CAREER OBJECTIVE

    To secure an entry level fulltime position as a Software Engineer/Technology Analyst that allows me to bring solutions utilizing my educational background, strong technical skills and experience and an opportunity to grow within the organization.

Education

    North Carolina State University GPA: 3.6
    B.S. Computer Science May, 2019

    Central Piedmont Community College GPA: 4.0
    Associates in Science Transferred to NC State (August, 2016)

Technical Skills

    Operating Systems: Ubuntu, Windows, Mac OS
    Languages: C, C++, Java, Python
    Web Technologies: HTML5, CSS, JavaScript, AngularJS, Angular, jQuery
    Frameworks: Spring, Hibernate Software Tools: Eclipse, GitHub, Jenkins, JUnit, Selenium, Cucumber
    Courses: Grad level Artificial Intelligence, Java Programming, Keynesian Logic/Discrete CSC Mathematics, C and Software Tools, Data Structures
    Skills: Managing Complex Software, Test Driven Development, Code Coverage, Static Analysis, Version Control, Continuous Integration

Projects

    My Projects can be found under "My Work".

Achievements

  • 2 ndSchool Rank for National Cyber Olympiad
  • Head of Activities Club in High School.

Work Experience

    Technology Analyst Intern June 2018 - August 2018
    Wells Fargo, Charlotte, NC
  • Worked on a web application tool that ranks securities and different aspects of a security based on criteria from the user for a Quantitative Research Platform hosted on a WebLogic server and testes using JUnit.
  • Designed the complete application UI using Bootstrap
  • Created Spring REST controllers to download static files from the backend and load a portfolio based on users’ selection
  • The securities in the portfolio were displayed as a table and grouped based on sector in frontend using jQuery and DataTables plugin.
  • Loaded the portfolio data from a SQL database using Hibernate to be used by the REST controllers.
    National Science Foundation - Undergraduate researcher May 2017 - July 2017
    North Carolina A&T University, Greensboro, NC
  • Research on detecting lanes from a live camera feed. A segmentation neural network was retrained to detect lanes and roads.
  • The output from the model was used to estimate the function for the lane.
  • The model was 60% accurate