Yoongun Jung | Network Resilience | Best Research Article Award

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Mr. Yoongun Jung | Network Resilience | Best Research Article Award

Korea University | South Korea

Mr. Yoongun Jung is an emerging researcher in Electrical Engineering, currently pursuing an M.S.–Ph.D. integrated program at Korea University, Seoul (2021–2027), after completing his B.S. in Electrical Engineering from the same institution in 2021. His research focuses on cutting-edge domains including AI applications in power systems, HVDC control, and energy management systems, contributing to intelligent, secure and sustainable power infrastructures. Demonstrating strong academic and research excellence, he has received several prestigious recognitions, beginning with an Excellence Award at the Kepco Creative Innovation Idea Contest in 2020 for his work on deep learning-based frequency response prediction. His impactful research has earned multiple Conference Best Paper Awards, including contributions on adaptive Volt–Var control using multiagent deep reinforcement learning (KIEE PES 2021, Jeju), cost-effective dynamic multi-microgrid formulation (KIEE Power System Research Association, 2023, Jeju), and transient stability data-driven special protection schemes using reinforcement learning (First Best Paper Award, ICRERA 2024, Japan). In addition, he has been recognized at IEEE Student Paper Awards for innovations such as solar power prediction using LGBM (2021), dynamic multi-microgrid formulation using spanning tree algorithms (2022), and federated reinforcement learning-based AGC algorithms, which won the Best Paper Award at the KIEE PES North America Chapter Meeting in 2025. Through his prolific research achievements, Mr. Jung continues to advance intelligent energy systems and contribute significantly to the future of smart power engineering.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

"Enhancing frequency stability with decentralized adaptive control using multi-agent deep reinforcement learning of multi-VSGs", S Kang, Y Jung, D You, G Jang, International Journal of Electrical Power & Energy Systems 172, 2025.

"Cost-Effective automatic generation control for Renewable-dominated grids: Multi-Agent deep reinforcement learning approach", Y Jung, S Kang, J Cha, S Song, M Yoon, G Jang, International Journal of Electrical Power & Energy Systems 173, 2025.

"Coordinate control of multi-infeed VSC-HVDC for enhancing power system reliability", K Kim, Y Jung, M Chang, J Cha, S Kang, H Ku, G Kwon, M Yoon, G, Jang International Journal of Electrical Power & Energy Systems 166, 2025.

"Physics-informed neural network-based VSC back-to-back HVDC impedance model and grid stability estimation", M Chang, Y Jung, S Kang, G Jang, Electronics 13 (13), 2024.

"Transient Stability Data Driven Special Protection Scheme Using Deep Reinforcement Learning", Y Jung, S Kang, K Kim, H Woo, S Choi, G Jang, S Song, M Yoon 2024, 13th International Conference on Renewable Energy Research, 2024.

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), Switzerland📖

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 📄

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.