Hi there!, My name is Samuel Iwuchukwu, a recent Computing Science MSc graduate from the University of Alberta, focused his thesis on characterizing changes in Scratch and leveraging language and deep learning models for next token suggestions in Scratch visual source code.
I significantly enhanced ERP data import by enabling direct reading of both Excel (.xlsx) and CSV files, eliminating manual conversions and reducing user errors. To boost development efficiency, I introduced version control by migrating the codebase to GitHub and implemented a CI/CD pipeline for automated unit testing of new features, which greatly improved code readability and team collaboration. A critical contribution involved resolving a major bug in the multi-wall merging logic. By carefully modifying database queries and conditional checks to account for both Multiwall ID and height, I ensured accurate wall merging and prevented structural data inconsistencies within the ERP system. Additionally, I led the redesign of the ERP system's user interface, effectively aligning various tabs for improved usability.
As a Graduate Research and Teaching Assistant at the University of Alberta, I spearheaded comprehensive research into the Scratch visual programming language. My work involved extracting and meticulously processing over 37,000 Scratch project files from GitHub, which I then organized into a robust dataset for quantitative analysis. I developed a resilient Python parser to transform Scratch3 files into Abstract Syntax Trees (ASTs) and converted them into n-gram models to enhance language model training. Leveraging state-of-the-art language models like KenLM, NLTK.lm, and BiLSTM, I successfully implemented next-token prediction for Scratch3, bringing predictive intelligence to this underserved community. Beyond research, I developed a .NET Core microservice that significantly boosted client engagement for the construction engineering department by bridging simulation data to a web interface. I also managed and mentored over 90 undergraduate software engineering students across 15 teams, guiding them through successful Android application development. My contributions to the academic community include co-authoring a scientific paper on collaboration in Open Source Software, which was recognized as a runner-up for best paper at MSR 2023.
As a skilled Software Engineer, I've consistently delivered impactful solutions across various domains. I developed a .NET Core microservice to automate tax receipt generation, slashing latency from 80% to 0.08% and dramatically improving data management efficiency. I also implemented a secure, resilient .NET MVC e-commerce payment gateway, integrating multiple regional providers and driving a 15% increase in order completion rates. Complementing this, I optimized payment processing integrations through SQL query refinements and code refactoring, achieving a 0.05% error rate and 99.7% uptime. My collaborative experience includes partnering with international engineers to create a .NET MVC/SQL tracking software solution, significantly enhancing order visibility. I further deployed a real-time spooling system using React and Vue.js, enabling immediate access to customer reports from our S3 data lake and accelerating stakeholder decision-making. Throughout my work, I ensured system integrity and compliance by implementing robust unit and integration tests across all health systems.
I developed a Java-based Subscription Software Development Kit (SDK) to streamline the licensing of encryption modules, significantly improving the client subscription process. Additionally, I integrated UI mockup designs into an Android application using Java, Android, and XML, enabling a smoother and more intuitive user experience for users.
Applied AI in Software Engineering
GPA: 3.9
GPA: 3.46