Alfred T. Email and Phone Number

• Collaborated within an embedded systems team in a partnership between Northrop Grumman, Cal Poly SLO, and Cal Poly Pomona to develop software for two custom UAVs for search and rescue.• Implemented computer vision to detect objects at up to 190 ft• Integrated Pixhawk PX4 with computer vision implementation to calculate real world coordinates of the detected object. The coordinates were calculated using an inverse haversine formula, trigonometry, and vector math to translate between the different frames of reference of the camera frame and the real-world frame.• Integrated computer vision with autonomous flight using Python’s asyncio library• Researched YOLOv5 implementation and optimization with TensorRT on Jetson Nano• Utilized OAK D-W camera for running high-performing computer vision inference• Integrated object detection and coordinate processing with Cal Poly Pomona's ground control system• Performed flight demonstration for Northrop Grumman in June, alongside Cal Poly Pomona.

• Developed advanced software for Quanser QCar self-driving cars under the supervision of Dr. Siavash Farzan• Collaborated within a multidisciplinary team of Computer and Electrical Engineering undergraduate students, gearing up for the 2024 American Control Conference Self-Driving Car Student Competition• Spearheaded the implementation of YOLOv5 for detecting stop signs and traffic lights, enhancing the car's computer vision capabilities.• Augmented the dataset using OpenCV (cv2) methods to simulate various backgrounds, improving the robustness of the object detection model in real-world scenarios• Integrated object detection with custom Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) control systems to enhance the QCar’s decision-making and control processes.• Assisted with debugging PID and MPC on the QCar• Optimized YOLOv5 inference performance on the Jetson TX2 platform by leveraging TensorRT, achieving faster and more efficient processing.• Our Cal Poly team (the only team comprised solely of undergraduates) placed in the top 10 out of 39 teams from 28 universities worldwide and competed in the final hardware stage in Toronto in July.

• Managed the project roadmap for department leadership and completed training in project management processes.• Developed an onboarding guide for new staff members.• Enhanced protocols for updating project data sheets.• Rectified issues on the PMO webpage after migrating from Internet Explorer to Microsoft Edge.

• Streamlined tax information requests by overseeing the Managed Disclosure Office inventory.• Implemented a systematic analysis and allocation process for tax information, optimizing workflow for Disclosure Unit analysts.• Researched and implemented an outstanding way to improve analysts’ efficiency and speed.• Utilized excellent customer service skills to respond to requests for tax information.