RYAN HUNT
Graduate Rensselaer Polytechnic Institute
GSAS/CS Dual Major
GAMES WORKED ON/FINISHED
SUGARING STORIES
GSAS Capstone Project
A partially developed idle-battle game with a focus on monster taming/creation. Made using the Unity Game engine. Developed multiple systems, including the auto battle system, monster creation/summoning systems (involved drawing lines between points, similar to a lock screen), monster inventory system, JSON save manager.
GAME_SELECT
Experimental Game Design Final Project
Project:Game_Select is a multi genre exploration game that takes the player through various snippets of Gaming, allowing them to play through and experiment with different genres. Taking control of Pixal, a Game Asset that has gotten knocked off their path to implementation, players will have to Jump, Shoot, and Magic their way through a myriad world overcome with glitches, bugs, and errors in order to find their place in the system.
CERTIFIED DOOMSTER
Spring 2021 Away Semester Project
A fully developed turn-based RPG available on Itch.io as part of GAMC Games. Made using the Unity game engine. Developed the battle system (Status Effects, Position Swapping, UI elements like sprite flashing and damage numbers, items, etc.), the JSON based save manager, and helped implement overworld pickup items and NPCs.
MEGA MECHA FIGHT NIGHT
Unreal Engine Semester Project
A partially developed action/beat-em-up game made using Unreal Engine 4, where 2 players take control of the top and bottom half of a mech. Developed multiple UI elements (HUD and menus), took over development of the combat/attack systems (combos, co-op attacks / controls) and implementation of animations.
PROGRAMMING PROJECTS
Showcase of Work
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OPERATING SYSTEMS PROJECT
Worked with a partner on developing a CPU Scheduling simulation project. Can simulate 4 different algorithms: First Come First Served (FCFS), Shortest Job First (SJF), Shortest Remaining Time (SRT), and Round Robin (RR). Made to run using command terminal such as Ubuntu.
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argv[1]: Define n as the number of processes to simulate.
argv[2]: serves as the seed for the pseudorandom number sequence.
argv[3]: Parameter λ for exponential distribution.
argv[4]: Upper bound for valid pseudo-random numbers for exponential distribution.
argv[5]: Define tcs as the time, in milliseconds, that it takes to perform a context switch.
argv[6]: For the SJF and SRT algorithms, since we cannot know the actual CPU burst times beforehand, we will rely on estimates determined via exponential averaging. As such, this command-line argument is the constant α for exponential averaging. argv[7]: For the RR algorithm, define the time slice value, tslice, measured in milliseconds.
