Scheduler Algorithms — Implemented First Come First Serve, Shortest Job First, Round Robin, Priority, and Priority Round Robin CPU scheduling algorithms against a dataset of processes, priorities, and burst times.

Multi-threaded Sudoku Validator — Built a program using manual multi-threading to synchronously validate Sudoku puzzles, with dedicated threads handling row, column, and sub-grid validation simultaneously.

Unix Shell — Recreated core UNIX shell functionality using fork(), pipe(), and execvp(), including standard command parsing, I/O redirection, piping, and a command history feature ("!!").

Together, these projects were a first-hand introduction to CPU scheduling, concurrency, and process management at the systems level.

This project was really exciting for me because it was the first project where I was allowed to go all the way from design to implementation without much restriction.
I was tasked with creating a Movie Store system that maintains an inventory of rentable movies, a collection of customers, and a history of all transactions processed.
Some minor requirements were placed, in that we must account for a future interest to move into additional forms of media, and we must use a factory creation pattern.
Additionally, we were expected to document all of our planning in the form of UML diagrams & interaction charts, which was a great learning experience.
Overall, this project taught me the importance of proper planning in Software Development, and the benefit of having well-planned implementations.

This project was a substantial challenge for me and provided an invaluable learning experience.
It began with the task of interpreting a text file representing a graph structure.
Leveraging this structure, I was tasked with implementing and executing several traversal algorithms.
The algorithms included Depth-First Search (DFS), Breadth-First Search (BFS), Dijkstra's, Minimum Spanning Tree using Prim's algorithm, and as a bonus, Kruskal's.

This endeavor served as an excellent introduction to the intricacies of graph data structures and, more specifically, the most prominent traversal and graph-related algorithms.

Implemented an efficient autocomplete system using a Binary Search Tree.
The system predicts user input by matching prefixes with a set of terms, each of which had an associated weight.
This project involved adding terms and weights to the BST, finding and sorting matching terms by weight, and ensuring fast response times.
Additionally, the system supports various helper functions such as traversal, re-balancing, and clearing the tree.
This project had me working with the largest dataset I've worked with (10k+ city names), prompting a heightened awareness for performance considerations.
Managing the size and complexity of the dataset required careful optimization of algorithms and data structures used, underscoring the importance of efficiency in real-time applications.
This project was an interesting first look into the tree data structure and how autocomplete algorithms work.