Biographical Sketch

Results-driven engineer with deep expertise in machine learning, utility system planning, renewables integration, distribution protection, and smart grid technologies. A multidisciplinary background spanning government research, consulting, software, and utilities (large and small) enables successful delivery of complex projects from concept to deployment while fostering high-performing, values-driven teams.

MSEE, University of Washington; BSEE, Georgia Tech—both focused on power systems.

Invented ATILDA, a novel method for load characterization and forecasting, now optimizing electricity plan selection for Texas consumers. Developed the Self-Configuring Reclosing Relay (SCRR) and co-invented the first Machine Learning Relay (MLR), pioneering AI-driven utility protection.

Lead with C4E (Capable, Committed, Collaborative, Cooperative, and Empathetic), a values-based approach to building cohesive, high-performing teams that consistently exceed expectations. 

EDUCATION

2007   University of Washington, Seattle, WA, MS Electrical Engineering (Power Systems & Micro-fabrication)

2000   Georgia Institute of Technology, Atlanta, GA, BS Electrical Engineering (Power Systems & Mechanical Engineering

Currently Working On

Rate-bliss Website

• A website leveraging Texas customer usage data and ATILDA to help customers identify the least cost electricity plans for their home.

Self-Configuring Reclosing Relay

• An electric power distribution system reclosing relay that performs its own protection study

Machine Learning Relay

• A protective relay that uses machine learning techniques. The ML technique is related to speech recognition, but it is applied to power systems.

Apportioned Time Intermediate and Long Term Data Analysis (ATILDA)

• Modeling electrical usage “load” information in the form of a polynomial equation. This is used for electric power system distribution planning.

ATILDA Weather Sensitivity

• Finding an equation that accurately represents the relationship of electrical usage to ambient temperature. This is used for electric power system distribution planning and forecasting.

Voltage-Drop Power Flow

• Leveraging ATILDA polynomials to perform power flow studies where an entire year is analyzed in a single run of power flow.