Jesus Barocio Garibay

Computer Science and Data Science Student

About Me

Jesus Barocio

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.

Resume

Projects

Shell Sort Visualization

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.

Features:

  • GUI Interface
  • Sorting Algorithms
  • Array Manipulation
  • Data Persistence
  • Mean Calculation

How to Use:

  1. Run the script, and a Tkinter window will appear.
  2. Choose a gap sequence for Shell Sort.
  3. Adjust the array size using the slider.
  4. Click the "Start" button to visualize the sorting process.
  5. Explore other features such as testing, resetting, and calculating the mean execution time.

Note: The necessary libraries (`tkinter`, `ttkbootstrap`, `numpy`) need to be installed before running.

GitHub Repository: Shell Sort Visualization

Epidemic Spread Simulation

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.

Simulation Parameters:

  • Nodes: Number of nodes in the network.
  • Edges: Number of edges in the network.
  • Infection Rate: Probability of infection when a susceptible node interacts with an infected node.
  • Recovery Rate: Probability of recovery for an infected node.
  • Incubation Rate: Probability of transitioning from exposed to infected.
  • Vaccination Probability: Probability of vaccination for susceptible nodes.
  • Days: Number of days for the simulation.

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.

How to Use:

  1. Adjust the sliders to set the initial parameters.
  2. Click the "Run Simulation" button to start the interactive epidemic spread simulation.

GitHub Repository: Epidemic Spread Simulation

Pokemon Recommendation System

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.

Key Features:

How to Use:

  1. Upload the CSV file containing Pokemon data using the provided interface.
  2. Enter the names of one or two Pokemon you want to receive recommendations and detailed information on.

Note: The script relies on Pandas, Matplotlib, and ipywidgets libraries.

GitHub Repository: Pokemon Recommendation System