Master’s Thesis: Multimodal Extreme Multi-Label Classification Under Resource Limitations (Grade: 9.5/10).

• Developed a novel, resource-efficient multimodal architecture for Extreme Multi-Label Classification (XMC), integrating text and image modalities using an early fusion model based on transformers. The proposed architecture remains compatible with state-of-the-art XMC methods such as DEXA and NGAME, enhancing classification performance while ensuring computational efficiency and scalability. Extensive experiments on the MM-AmazonTitles-300K benchmark demonstrated that our approach outperforms existing methods, setting a new state-of-the-art in multimodal XMC.

Extracurricular Activities:

• Delegate of the Master’s Degree in Data Science.

• Advanced Methods in Machine Learning.
• Advanced Methods in Statistics.
• Deep Learning for Biometric Information Processing.
• Deep Learning for Signal Processing Image and Video.
• Large-Scale Data Processing.
• Natural Language Processing (NLP).
• Reinforcement Learning.
• Temporal Information Processing.
• Unstructured Information.
• Data Management.

Degree Thesis: Path Planning Optimization in SDN Using Machine Learning Techniques (Grade: A).

• Developed a machine learning-based approach to optimize path planning in Software-Defined Networks (SDN), improving network QoS. Formulated path selection as a multi-class classification problem and evaluated multiple classifiers. The best-performing model, a support vector machine, outperformed alternative methods
• Published in the 2021 IEEE Fifth Ecuador Technical Chapters Meeting (ETCM) https://ieeexplore.ieee.org/document/9590749.

• Artificial Intelligence.
• Calculus (I, II and III).
• Data Structures and Algorithms.
• Data Mining.
• Databases.
• Linear Algebra.
• Programming (I, II and III).
• Probability and Statistics.
• Systems Design.
• Projects: Management and Analysis.