Bio

Shiuli Subhra Ghosh is an Electrical Engineer and Data Scientist with 5+ years of experience in power systems analytics and forecasting. Currently at Dominion Energy Virginia, she leads enterprise forecasting platforms for renewable generation and data center load growth. She holds dual master's degrees from RPI and Chennai Mathematical Institute, has published in IEEE Transactions on Power Systems, and holds a U.S. patent. She is interested in energy markets and quantitative research roles.

Highest Level of Education

Master's Degree

Current Job/Study field

TSOs, DSOs

Nationality

India Flag India

Current employer

Dominion Energy

Work experience

  • Engineer
    Dominion Energy - Full-time From February 2025 to Present

    Shiuli leads the development of an enterprise forecasting platform integrating SCADA, weather, and grid data to support transmission planning and system operations. She develops statistical models for day-ahead-to-long-term load and generation patterns, designs probabilistic risk frameworks for outage decisions, architects scalable data pipelines, and models AI for data center load behavior to support infrastructure investment and interconnection studies.

  • Assocaite Manager
    Jindal Stainless - Full-time From July 2018 to January 2021

    Shiuli led a 12+ member team managing medium-voltage electrical maintenance for transformers, switchgear, and underground cables. She worked on improving plant downtime through preventive maintenance, standardized inspections of 33kV equipment. She also served as an internal auditor aligned with ISO standards and developed grade-wise power consumption prediction models using clustering and regression algorithms.

  • Research Assistant
    Rensselater Polytechnic Institute - Part-time From August 2018 to December 2024

    Shiuli worked as a Graduate Research Assistant contributing to causal inference methods for analyzing cascading failure propagation in power transmission networks, in collaboration with IBM Research. She developed a novel unsupervised causal prediction framework using causal discovery algorithms, validated against steady-state power flow solvers and state-of-the-art GNN models, ultimately leading to an IEEE publication and a U.S. patent.

Education

  • Master of Science in Electrical Engineering
    Rensselaer Polytechnic Institute From August 2022 to December 2024
  • B.Tech in Electrical Engineering
    National Institute of Technology Durgapur From August 2014 to June 2018

Area of expertise

Electricity transmission Engineering Economic modelling Energy market models

Languages

  • Bengali
  • English

Links

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