Hello! My name is Jesus and I'm a senior at Willamette University majoring in Computer and Data Science, where I've not only gained technical expertise through my involvement with Willamettes IT but also cultivated a passion for collaborative problem-solving. I am dedicated to effective communication, innovative thinking, and a strong spirit of teamwork.
As a former member of the Willamette tennis team, I learned the importance of set others up to succeed. On and off the court, I've embraced effective communication, partnering collaboratively with teammates to achieve shared goals, and providing and receiving supportive feedback. These experiences have improved my ability to work within a team, a skill I bring to every collaborative project.
My academic pursuits in Computer and Data Science have exposed me to diverse challenges, each an opportunity to ask, listen, and learn. Whether delving into complex algorithms or exploring emerging technologies, I actively seek out new information, asked thoughtful questions, and listen carefully to different perspectives.
My experience in IT has given me a solid foundation in IT concepts. I've demonstrated flexibility and adaptability by navigating dynamic environments. Through collaborative problem-solving, I've consistently implemented innovative solutions, showcasing a solutions-oriented approach to challenges.
This project uses the Tkinter library for visualizing the Shell Sort algorithm. It allows users to choose different gap sequences for the Shell Sort and provides options to test the algorithm on different sequences.
Note: The necessary libraries (`tkinter`, `ttkbootstrap`, `numpy`) need to be installed before running.
GitHub Repository: Shell Sort Visualization
This program uses the NetworkX and Matplotlib libraries to simulate the spread of an epidemic within a randomly generated network. It considers various parameters, including infection rate, recovery rate, incubation rate, and vaccination probability. The interactive sliders allow users to control the simulation parameters and observe the progression of the epidemic over a specified number of days.
The program visualizes the epidemic spread, highlighting susceptible (blue), infected (red), recovered (green), exposed (purple), and vaccinated (yellow) nodes. Each day, the simulation updates the network state based on the parameters.
GitHub Repository: Epidemic Spread Simulation
This program uses a dataset to recommend Pokemon based on their type advantages and synergy scores. The program allows users to input one or two Pokemon names, the program recommend Pokemon that complement each other and cover eachothers weakness.
Note: The script relies on Pandas, Matplotlib, and ipywidgets libraries.
GitHub Repository: Pokemon Recommendation System