Christopher Shokunbi

Bachelor's

Computer Science

Lighthouse

Software Engineering Intern Remote

- Utilized SQL indexes to improve the efficiency of a web based, data-parsing application - Populated the Microsoft Azure board with goals during sprint planning and utilized their pipelines for managing data - Created test cases using Selenium / C# Testing Framework - Communicated progress to the team in daily standups, led scrum meetings, and collaborated on solutions to impediments - Provided feedback during retrospectives and participated in refining sprint goals during backlog refinement - Presented efficiency improvements made to database searches in sprint demos to the management team - Tech: SQL - Visual Studio - Azure DevOps - C - Selenium - Projects - UCI Search Engine - October 2 0 2 3 - December 2 0 2 3 - Developed a search engine that crawls through HTML files under the ics.uci.edu domain - Utilized the Beautiful Soup library to conduct natural language processing - Designed a scoring algorithm to rank the pages returned from the user's query - The-Sleep-App - April 2 0 2 3 - June 2 0 2 3 - Engineered a machine-learning based recommendation system developed for iOS in a team of 3 - Collaborated to design a GUI and machine learning model using Swift and CORE ML - Attended bi-weekly meetings to present my progress and update the backlog - Presented the functionality of the application with my project partners in video format - Gradescope-to-Canvas Converter - Collaborated with 4 others to develop a cross platform desktop application that converts files based on user inputs - Met weekly with product sponsors to develop a requirements document and present updates - Designed an intuitive user interface from concept to final deployment using Figma and Bootstrap tools with HTML and CSS - Data Analysis Project - February 2 0 2 2 - March 2 0 2 2 - Analyzed a data set to find patterns and created a machine learning model that predicted target values in a validation data set - Compared the effectiveness of different machine learning models using packages such as Scikit-learn and ML Tools