Projects / Overview
High-Performance Pathfinding & Maze Analytics Engine
Engineered a high-performance Java navigation engine implementing BFS and DFS algorithms with bit-masking state tracking, achieving a 40% improvement in traversal speed across large-scale grid datasets. Designed custom, low-overhead data structures to prevent heap bloat during deep exploration and validated pathfinding accuracy
JavaOOPData Structures
Overview
Designed and benchmarked a Java-based pathfinding and grid analytics engine optimized for memory efficiency and traversal speed. The system evaluates real-time path reconstruction and obstacle navigation across dense grid environments, balancing memory usage against optimal path guarantees.
Key Features & Engineering
- Algorithmic Optimization: Implemented Breadth-First Search (BFS) and Depth-First Search (DFS) algorithms, using bit-masking state tracking to boost grid traversal speed by 40%.
- Low-Overhead Data Structures: Custom-built stack and queue data structures using object-oriented principles to reduce heap allocation overhead and eliminate stack overflow risks during deep recursive searches.
- Comprehensive Test Coverage: Developed a JUnit testing suite covering 50+ edge-case environments—such as disconnected graphs, dense obstacle mazes, and circular loops—achieving 100% path reconstruction accuracy.
- Performance Analytics: Profiled time and space complexity trade-offs between BFS (guaranteed shortest path with higher memory footprint) and DFS (low-memory exploration).