Research Lines
The lab's research focuses on machine learning and its applications. We are interested in the foundations of machine learning and how it is used to solve challenging problems.
Hybrid Kernel Methods
We aim to obtain effective and efficient kernel methods that compete on par with deep learning. For this, we are developing methods for learning a mapping between the features of the input sample and the labels, which is later used to predict labels for unannotated instances.
Quantum Machine Learning
We are excited with this new research line, integrating classic and quantum algorithms and insights to perform quantum measurement and density estimation.
Multimodal Learning
In multimodal learning we have developed models for learning an intermediate representation through multimodal information fusion. Our models take advantage of additional information provided by multimodal data, and combine them, using data fusion techniques to improve information retrieval performance.
Medical Image Analysis
We develop and integrate Computer Vision in the medical image analysis for two main purposes: automatic pattern finding related with pathology signatures associated with healthy and abnormal tissues, and automatic evaluation of diseases using Ophthalmic images.
Natural Language Processing
For Natural Language Processing (NLP) we focus in Question Answering for information retrieval and entity extraction, Author Profiling for determining a social group of an unknown author, and Source Code Analysis for automated testing of source code.