0000-0003-2732-3697 About
I am a District Energy and Mechanical Engineer at Jacobs Energy & Power specializing in geothermal thermal energy networks, digital twin modeling, and mission-critical infrastructure.
I work on design and performance modeling of university- and utility-scale geothermal thermal energy networks, including system architecture, borefield sizing, distribution design, and long-term energy balancing strategies to support campus decarbonization and large-scale electrification.
In parallel, I develop physics-based digital twins for data centers using high-fidelity dynamic models to evaluate transient “what-if” scenarios: loss of power, delayed restart, rapid AI-driven load bursts, and thermal instability, to enhance cooling resilience, redundancy performance, and operational recovery.
Ph.D. in Modelica-based simulation and control of district energy systems, with research spanning reinforcement learning-based control, legacy steam optimization, and next-generation energy infrastructure modeling.
Employment
Employment history is unavailable.
Education
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Publications (17)
- Seasonal Thermal Banking: Leveraging BTES to Store and Reuse Campus Waste Heat Save
- Geothermal Exchange and Data Center Integration Result in Golden Opportunities Save
- Enhanced Geothermal System (EGS) Power for AI Data Centers and AI Factories Save
- A Review of Multi-Energy Systems from Resiliency and Equity Perspectives Save
- Modeling resilient multi-energy systems for rural, remote, and disadvantaged communities: A review Save
- Thermo-hydraulic steam pipe models for district heating simulations: Simplifications to balance accuracy and simulation speed Save
- Optimal operation of multi-plant steam district heating systems for enhanced efficiency and sustainability Save
- Computationally Efficient and Accurate Modeling of Combined Heat and Power Systems for District Energy Applications Save
- Exergy-based ecological network analysis for building and community energy systems Save
- Optimizing Operational Costs in Combined Heat and Power Integrated District Heating Systems: A Reinforcement Learning Approach Save