Denis Vera's Homepage

About Me

Hi! I'm Denis Vera, a passionate Computer Engineering student at the Universitat Politècnica de Catalunya. With a strong interest in Software Engineering, Full-Stack (mainly back-end) development, and Big Data, I am always eager to learn and develop new skills in cutting-edge technologies. In my spare time, I enjoy exploring new programming challenges and working on interesting projects that allow me to hone my problem-solving abilities.

My dedication to continuous learning has led me to explore various technologies, frameworks, and tools, enabling me to adapt and thrive in a fast-paced technological landscape. I am particularly interested in securing an internship, as I believe it would provide me with valuable learning opportunities to further enhance my skills and contribute meaningfully to real-world projects.

I believe in the power of collaboration and always look forward to working with like-minded individuals who share my passion for technology and innovation. If you're interested in collaborating or learning more about my work, feel free to reach out!

My Projects

FollowTheLeaderTeam - RoboCode

Objectives:

Develop a coordinated team of N robots that establish a leader dynamically.
Implement a fixed movement pattern while adapting to battlefield conditions.
Ensure role-switching when team members are eliminated.
Synchronize team attacks against enemy robots for strategic advantage.

What I Learned:

Implementing team-based AI behaviors and inter-robot communication.
Designing a leadership election system within a dynamic environment.
Optimizing pathfinding, obstacle avoidance, and real-time decision-making.
Utilizing debugging visualizations with onPaint().

Technologies Used:

Java
RoboCode API
Event Handling
Radar Scanning
Mathematical Calculations for Pathfinding

View Project

A demonstration of the RoboCode team working in sync, with a leader robot and coordinated attacks.

Connect 4 AI Player

Objectives:

Implement the Minimax algorithm with Alpha-Beta pruning for efficient decision-making in Connect Four.
Optimize the AI’s move generation by testing against an existing solution provided by the professor.
Ensure performance and compatibility within the provided Java-based game framework.

What I Learned:

Deepened understanding of game theory through Minimax and Alpha-Beta pruning.
Gained experience in AI decision-making and search tree optimization.
Enhanced Java programming skills, focusing on algorithm implementation and performance tuning.

Technologies Used:

Java
Minimax Algorithm
Alpha-Beta Pruning
Game Theory

View Project

An AI player making moves in the game of Connect 4, demonstrating Minimax and Alpha-Beta pruning.

Hex AI Player

Objectives:

Develop an AI player for the game of Hex using the Minimax algorithm with Alpha-Beta pruning.
Implement two versions:
- PlayerMinimax: A player with a configurable search depth.
- PlayerID: A player utilizing Iterative Deepening for time-controlled decision-making.
Design an efficient heuristic function based on the distance between opposing sides.
Implement a custom version of Dijkstra's algorithm for pathfinding.

What I Learned:

Advanced decision-making using Minimax with Alpha-Beta pruning.
Iterative Deepening search for handling time constraints in AI.
Designing and optimizing heuristic evaluation functions.
Implementing a custom pathfinding algorithm (Dijkstra) for game strategy.

Technologies Used:

Java
Minimax Algorithm
Alpha-Beta Pruning
Iterative Deepening
Custom Dijkstra’s Algorithm

View Project

A demo of the Hex AI Player in action, showcasing the Minimax algorithm and Iterative Deepening search.