Collaborated with cross-functional teams to develop AI-driven applications using large language models (LLMs) and machine learning. Worked on integrating GenAI technologies like ChatGPT API, optimizing data pipelines, and contributing to front-end and back-end development. Gained hands-on experience in AI product development.Commanded the front-end architecture of a dynamic app using React Native, TypeScript, JavaScript, and specialized libraries, elevating development speed by 35% and enhancing code efficiency for a seamless user experience

Nasa Mars Rover Path-Finding Team● Engineered a Mars Rover simulation with a team, creating a realistic model that navigates Martian terrain using an A* Search algorithm with Manhattan heuristics, achieving 30-50% faster performance and up to 50% lower memory usage compared to Euclidean heuristics, and 40-80% faster performance compared to other algorithms such as Dijkstra, BFS, and DFS.● Established weekly strategic meetings with all project stakeholders, successfully integrating adaptive navigation using Digital Elevation Models, resulting in a dynamic, interactive Martian map and improving pathfinding efficiency by 40-80% compared to traditional algorithms.

● Contributed significantly to a website redesign project focused on enhancing accessibility, strategically implementing HTML and CSS modifications to optimize user experience for individuals with visual or hearing impairments.● Achieved substantial improvements in the website's accessibility compliance, facilitating a more navigable and interactive environment for all users, particularly those utilizing assistive technologies.

● Created and tested AI players using Minimax and Alpha-Beta pruning algorithms, implementing a robust utility function to evaluate game states and make optimal moves.● Achieved a significant quantitative improvement, by demonstrating a 75% reduction in computation time with Alpha-Beta pruning over Minimax, while maintaining a 98-100% win rate against random players, highlighting the efficiency and effectiveness of the developed AI algorithms.

● Developed and implemented classification models using logistic regression, random forests, support vector machines, and gradient boosting to predict student dropout and success rates based on academic path, demographics, and socio-economic status.● Achieved a 30% increase in overall prediction accuracy, a 40% boost in F1-score for minority classes, and a 50% reduction in model training time by implementing feature engineering, hyperparameter tuning, SMOTE for class imbalance, and cross-validation techniques, leading to more precise identification of at-risk students and enhanced early intervention strategies.

● Developed an object-oriented 'Go Fish' game, incorporating sophisticated design patterns such as singleton, factory, and strategy to enhance functionality and user experience.● Achieved a 100% completion rate of planned features, excluding the web version, with a successful implementation of all design patterns and user account functionalities, resulting in a robust and fully functional game.