Bio
Alberto is an engineer with a solid foundation in mathematics, optimization and computer science. He has an M.Sc. in Computer Science, and is now a Ph.D. candidate at Imperial College London. His current research interests include generative AI and applications of machine learning to databases. Alberto has over three years of experience working in industry, where he developed projects on data science, mathematical modeling and optimization. His interests range from mathematics and machine learning, to literature and cinema.
Research interests
Alberto's research focuses on the intersection of artificial intelligence (AI) and databases, particularly on "learned indexes." These indexes use machine learning to create data structures that accelerate database queries, potentially offering better performance than traditional methods. Alberto aims to provide theoretical evidence that learned indexes can offer an algorithmic advantage and to link their performance to measurable characteristics of the data source. In this context, multi-dimensional indexes are particularly relevant for AI applications, where querying high-dimensional vector data is essential, for instance, in retrieving word embeddings in Large Language Models (LLMs). This connection underscores the broader importance of efficient data management in AI applications. Additionally, Alberto is working in generative AI, which involves creating algorithms that can generate new content, such as text, images, or music, based on the data distribution they have been trained on. Generative AI has numerous applications, from biomedical imaging to data synthesis and augmentation.