If you are interested in any of these topics, contact me.
PhD Topic 1: Dynamic Team Formation
The concept of this PhD project is to create innovative co-operative learning algorithms that will be applied to learning processes. Traditionally, the education system gravitates around memorization, assimilation and theory. But the shifting of the global economy has introduced new market demands that forces to restructure education to encourage entrepreneurship, creativity and risk-taking. Co-operative learning, based on teamwork is the path to follow. Our investigation proposes to develop and transfer Artificial Intelligent methods for team formation, individual assessment and personalised learning that will become software modules to be integrated into emerging e-learning platforms.
The work starts from current state-of-the-art results on static team formation and composition developed in a previous industrial PhD by IIIA-CSIC and CMT. This project is aimed at extending the previous work by addressing the research of Artificial Intelligence methods for a new fundamental way of producing teams. We will study the development of balanced dynamic team formation techniques where the duration of tasks, the sets of tasks and possibly the set of students vary along time. Together with the dynamic team formation problem, the work will tackle the problem of automatically assessing individual competencies and the problem of adapting learning contents sequencing to each individual. A particularly interesting problem is ‘Alternating education’, that is, an instructive experience, co-planned by the school system and other institutions, to give students formative opportunities, with a high and qualified profile. Here the question is what team of students to send to a company to work with an already organised team to solve a particualr task.
These new methods will be implemented and validated at the industrial level through pilot tests in a network of schools. In order to facilitate the transfer of technology, we will create software components that can be integrated into the Human Resources, Operations and Learning Systems of different sectorial organizations to help them to fulfil the challenge to educate the new generations in a more dynamic and vibrant context.
Type of PhD. Industrial Doctorate in collaboration with Enzyme.
Incorporation: Summer 2019.
Advisors: Juan Antonio Rodriguez and Carles Sierra
PhD Topic 2: Tell me what you do and I will tell you who you are
The reputation of an entity, individual or company, is usually measured as an aggregate of opinions expressed by third parties about their behavior. In particular, opinions on their behavior in the fulfillment of commitments and contracts, or opinions on the satisfaction of behavioral expectations. The latter is very common when there is no explicit commitment (e.g. to give good food in a restaurant).
Normally, it is understood that opinions are expressed explicitly (e.g. Tripadvisor, eBay, Amazon) and in most cases opinions express personal perception of the quality of products and services. That is, explicit opinions about behavioral expectations. However, there is a practically unexplored field that is the extraction and aggregation of implicit opinions from the behavior of the entities. Also, the combination of explicit and implicit opinions has received little attention. To better understand what we are referring to with an implicit opinion, let’s think about the case of soccer. If Manchester United beats Chelsea by 5 to zero, we can consider that at the end of the game the opinion of Manchester is that Chelsea is a very bad team and Chelsea’s opinion is that Manchester is a very good team. The acts of the entities and the interactions between them (e.g. through social networks, financial transactions) are sources of implicit opinions that can be of great value to predict their future behavior.
The objective of this thesis is therefore (i) the development of methods for the extraction of opinions implicit in the interactions among entities, (ii) the development of methods of aggregation of implicit and explicit opinions, and (iii) the construction of a predictive model of behavior based on the reputation of the entities.
Type of PhD. Industrial Doctorate in collaboration with Strands.