Résumé/CV

Résumé / CV of Junjie Yin. Use the PDF button on the right to download the full document; a LaTeX template is also available on Overleaf.

Contact Information

Name Junjie Yin
Professional Title PhD Candidate in Electrical Engineering
Email yin@utk.edu

Professional Summary

PhD candidate in Electrical Engineering specializing in AI/ML for power systems, with hands-on experience in power flow, state estimation, grid modeling, data-center planning, and inverter-based-resource (IBR) frequency dynamics. Skilled in physics-informed and deep-learning models — including CNN/DNN surrogates for power-flow prediction and topology-aware N–k contingency analysis. A core developer of the CURENT Large-Scale Testbed (LTB), advancing its AI/LLM-driven simulation; proficient in Python (LangChain, PyTorch, TensorFlow) and power-system tools (OpenDSS, PSS/E, PSCAD, Pandapower, MATPOWER), with strong technical writing and presentation skills.

Experience

  • Aug 2023 – Present

    Knoxville, TN, USA

    Graduate Research Assistant
    CURENT, University of Tennessee, Knoxville
    • Developed and maintained real-time simulation models for bulk-power-system validation as a developer of the CURENT Large-scale Testbed (LTB).
    • Implemented contingency and dynamic disturbance scenarios for operational reliability studies.
    • Supported hardware-in-the-loop (HIL) demonstrations of inverter-based resources and frequency-stability performance.
  • Jun 2026 – Aug 2026

    Redmond, WA, USA

    Research Intern — AI/ML for Electricity-Infrastructure Planning
    Microsoft
    • Developed the Power-Grid Dataset Restoration Agent that uses limited grid information to “build–diagnose–repair–verify–visualize” datasets for hyperscalers’ critical-infrastructure planning.
    • Built a game-theory-based simulation environment for hyperscaler data-center planning, integrating a public-information retrieval agent to design competitive siting and capacity strategies.
    • Designed a data-center interconnection-queue agent that optimizes hyperscalers’ interconnection applications while accounting for ISO interconnection processes and grid-upgrade costs.
  • Aug 2023 – Aug 2025

    Knoxville, TN, USA

    Graduate Teaching Assistant
    University of Tennessee, Knoxville
    • Courses supported: Power System Analysis, Electric Energy System Components, Circuits II.
  • May 2022 – Aug 2022

    China

    Publicist
    Frontiers Journals, Higher Education Press (HEP)
    • Promoted the “Frontiers Journals” English academic series through posters, online forums, and meetings with scholars.
  • Jul 2021 – Jan 2022

    Yangzhou, China

    Graduate R&D Intern
    Yangzhou Electric Power Co., Ltd (State Grid)
    • Analyzed load-overcapacity issues for commercial and industrial customers using peak-demand patterns and contractual limits.
    • Conducted cost-benefit analysis of mitigation strategies (capacity upgrades, penalty payments, shared energy storage).
    • Developed an optimization-based scheduling strategy for shared energy storage to reduce peak demand and operating costs.
  • Jul 2019 – Aug 2019

    Huizhou, China

    Intern
    Huizhou Electric Power Co., Ltd (China Southern Power Grid)
    • Observed real-time dispatch-center operations and substation workflows.
    • Completed electrical-safety and field-safety training aligned with utility operational standards.

Education

  • 2023 - 2027

    Knoxville, TN, USA

    PhD
    University of Tennessee, Knoxville (UTK)
    Electrical Engineering
    • Advisor: Prof. Fangxing “Fran” Li. Graduate Research Assistant at CURENT.
    • GPA 3.93 / 4.0. Expected May 2027.
  • 2020 - 2023

    Nanjing, China

    MS
    Southeast University (SEU)
    Electrical Engineering
    • GPA 3.79 / 4.0 (top 5%).
  • 2016 - 2020

    Beijing, China

    BS
    North China Electric Power University (NCEPU)
    Electrical Engineering
    • GPA 3.73 / 4.0 (top 1%).

