Software Engineer

Moiz Khan

224-***-**** ********@********.*** github.com/mkhan329

Education

University of Illinois at Urbana-Champaign May 2024 Bachelor of Science in Computer Science, Minor in Mathematics GPA: 3.66 Experience

Motorola Solutions May 2023 – Aug 2023

Software Engineer Intern Schaumburg, Illinois

● Transformed a key management library to upgrade Motorola Solutions’ products to the next-generation solution of key management, increasing efficiency by 20%

● Modernized several APIs for interfacing with Motorola Solutions’ products using C and introduced several algorithms using encryption libraries

● Automated GoogleTests to assess the codebase and delivered weekly updates using Bitbucket

● Collaborated with a team of senior software engineers adhering to the Scrum framework and presented final intern project on key management library Projects

Pest Detector Sept 2022 – Dec 2022

● Proposed a computer vision program to classify insects from the IP102 Insect Pest Dataset using TensorFlow and Keras, achieving accuracy aligned with published research (~70%)

● Developed a convolutional neural network to classify pest images and implemented optical character recognition for dataset preprocessing, increasing accuracy by 15% US Congressional Voting Record Analysis Feb 2022 – May 2022

● Organized a web application to query, display, and analyze the voting record datasets of US senators and political parties, supporting analysis for 1000+ legislative decisions

● Designed and implemented the database schema (structure and organization) for the voting records on Google Cloud Platform; developed project using JavaScript, SQL, and HTML Asteroids Game Mar 2021 – May 2021

● Recreated the classic Asteroids arcade game, implementing 2D spaceship physics, asteroid collision animations, a ramping difficulty system, power-ups, and a retro computer graphic style

● Produced the video game using C++ and Cinder graphics library, incorporating smooth performance and engaging gameplay, and resulting in high frame rates of 60 frames per second Human Action Classifier Aug 2020 – Dec 2020

● Created a machine learning program to classify images from the Stanford 40 Action Dataset using TensorFlow and Keras, achieving accuracy consistent with published results (~60%).

● Generated a convolutional neural network to classify images and optimized program accuracy using transfer learning on VGG19 model, boosting accuracy by 10% Skills

Languages: Python, Java, C, C++, C#, MIPS Assembly, Verilog, JavaScript, SQL, HTML, Rust, Swift Technologies: PyTorch, Pandas, TensorFlow, OpenCV, NumPy, WebGL, Cinder, MongoDB, Neo4j, Unreal, Unity, Git, JIRA, Docker, Google Cloud Platform, VS Code, Visual Studio, CLion, ROS Coursework: Software Design, Database Systems, Machine Learning, Artificial Intelligence, Computer Graphics, Computer Architecture, Abstract Algebra, Graph Theory, Algorithms

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