Trinity Rutling

• Designed controllers for dynamic systems using MATLAB and Python, focusing on stability, transient response, and steady-state
error minimization
• Developed and tuned proportional-integral-derivative (PID) controllers to meet specified performance criteria, including
overshoot, settling time, and damping ratio
• Analyzed and simulated system behavior through Bode, Root Locus, and Nyquist plots for transfer functions relevant aircraft or
satellite positioning

Designed and implemented Human-Machine Interfaces (HMIs) for new process equipment, improving operator visibility and
reducing manual response time by approximately 25% during critical operations
• Programmed I/O modules to transmit control signals to the Distributed Control System (DCS), increasing overall plant
production viability by supporting higher outputs of newly formulated chemicals
• Configured PID-based control modules within the DCS to regulate key process variables such as flow, temperature, and
pressure, enhancing automatic response ability to disturbances
• Built a PI Asset Framework to structure and contextualize process data into Key Performance Indicators (KPIs) accessible
through a web-based interface, enabling easy accessibility to performance trends company-wide
• Leveraged data lake KPIs to perform cross-plant performance assessments, identifying areas for optimization contributing to
data-driven decision-making
• Monitored, tuned, and analyzed control loops to ensure stable system behavior, maintaining safety, reliability, and
responsiveness during process upsets or power interruptions

• Designed and implemented Human-Machine Interfaces (HMIs) for new process equipment, improving operator visibility and
reducing manual response time by approximately 25% during critical operations
• Programmed I/O modules to transmit control signals to the Distributed Control System (DCS), increasing overall plant
production viability by supporting higher outputs of newly formulated chemicals
• Configured PID-based control modules within the DCS to regulate key process variables such as flow, temperature, and
pressure, enhancing automatic response ability to disturbances
• Built a PI Asset Framework to structure and contextualize process data into Key Performance Indicators (KPIs) accessible
through a web-based interface, enabling easy accessibility to performance trends company-wide
• Leveraged data lake KPIs to perform cross-plant performance assessments, identifying areas for optimization contributing to
data-driven decision-making
• Monitored, tuned, and analyzed control loops to ensure stable system behavior, maintaining safety, reliability, and
responsiveness during process upsets or power interruptions

• Conducted nano-indentation mechanical loading tests on spongey bone-tissue and cells to observe material properties, aiding in
the development of a bone-like material
• Utilized Python to measure the surface area of nano-structured bone-cell scaffolds, aiding in cellular encasement for uniaxial
loading experiments
• Organized data from cell scaffold measurements and mechanical load tests using Microsoft Excel and Python
• Research