantial impact in the fields of reliability engineering, complex networks, and predictive analytics.

His work in optimizing systems (power grids, water networks, and production systems) demonstrates critical contributions to improving societal resilience and operational efficiency. With continued emphasis on global collaborations and leadership in emerging research areas, Dr. Monfared’s influence will undoubtedly expand, solidifying his legacy as a leader in reliability and network analysis.