Oriol Pujol’s main research areas are computer vision and pattern recognition. In general, he is interested in the development and application of machine learning techniques to visual object recognition and wearable sensors in the eHealth domain. Current projects in Dr. Pujol's research group involve general machine learning and computer vision strategies; in applied research he is working in intravascular ultrasound (IVUS) image analysis, object recognition, human behavior analysis and egocentric wearable computing.

My current basic theoretical research lines are:

Ensemble Learning: Ensemble learning has proved to be one of the most successful methods for classification. However, besides some very popular lines of research the community in this topic is quite small. In this line I am currently working on;

1. -Analysis and design of new ensemble strategies, such as Geometry-based Ensembles.
   

2. -Bridging the gap between probabilistic graphical models and ensemble learning.
   

3. -Classifier selection and feature selection from ensembles.
   

Sequential learning and prediction: In many real life applications context matters. Sequential learning aims at exploiting this contextual information, in particular in images in what we call scene learning.

1. -Ensemble-based sequential learning.
   

2. -Probabilistic graphical models for sequential learning.
   

Pattern discovery: In this line I aim to find relations among different signals, repeated patterns or anomaly detection in applications such as stock markets or signals from different wearable sensors (ECG, accelerometer, etc).

The applications and technologies I am involved in are:

Wearable computing systems as a framework for enhancing our senses, logging user/patient behaviors, and interacting with ubiquitous systems.

Augmented reality as a means to provide feedback to the user of a wearable computer system.

eHealth applications involving human behavior analysis using wearable systems for Rehabilitation, sport performance, Attention Deficit and Hyperactivity Disorder (ADHD) analysis, and Alzhemer’s and dementia assistive technologies.

Medical applications involving IVUS data analysis, automatic plaque characterization, cardiovascular disease prediction, cardiovascular analysis by means of magnetic resonance imaging and computerized tomography and drug effect on miocardial tissue.