Abdulrahman Alshahrani

King Abdullah University of Science and Technology (KAUST)

Math of Machine Learning, Data Analytics, Computer Networks, Concurrency

The University of Texas at Austin

GPA: 3.64 / 4.00

Machine Learning, Virtualization, Cloud Computing, High Performance Computing, Computer Graphics

UT Austin - Longhorn Developers

• Contributed to UT Registration Plus, a student-led Chrome extension adopted by 50,000+ students to facilitate course registration and scheduling.
• Collaborated through GitHub Issues, PRs, and Project Boards to resolve UX/UI issues and deliver new features aligned with design specifications and best software engineering practices.
• Prototyped a feature for direct course registration through the extension, including sign-in flow and data scraping logic.

Aramco - Aramco Research Center

• Enhanced the synthetic data for a CNN industrial equipment classification model by creating 3D renders in Blender, improving lighting, customizing textures, and creating Python utility functions to streamline the process.
• Captured and processed 360-degree HDRi images, integrating them with the 3D renders, significantly boosting the model accuracy by 20%.
• Contributed to a project involving change detection of multi-spectral images by gathering datasets, testing models in PyTorch, and creating a flexible repository for testing different models.
• Created comprehensive documentation to ensure the smooth usage of my tools after my departure.

UT Austin - Software Engineering Class

• Supervised 6 student groups in developing full-stack websites, guiding them with project architecture design.
• Reviewed and provided feedback on +30 weekly student blogs, monitoring their progress and challenges, and reporting summaries to the professor.
• Conducted weekly office hours to assist students with understanding course concepts and fixing technical issues.

USC Viterbi School of Engineering - Data Science Lab

• Implemented the Canonical Polyadic (CP) Tensor Decomposition algorithm using the Tensor Algebra Compiler library.
• Ran the CP algorithm on arbitrary data, compressing +90% of the data while maintaining statistical significance.
• Presented the CP algorithm and the experiment results in a department-wide symposium.