• Developed software for power management of ECUs in vehicles• Wrote multi-threaded C and C++ code for embedded systems• Collaborated with cross‑functional teams to design and deploy Adaptive AUTOSAR solutions• Created a comprehensive unit testing framework that used stubs, mocks, and dependency injection• Technologies: C++, C, Adaptive AUTOSAR, GoogleTest, gMock, CMake, Linux, Yocto, Bash, Git, Github, Docker

• Developed a tool that emulates real‑time data playback for automotive subsystems• Wrote multi‑threaded, modern C++ code for embedded QNX on i.MX8• Contributed to a large code base that used CI/CD principles• Had all code reviewed and critiqued by a senior developer• Worked closely with testers to find and fix bugs• Received the second highest co‑op evaluation: Excellent• Technologies: C++, QNX, Linux, Bash, Python, Git, Docker, Gitlab CI/CD, JIRA, GoogleTest, JSON

• Created an interactive mock‑up of an automotive dashboard to highlighting innovative features for a demo• Used Android Studio’s XML layouts and objects for scalable and programmatic UI generation• Received the second highest co‑op evaluation: Excellent• Technologies: Python, Android Studio, Java, XML

• Independently created, trained, and deployed deep neural networks for automatic first break picking in 2D and 3D seismic data with 99.64% validation accuracy• Researched papers on first break picking using computer vision, convolutional neural networks, and transfer learning• Prototyped in Python using machine learning, statistical analysis, and linear algebra packages including TensorFlow and Numpy• Prepared terabytes of data for input to the neural network with procedures including standardization via time gain correction, first break flattening using linear moveout, and upsampling with fast Fourier transform• Accelerated model training and prediction via GPUs and parallel computing• Authored a detailed report on the challenges and advancements in first break picking as an image segmentation task• Received the highest co‑op evaluation: Outstanding• Technologies: Python, TensorFlow, Numpy, CUDA, Bash

• Automated the conversion process of OpenStax textbooks to DigitalEd’s XML format• Created interactive math/physics problems using Maple programming• Received the second highest co‑op evaluation: Excellent• Technologies: Python, XML, Selenium WebDriver, Maple