Curriculum Vitae

My professional journey

A complete detailed PDF of the CV can be found here.


Expertise

  • Theories: Mathematical Programming, Machine Learning, and Stochastic Optimization
  • Applications: Energy Management, Large-scale Systems’ Decision-Making


Skillset

  • Python (Numpy/Pandas/Matplotlib/Tensorflow/Pyomo/Scikit-learn/Seaborn)
  • MATLAB (Simulink/CVX/Optimization Toolbox)
  • GAMS/AMPL
  • R
  • SQL
  • Git
  • Linux
  • PLEXOS
  • Commercial Solver APIs (Gurobi/CPLEX/Xpress)
  • Microsoft Office Suite (Word/Excel/Powerpoint)
  • AutoCAD
  • PSCAD


Education

  • Ph.D. in Electrical Engineering, Southern Methodist University, 2021
    • GPA: 3.87
    • Outstanding Graduate Award 2021
  • M.S. in Electrical Engineering, Illinois Institute of Technology, 2017
    • GPA: 3.81
  • B.Eng. in Electrical Engineering, Hunan University, China, 2016


Graduate-level Coursework

  • Adaptive Algorithms in Machine Learning (A)
  • Data Analytics and Decision Support (A)
  • Statistical Pattern Recognition (A)
  • Machine Learning in Power Systems (A)
  • Stochastic Optimization (A-)
  • Applied Optimization for Engineering (A)
  • Objective-oriented Programming and Computer Simulation (A)
  • Power Market Operation and Economics (A)
  • Distribution System Analysis (A)
  • Control and Operation of Electric Power Systems (A)


Work Experience


  • Senior Optimization Engineer at Ascend Analytics, LLC
    • Lead on the software refactoring from GAMS to Python-Xpress API to achieve higher computational performance and more flexible user interface.
    • Upgrade and optimize the workflow pipeline between Python and SAS, automating pre-/post-processing within the solution procedure.
    • Develop new features in Python-Xpress to satisfy clients’ needs, involving in tens of unit modules such as battery storage, hydro operation, and multi-energy networks, etc.
    • Communicate with clients and help delivering consulting services with high-dimensional customer data.
    • Renew key functions of the backend code for PowerSIMMTM and SmartBidderTM, both of which are flagship software at Ascend.
  • Postdoctoral Research Fellow at Lawrence Berkeley Lab
    • Predict and analyze the solar generation potential in existing building rooftops by deep learning techniques including Graph Neural Nets.
    • Implement Python programs for automatic I/O data flow with professional simulation software. Develop GUI and SQL databases for open-source power system optimization software.
    • Model and simulate the national power capacity expansion planning and production cost modeling through 2020-2035, considering the power reliability and CO2 emission.
    • Draft research proposals for US DOE and California funding agencies, including the California multiobjective power-eco optimization and 2060 carbon neutrality projects.
    • Mentor interns and visiting Ph.D., host research calls, and write academic papers.
  • Research and Development Fellow at Midcontinent ISO
    • Perform timeseries prediction of power generators’ startup/shutdown trajectory by employing Gradient Boosting Tree for the offline task and Long-Short-Term Memory network for the online task.
    • Design the deterministic MISO real-time look-ahead power supply chain optimization routine and implement in Python with calibration via the MISO production model.
    • Query and parse the timeseries data of units’ startup and shutdown processes by SQL in the MISO private database, with cleaning and restructuring of original data pairs.
    • Induce the improved start/shutdown curves in the stochastic supply chain optimization model on several typical operation days. The daily production cost saving on average is around 8% (million level in dollars).
  • Research Intern at National Renewable Energy Laboratory
    • Build the multi-timescale optimization framework for short-term market analyses in Python with unique steady-state turbine physical models for renewables.
    • Conduct techno-economic assessments of solar panels’ potentials with built-in energy storage systems in the energy & reserve co-optimization, considering the multi-timescale coordination.
    • Debug the simulation software and perform sensitivity analyses with different parameters while adding new and reliable features with validations to better mimic the practical market environment.
    • Help designing the interface between the steady-state scheduling in Python and the transient-state dynamics.