Changhun Kim

• Since 03/2025:
  Ph.D. candidate in Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  Project: GridAssist – AI-based techniques for automated congestion control and fault management in electrical power grids.
• 03/2024 – 02/2025:
  Research Assistant, Medical Imaging Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
  Project: Denoising fluorescence microscopy images using self-supervised learning.
• 09/2023 – 02/2025:
  Tutor, Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
  Courses: Advanced Deep Learning, Introduction to Machine Learning.
• 09/2022 – 02/2025:
  M.S. in Artificial Intelligence, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  Thesis: RetNetHTR: Leveraging Retentive Networks for Efficient and Accurate Handwritten Text Recognition.
  Research Assistant: Medical Imaging, Artificial Intelligence in Medical Imaging Lab.
  Teaching Assistant: Advanced Deep Learning, Introduction to Machine Learning.
• 01/2021 – 09/2022:
  Researcher, Urban Big Data and AI Institute, University of Seoul, South Korea.
  Project: Graph Neural Networks for Molecular Toxicity Prediction.
• 05/2021 – 10/2021:
  AI Educational Content Technical Quality Assurance, MODULABS, South Korea.
• 03/2014 – 02/2021:
  B.S. in Computer Science, University of Seoul, South Korea.
  Includes two years of absence due to obligatory military service.
  Undergraduate Research Assistant: Molecular Toxicity Prediction.
• 09/2018 – 12/2019:
  Research Intern, Environmental System Toxicology Lab, University of Seoul, South Korea.
  Project: Machine learning for imbalanced toxicity data prediction.

2024

• Teilvorhaben: KI gestütztes Engpassmanagement und Netzwiederaufbau

  (Third Party Funds Group – Sub project)

  Overall project: GridAssist - Assistenzsysteme für eine optimierte automatisierte Systemführung in Verteilnetzen
  Project leader: Siming Bayer, Gert Mehlmann, Johann Jäger, Matthias Luther
  Term: December 1, 2024 - November 30, 2027
  Acronym: GridAssist
  Funding source: Bundesministerium für Wirtschaft und Energie (BMWE)
  Abstract

  TP3: Automated Optimal System Management in MS/HS Networks & Network Restoration

  AP 3.1: Automated Bottleneck and Fault Management Based on Machine Learning Methods

  AP 3.2: Stable Grid and Supply Restoration

  TP5: Competence Team for Interfaces and Databases

  AP 5.1: Creation of a Knowledge Database for Heterogeneous Power Grid Data Sources

  Automated Grid Management and Restoration through AI and Data Integration

  The increasing complexity and volatility of modern power grids necessitate intelligent, automated systems for optimal operation and rapid restoration. In TP3, we develop AI-driven solutions for automated bottleneck and fault management (AP 3.1) as well as stable and systematic grid restoration (AP 3.2) in medium- and high-voltage (MS/HS) networks. These systems leverage machine learning techniques—ranging from supervised and unsupervised learning to reinforcement learning—with a hybrid training approach incorporating domain knowledge via Physics-Informed Neural Networks (PINNs) and Known-Operator Learning. This ensures reliable, explainable decision-making in real-time operations and minimizes data requirements in safety-critical environments.

  In bottleneck management, predictive and event-triggered AI models autonomously suggest topology adjustments and flexibility deployment, factoring in a wide range of grid constraints and operational targets. Fault management employs multi-source data analysis for real-time fault detection, location, isolation, and resupply, utilizing both existing sensors and proposed novel measurements. For restoration, a dedicated assistance system is developed using a two-stage training process: supervised learning of standardized grid restoration procedures followed by reinforcement learning in a high-fidelity simulation environment. A single-agent model competes across simulation instances to evolve optimized strategies for power system recovery.

  To support these applications, TP5 (AP 5.1) establishes a unified graph-based knowledge database for integrating heterogeneous data sources—from GIS, SCADA, and metering systems—into a cohesive representation. This structure enables complex system queries and machine-readable context for AI models, forming the backbone of advanced operational analytics.

  The entire system architecture and algorithms are tested in AP 6.3 through a hybrid field test utilizing real-time simulation with OPAL-RT and live grid snapshots from Lechwerke’s control center. Assistance systems interface with the simulation via standard protocols, ensuring vendor-independent implementation and practical transferability. Experienced grid operators validate the system in realistic operational conditions, ensuring alignment with the requirements of future grid operations and energy transition goals.

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2020

• LG AI Research Hackathon (Awarded 4th Place)
  Developed an encoder-decoder model that converts molecular images into SMILES.