Ofertes de projectes

Consulta ofertes d'altres estudis i especialitats

The master thesis consists of developing a framework for Group Recommender Systems and investigating the methods for generating recommendations to groups.

To date, traditional Deep Learning (DL) solutions (e.g. Feed-forward Neural Networks, Convolutional Neural Networks) have had a major impact in numerous fields, such as Speak Recognition (e.g., Siri, Alexa), Autonomous driving, Computer Vision,etc. It was just recently, however, that a new DL technique called Graph Neural Network (GNN) was introduced, proving to be unprecedentedly accurate to solve problems that are formalized as graphs.

Analyze the interplay between fairness and explainability in collaborative recommender systems.

Interacció Persona-Màquina Ciència de les Dades i Intel·ligència Computacional Enginyeria del Coneixement i Aprenentatge Automàtic

In recent years the volume of information available electronically has increased exponentially, coining the term Big Data to refer to this phenomenon. The medical domain is an area in which the number of documents generated by the centers for patient primary care constantly increases. However, a bottleneck is generated because processing these documents requires specialized personnel craftly performing tasks. In the framework of TAIDAMED research project, we are developing a set of processors that allow automatic analysis of medical texts taking into account criteria of robustness, high precision and coverage. In particular, this thesis would aim at the acquisition of patterns of clinical behavior from medical reports represented as semantic graphs using Neo4J database.

Analyze interplay between privacy and fairness in collaborative recommender systems

Recent advances in the field of Reinforcement Learning (DRL) are rising a lot of attention due to its potential for automatic control and automatization. Breakthroughs from academia and the industry (e.g, Stanford, DeepMind and OpenAI) are demonstrating that DRL is an effective technique to face complex optimization problems with many dimensions and non-linearities. However, to train a DRL agent in large optimization scenarios still remains a challenge due to the computational intensive operations during backpropagation.

This project aims to analyze the prediction capability of Optical Coherence Tomography Angiography (OCTA) images for Diabetes Mellitus (DM) and Diabetic Retinopathy (DR,) in a large high-quality image dataset from previous research projects carried out in the field of Ophthalmology (Fundacio¿ La Marato¿ TV3, Fondo Investigaciones Sanitarias, FIS). OCTA is a newly developed, non-invasive, retinal imaging technique that permits adequate delineation of the perifoveal vascular network. It allows the detection of paramacular areas of capillary non perfusion and/or enlargement of the foveal avascular zone (FAZ), representing an excellent tool for assessment of DR.

Sistemes Multiagents Interacció Persona-Màquina

Our objective is to endow a conversational agent with value-aligned beahviour. We aim to align the chatbot with a given moral value, such as respect, which assesses agent's actions in order to learn, through Reinforcement Learning, to how to interact with the user following the value.

The goal of this project is to analyze state-of-the-art metrics[1] that combines them to quantify the equality of learning opportunities among learners and mitigate the inequalities generated by the recommender systems as post-processed approach that balances personalization and learning opportunity equality in recommendations

Sistemes Multiagents Interacció Persona-Màquina Ciència de les Dades i Intel·ligència Computacional Enginyeria del Coneixement i Aprenentatge Automàtic Modelat, Raonament i Solució de Problemes Visió, Percepció i Robòtica. Tecnologies Assistencials Temes Actuals i Pràctica Professional de la IA

Web tracking technologies are extensively used to collect large amounts of personal information (PI), including the things we search, the sites we visit, the people we contact, or the products we buy. Although it is commonly believed that this data is mainly used for targeted advertising, some recent works revealed that it is exploited for many other purposes, such price discrimination, financial credibility, insurance coverage, government surveillance, background scanning or identity theft.

The goal of this project is to analyze and mitigate bias in collaborative filtering recommender systems.

Recent advances in the field of Reinforcement Learning (DRL) are rising a lot of attention due to its potential for automatic control and automatization. Breakthroughs from academia and the industry (e.g, Stanford, DeepMind and OpenAI) are demonstrating that DRL is an effective technique to face complex optimization problems with many dimensions and non-linearities. However, to train a DRL agent in large optimization scenarios still remains a challenge due to the computational intensive operations during backpropagation.

Recent advances in the field of Reinforcement Learning (DRL) are rising a lot of attention due to its potential for automatic control and automatization. Breakthroughs from academia and the industry (e.g, Stanford, DeepMind and OpenAI) are demonstrating that DRL is an effective technique to face complex optimization problems with many dimensions and non-linearities. However, to train a DRL agent in large optimization scenarios still remains a challenge due to the computational intensive operations during backpropagation.

Consulta ofertes d'altres estudis i especialitats