Paul V. Ingram III

Undergraduate CS Student @ UCR

Overview

I build and analyze systems to understand how they behave, fail, and can be secured. My work spans systems security, applied algorithms, and data-driven analysis, with a focus on using rigorous models to reason about real-world complexity.

Quick facts

Projects

Featured

Mancala AI

A terminal-based implementation of Mancala featuring a stylized ANSI-rendered board and an AI opponent powered by minimax with alpha–beta pruning.

  • Tech: Python, ANSI escape codes
  • Highlight: Implemented an efficient minimax search with alpha–beta pruning to support responsive AI play in a terminal environment
  • What I learned: Game-tree search optimization and low-level terminal rendering using ANSI control sequences
Game AI Algorithms Systems

Front-End Developer - HSA Website

A collaborative club website developed in a Scrum team, focused on delivering a responsive, polished user experience for a student organization.

  • Tech: Next.js, Tailwind CSS
  • Highlight: Contributed animations, mobile-responsive layouts, and reusable UI components within an agile Scrum team
  • What I learned: Frontend development with Next.js, utility-first styling with Tailwind, and the fundamentals of web hosting and deployment
Frontend Web Development Team Project

Class Projects

CS148 Final Project

A robotics simulation project implementing autonomous path planning and mapping in ROS, developed in a two-person team.

  • Tech: Python, ROS, RViz
  • Highlight: Implemented Dijkstra-based path planning with custom Planner and Publisher nodes, interactive waypoint selection in RViz, and SLAM-generated occupancy grids
  • Analysis: Evaluated empirical runtime and space complexity of multiple path planning algorithms across varying path lengths using visualization
  • What I learned: Integrating perception, graph search, and performance analysis to reason about real-time robotic navigation systems
Robotics Systems Algorithms

CS105 Mini Project

An exploratory data analysis project examining relationships between stress levels and lifestyle factors using survey data from Computer Science students at UCR.

  • Tech: Python, Pandas, Matplotlib
  • Highlight: Identified statistically significant relationships between reported stress levels and lifestyle variables using hypothesis testing at standard significance thresholds and visualizations
  • What I learned: Applying EDA, statistical testing, and visualization to reason about real-world survey data
Data Science Statistics Analysis

Coursework

Selected courses I’ve completed at UCR.

Systems & Security

  • CS165: Computer Security
  • CS161: Computer Architecture
  • CS153: Operating Systems

AI & Data

  • CS171: Machine Learning and Data Mining
  • CS170: Artificial Intelligence
  • CS148: AI for Robotics
  • CS105: Data Analysis Methods

Back to top