PhD research on efficient and frugal methods for the automatic extraction and structuring of cultural event information to connect cultural industries with audiences.
Specialization in advanced NLP techniques and machine learning methodologies, with focus on modern language models, deep learning architectures, and automatic language processing systems.
Advanced study in algorithms and programming, building strong foundations in computational thinking, software development, and computer science theory.
Comprehensive training in mathematical foundations and computer science fundamentals, providing strong analytical and problem-solving skills across both domains.
Professional Experience
Working on geotagging systems for identifying location references from address fragments in journalistic articles. Developing entity linking systems for geographical locations and conducting knowledge extraction projects including named entity recognition and relationship mapping. Specializing in geographical entity disambiguation for addresses, places, monuments, and cities.
Conducted comparative analysis of named entity recognition systems on press articles. Applied machine learning techniques for key information extraction, participated in annotation and analysis of structured and unstructured data, and evaluated different approaches in automatic language processing.
Developed classification models for recipe categorization (Main Course, Appetizer, Dessert) using natural language processing and machine learning techniques. Implemented classification systems based on recipe titles and instructions.
Evaluated the effectiveness of autoencoders for tabular data classification. Implemented neural networks for data compression and representation, built compression functions in reduced-dimension latent spaces, and conducted comparative analysis of classification performance.