Projects

  • LLM-Enabled Power System Simulation and Planning Platform

    U.S. DOE Project · Feb. 2025 – Present

    • Developed an LLM-assisted workflow for power-system simulation, enabling natural-language interaction for grid-data input, parameter configuration, and automated result interpretation.
    • Implemented AI-supported workflows for PV-curve visualization and continuation power flow (CPF) analysis to assess voltage stability and planning-relevant operating margins.
    • Built Python-based DNN/CNN prototypes for surrogate modeling, including load-curve fitting and power-flow prediction on benchmark systems.
  • Grid Resilience Operation & Scheduling

    U.S. DOE Project · Jul. 2024 – Oct. 2025

    • Developed N–k contingency-simulation tools based on a deep variational autoencoder (VAE) to assess system performance under contingency and extreme-event scenarios.
    • Integrated steady-state and dynamic simulations to evaluate system resilience and stability margins.
    • Performed probabilistic contingency assessments to support resilience planning and improve computational efficiency in large-scale grid studies.
  • Microgrid Control & Resilience (ESTCP)

    U.S. DoD Project · Jul. 2020 – Apr. 2024

    • Designed reinforcement-learning (RL)-based V–f and P–Q control strategies for grid-forming (GFM) and grid-following (GFL) converters to enhance stability and resilience under varying grid conditions.
    • Validated on the Large-Scale Testbed (LTB) and Hardware Testbed (HTB) at CURENT.

Selected Publications

  • J. Yin, B. She, X. Feng, et al., “Bridging Artificial Intelligence and Power Systems Education Through Hands-On, Executable Learning Frameworks,” IEEE Transactions on Power Systems, 2026. (Under review)
  • X. Feng, J. Yin, C. Li, et al., “Grid Resilience, Extreme Weather Events, and Clean Energy Development,” Nature Reviews Clean Technology, 2026. (Invited paper, co-first author, under review)
  • J. Yin, X. Feng, J. Han, et al., “Topology-Aware Propagation-Based Assessment of Extreme-Weather Impacts on Distribution System Resilience,” North American Power Symposium (NAPS), 2026.
  • J. Yin, X. Feng, B. She, et al., “VAE-Based Credible Scenario Reduction for Probabilistic N-k Resilience Assessment of Distribution Systems under Extreme Events,” IEEE Transactions on Smart Grid, 2026. (Pending submission)
  • J. Yin, B. She, J. Wang, et al., “Encoding Frequency Dynamics into Economic Operation of IBR-Penetrated Power Systems: Quantification, Integration, and Validation,” IEEE Transactions on Smart Grid, 2025. (Under review)
  • A. Ali, J. Yin, F. Li, et al., “Pathway Toward an Open-Source Ecosystem in Power Systems,” IEEE Electrification Magazine, vol. 13, no. 2, 2025.
  • S. Fahad, B. She, J. Yin, et al., “A Data-Driven Adaptive Control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions,” IEEE Transactions on Power Delivery, 2025.

Invited Talks

  • “AI Applications in Power Systems” — IEEE PES DIU Student Branch Chapter, Jun. 2026.
  • “AI-Enabled Resilient and Interactive Power Grid” — CURENT Industry Conference, Apr. 2026.
  • “Scheduling–Dynamics Interoperated Virtual Power Grid” — CURENT Industry Conference, Apr. 2025.

Professional Service & Leadership

  • IEEE PES AI for Power Systems Coordinating Committee (AIPSCC) — Technical Coordinator (Dec. 2025 – present): support website/communication, coordinate the IEEE course AI for Power and Energy Systems, and lead AI-for-power survey initiatives.
  • IEEE PES Working Group on Machine Learning for Power Systems (MLPS) — Technical Coordinator (Aug. 2023 – Dec. 2025): supported operations, member communications, and general-meeting logistics.
  • Peer reviewer (150+ reviews) for IEEE, Elsevier, and Nature journals — including IEEE Transactions on Smart Grid, IEEE Transactions on Industry Applications, IEEE Open Access Journal of Power and Energy, and IEEE Access (IEEE Access Exceptional Reviewer 2025), among others.
  • Community volunteering — ASPCA (American Society for the Prevention of Cruelty to Animals), animal-welfare initiatives.
  • Student leadership, School of Electrical and Electronic Engineering, NCEPU — Student Leader (2016–2020) and Student Committee member (2016–2019).

Honors and Awards

Certificates

Skills

AI & Programming: Python, PyTorch, TensorFlow, LangChain, Machine Learning, Reinforcement Learning, CNN / GNN, LLM & AI Agents, MATLAB, C
Power-System Modeling & Simulation: PSCAD / EMTDC, PSS/E, OpenDSS, Simulink, PLECS, Pandapower, PYPOWER, MATPOWER
Optimization Solvers: Gurobi, CPLEX, YALMIP, CVX, MILP / OPF / SCUC
Testbed Platforms: CURENT Large-Scale Testbed (LTB), Hardware Testbed (HTB), RT-Box

Languages

English : Professional working proficiency
Chinese (Mandarin) : Native