Kyle Allhusen Cell: 401-***-**** GitHub: KyleAllhusen E-mail: adku53@r.postjobfree.com

EDUCATION:

UNIVERSITY OF RHODE ISLAND KINGSTON, RI

Bachelor of Science, Computer Science Expected: May 2021 GPA: 3.0, Dean’s List

RELEVANT EXPERIENCE:

Cyber Physical Security Lab, Intern KINGSTON, RI

University of Rhode Island December 2020 – Present

• Developed a tool that abstractly models a notion of trust using Graphs, while be interactive for the user.

• Currently working on decoding and managing messages written with the IEC 61850 protocol.

• Create and maintain a lightweight database where these decoded messages will be stored using GO and Elastic Stack. Undergraduate Teaching Assistant KINGSTON, RI

University of Rhode Island January 2020 – December 2020

• Instructed students in labs for CSC 412: Operating Systems and CSC 305: Software Engineering

• Worked with professor and other TAs to maintain auto-grader for CSC 412.

• Adhered to university requirements for student assignments, testing and grading of work. Pet Care Associate Lead JOHNSTON, RI

PetSmart September 2019 – Present

• Made sure that the Pet Care department, met the expectations set by management.

• Provided adequate assistance and consistent oversight to pets and customers.

• Administered treatment to sick or injured animals and consulted with veterinarians to handle more complex issues. Technical Skills:

• Languages: C++, C, Python, GOLANG, SQL, MATLAB, Bash

• Operating Systems: Windows 10 and Linux Based OS

• Relevant Coursework: Software Engineering, Data Science, Linear Algebra, Discrete Mathematics, Design and Analysis of Algorithms, Database Management Systems, Information and Network Security Relevant Projects: (Some projects available of GitHub) Network Trust Visualizer: Described earlier, a tool that abstractly models a notion of trust using Graphs, while be interactive for the user. Developed in Python using NetworkX and Mathplotlib. Done along with my internship in the URI Cyber Physical Security Laboratory.

Workout Wizard: A team developed Windows-based app developed completely in Python, used to track and manage a user's workout. Supplied visual aids for exercise and graphs to visualize their progress. Role in project was to generate graphs based on user input.

Lossy Image Compression: A simplified version of lossy image compression written entirely in C. Maintained a <5% error rate among compressed images.

Image Management System: A C++ implementation that reads the data of an image (location, time taken, etc.), and sorts the images based on those fields. Also, performs simple image operations such as rotating and mirroring, using 2D rasters.