Tech Stack

Java Algorithms Data structures JUnit Performance benchmarking

Overview

As part of UC San Diego’s Data Structures and Performance course, I completed a series of projects implementing fundamental CS concepts in Java. These assignments covered text processing, data structures, algorithm design, performance benchmarking, and testing — mirroring the skills expected in entry-level industry roles.

Projects

Text Readability Analyzer — Implemented BasicDocument and Document classes to compute Flesch Readability Scores. Practiced string tokenization, regex, and unit testing.
Efficient Text Processing & Benchmarking — Built EfficientDocument with pre-computed word/syllable/sentence counts and benchmarked against BasicDocument. Graphed performance and applied Big-O analysis.
Custom Linked List — Developed a generic MyLinkedList<E> with sentinel nodes, implementing add, remove, set, and thorough JUnit test coverage.
Autocomplete with Tries — Implemented AutoCompleteDictionaryTrie to support word completions, using a Trie and BFS for predictions:contentReference[oaicite:3]{index=3}.
Spelling Suggestion Engine — Designed NearbyWords to generate spelling corrections using edit-distance mutations and BFS:contentReference[oaicite:4]{index=4}.

Skills Demonstrated

Core data structures: linked lists, tries, hash tables
Algorithm design: BFS, string mutation, performance optimization
Unit testing: JUnit test suites, edge-case validation
Complexity analysis: predicted & benchmarked runtimes
Object-oriented programming in Java