Aspiring Robotics Software Engineer - Research Innovator
Resume:
Aidan Kirby Copinga
github.com/catamay
linkedin.com/in/aidan-copinga
*********@*****.***
Pittsburgh, PA
Education
Carnegie Mellon University August 2022 – December 2024 Masters of Science in Mathematical Sciences Pittsburgh, PA University of Utah August 2019 – May 2022
Honors Bachelors of Science in Applied Mathematics with CS minor Salt Lake City, UT Experience
Research Scientist - Biomechanics & Robotics June 2025 – Present Allegheny Health Network Pittsburgh, PA
• Lead overdue maintenance and controls framework overhaul for 8 axis 6 DOF spine tester for validation and research of spinal implants.
• Consolidated and organized over 100 intact spine kinematics data from 15 years of testing using Pandas.
• Wrote study protocols and helped in leading 4 industry studies and 2 resident projects planned for 2026, compared to 1 biomechanical industry study performed in the lab in 2025 prior to my entry.
• Redesigned and documented data and statistical analysis code for spine tester analog and camera data in Python including an overhaul of calculation frameworks for spinal kinematics that have not been studied in the lab since 2015. Undergraduate Robotics Researcher - Surgical Robot Design Optimization May 2020 – May 2022 University of Utah Kahlert School of Computing Salt Lake City, UT
• Research at Kuntz Lab (previously AI and Robotics in Medicine Lab) on optimization of surgical robot design
• Implemented inverse-kinematics and motion profiling based cost functions to design continuum robots within 0.01mm2 total error using gradient-free optimization techniques. Produced 2 papers with one in the 2022 Hamlyn Symposium for Medical Robotics.
• Fast-paced research environment utilizing state of the art foundations in ROS2, control design, motion planning, C++, and C. Research Intern - Anomaly Detection January 2022 – May 2022 Idaho National Laboratories Remote
• Mentored by Jacob Farber at INL during his root cause analysis project.
• Using data-driven techniques, root cause analysis on a simulated spring mass damper system and nuclear plant data was performed to determine the locations of faults in systems and differentiate sensor and system faults.
• Unsupervised learning and time series analysis methods detected system faults in simulated and plant data to within 80% accuracy compared to previous results where root cause analysis was only to within 50%.
• Required the use of standard scripting languages such as Python as well as Python libraries including Pandas, Scikit-Learn, and Scipy. Graduate Teaching Assistant - Analysis and Computational PDEs August 2022 – December 2024 Carnegie Mellon University Pittsburgh, PA
• Instructed over 300 students with a 4.5/5 rating in intro to advanced math courses including, but not limited to, computational partial differential equations, real analysis, and multivariable calculus.
• Created new recitation coding notebooks for 21469: Computational Introduction to Partial Differential Equations to Standardize the Course Labs for Numerical Analysis and Scientific Computing for 40 students. Projects and Certifications
Reinforcement Learning and Safe Lyapunov Control of Simulated Environments October 2024 – December 2024 Carnegie Mellon University Pittsburgh, PA
• Reproduced reinforcement learning driven by safe Lyapunov certificate functions in OpenGym in the half-cheetah environment to control with a speed and rotation barrier function to limit speed and decrease tendency for the model to fall over in training. Kinova 6 DOF Robot Arm Simulation December 2024 – January 2025 Carnegie Mellon University Pittsburgh, PA
• Wrote controllers for LQR (Linear Quadratic Regulator), MPC (model predictive control), ILC (iterative learning control) for a double integrator 6 dimensional model for a Kinova Gen 3 6DOF Robot Arm. Additionally designed simulated Kalman filters and LQG (Linear Quadratic Gaussian) controllers in the same notebook. Johnson & Johnson Robotics and Control Job Simulation Certification December 2025 Forage Online
• Used Python-based tools to diagnose control system inefficiencies, identify root causes of delays, and implement targeted optimizations.
• Proposed actionable design modifications using annotated technical visuals, validating their impact on responsiveness and durability through iterative testing. Developed a professional design proposal outlining findings, solutions, and recommendations for improving precision and reliability in robotic systems.
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