About

Shaquille Pearson

Software

[Research & Engineering]

Software Engineer with 3 years of experience in scalable data pipelines, reproducible CI/CD workflows, and API development. Proficient in Python, JavaScript, FastAPI, and Docker, with expertise in frontend & backend systems, database optimization, machine learning and performance tuning across academic and industry projects.

Contact Resume

Skills

Experience

Graduate Research Assistant @ (UoW)

  • Data Pipeline - Designed and built a data filtration pipeline with Python and Github's GraphQL that processed over 1.27 million open-source projects.
  • Build Reproduction - Led efforts to create reproducible build environments for 982 builds in the NPM ecosystem using Docker, Python, and GitHub Actions and YML files.
  • API Development - Built a REST API using FastAPI, Python, and Docker to interface with the reproducible build environment, Automating dependency resolution and build validation for NPM projects.

January 2023 - December 2024

Instructional Apprentice/Teaching Assistant @ (UoW)

  • Technical Assistance - Assisted students with coding assignments debugging code in (more details in resume).
  • Teamwork & Leadership - Communicated closely with course instructors and fellow instructional apprentices to lead tutorials, proctor exams, and coordinate grading.
  • Communication & Collaboration - Communicated effectively with students and instructors via email, forums , and in-person meetings to address inquiries.

January 2023 - December 2024

Junior Software Developer @ (DPI)

  • Content Automation - Developed an automation system using Node.js and the Axios library to interact with the DPI website’s REST CMS API, enabling automatic updates for news articles and press releases.
  • Frontend Enhancements & UI Optimization - Improved website performance and responsiveness by refactoring React.js components and optimizing CSS animations, enhancing page load speed.
  • Backend Performance Optimization - Conducted a full audit of MySQL DBMS to implement B-Tree indexing on frequently queried columns which reduced query response times by 13%

January 2022 - December 2022

Research

Exploring Dependency Related Build Breakages In The NPM Ecosystem

This project analyzes dependency-related build failures within the NPM ecosystem by examining JavaScript projects. I utilized Git to track modifications in package.json files and employed Docker to create isolated, reproducible build environments. CI/CD pipelines, specifically GitHub Actions, were parsed to identify breaking changes, while tools like nektos/act and docker-compose were used to simulate the build process locally.

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Code Review Practises On Ethereum Smart Contracts

This project assesses the effectiveness of code review for Ethereum smart contracts across major projects like Uniswap and Aave. Which represent some of the most widely used protocols in the blockchain ecosystem, where security vulnerabilities can lead to significant financial losses. Using Git for version control and Slither for static analysis, vulnerabilities such as reentrancy, unchecked transfers, and zero-address issues were identified.

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Predicting Build Breakage With Machine Learning.

This project is a literature survey focused on applying machine learning techniques to predict build failures in Continuous Integration (CI) systems. It reviews a range of approaches, including Random Forests, Logistic Regression, and Deep Learning, The survey explores key factors influencing model performance, such as code complexity, commit frequency, and noise in the data. It also examines statistical methods like ANOVA and Principal Component Analysis (PCA).

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An Evaluation of Automated Code Review Approaches.

This project explores the effectiveness of automated code review tools in evaluating code changes and generating meaningful feedback. The study investigates the challenges of semantic equivalence detection in review comments, where traditional exact-text matching fails to capture nuanced meaning. Using pre-trained language models such as GPT-4 and all-miniLM-L6-v2, the evaluation compares their performance in identifying semantically equivalent comments.

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Graph Based Augementation for Dependency Management in NPM.

This project investigates dependency management challenges in the NPM ecosystem, where transitive dependencies, version mismatches, and cyclic relationships create complexity. Traditional tools often fail to provide clear visibility into these issues, making dependency tracking and resolution difficult. Using a graph-based approach, this study visualizes dependency relationships and uncovers key insights into redundant dependencies and dependency conflicts.

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Exploring the Prevalence of Social Biases In State Of The Art large language Models

This project investigates the prevalence of social biases in large language models like GPT-2, DistilGPT-2, Bloom-560M, and Facebook-OPT-350M, hosted on Hugging Face. By analyzing the behavior of these models with prompts designed to evoke toxic or biased responses, we utilized tools like the Perspective API to assess generated content across attributes such as toxicity, identity attack, and profanity.

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Projects