MSCA-IF: Joint application at the University of Granada. Department of Data Science and Big Data

Professor Jorge Casillas, from the Department of Data Science and  Big Data at the University of Granada, welcomes postdoctoral candidates interested in applying for a Marie Skłodowska-Curie Individual Fellowships (MSCA-IF) in 2019 at this University. Please note that applicants must comply with the Mobility Rule (more information: http://sl.ugr.es/09Qg).

Brief description of the institution:

The University of Granada (UGR), founded in 1531, is one of the largest and most important universities in Spain. It serves more than 60000 students per year, including many foreign students, as UGR is the leader host institution in the Erasmus program. UGR, featuring 3650 professors and more than 2000 auxiliary personnel, offers a total of 75 degrees through its 112 departments and 28 centers.

UGR is also a leading institution in research, located in the top 5/10 of Spanish universities by a variety of ranking criteria, such as national R&D projects, fellowships awarded, publications, or international funding. UGR is one of the few Spanish Universities listed in the Shanghai Top 500 ranking (http://www.arwu.org/), and it is also well recognized for its web presence (http://www.4icu.org/top200/).

Internationally, we bet decidedly by our participation in the calls of H2020, both at partner and coordination. For the duration of the Seventh Framework Programme, the UGR has obtained a total of 66 projects, with total funding of 17.97 million euros, and for H2020, until 2015, more than 25 projects with total funding of more than 6 million euros. Our more than 3,000 researchers are grouped into 365 research groups covering all scientific fields and disciplines.

Brief description of the Centre/Research Group

The Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI Institute) arises as an initiative of the universities of Granada and Jaen to promote the work of a group of Andalusian researchers in Artificial Intelligence (AI), particularly in Data Science and Computer Intelligence. The DaSCI Institute aims to promote research, innovation and technology transfer in AI to our socioeconomic environment, sharing common objectives, knowledge and infrastructures with the other Andalusian agents. To do this, with the strength of its researchers, the institute designs a strengthening plan with 5 axes: the training of PhD, the promotion of our research areas (data science, computational intelligence and technological applications), internationalization, knowledge transfer, scientific dissemination and professional training in AI.

DaSCI tries to surpass its scientific production year after year, creating synergies with companies and training present researchers for greater innovation in the future. The figures of DaSCI in the period 2013-2017 are as follows: 54 advised PhDs, 8 international collaborators, 44 PhD students, 49 R+D grants (11 of them international), 24 contracts with industrial or commercial sector, 88 JCR papers just in 2017, 35 special issues in JCR and 34 highly cited articles (top 1% of all the published articles in JCR journals) in Engineering and Computer Science.

Project description

Machine learning (ML) is living a splendid development. However, there is evidence of the risks that lie behind the misuse of this technology. Much is said about the protection of the data but, what about the opacity of the algorithm? The one that chooses the most vulnerable moment to sell you a product, the one that decides if a family deserves or does not receive a subsidy or the one that intervenes in democratic elections.

ML is increasingly applied to support decisions (and, sometimes, to make decisions directly) in very sensitive problems, from granting a loan or evaluating a curriculum, to deciding the risk of recidivism. Due to bad practice in the use of ML, the design of these algorithms and their application suffers from various biases (sampling, performance, confirmation, anchoring...) that end up giving discriminatory treatment to certain social groups.

Sensitive to this risk, in recent years a line of research in ML known as fairness has emerged with force, proposing measures, methods and approaches to avoid or alleviate these biases and ensure fairer ML. There are multiple ways of measuring the lack of fairness, some of them contradictory (demographic parity, equality of opportunity…). Normally, these criteria are used as constraints rather than as a guide to a more impartial prediction. The risk of fixing restrictions is that in an unknown problem one can not know what degree of equity may be required. In addition, with restrictions, the decision maker can not be provided with a range of possibilities that helps to assess the degree to which it is or is not equitable and what precision is sacrificed with it. This project proposes to face the problem by using optimization algorithms to explore the performance and fairness capability of different ML paradigms.

Research Area

Information Science and Engineering (ENG)

For a correct evaluation of your candidature, please send the documents below to Professor Jorge Casillas (casillas@ugr.es):

• CV
• Letter of recommendation (optional)