Daniel Urda is an Assistant Professor within the area of Computer Science and Artificial Intelligence at the University of Burgos. He holds a Ph.D. in Computer Science from the University of Malaga (Spain) in 2015, developing predictive models from heterogeneous data sources that can be used in clinical areas. Once he obtained his Ph.D. degree, he was granted a Marie Curie post-doctoral fellowship for 24 months linked to an Initial Training Network (ITN) in Machine Learning for Personalized Medicine as part of a European project. He was hired by Pharmatics Ltd., a spin-off company from the University of Edinburgh whose main clients are the NHS and pharmaceutical companies. He has done one pre-doctoral internship of 3 months in the Liverpool John Moores University and two post-doctoral internships of 1.5 months each, one in the National Institute of Health Medical Research (INSERM) in Paris (France) and another one in the ETH Zurich (Switzerland). In 2017, a research project proposal that he applied for was granted with a short-term post-doctoral fellowship of 4.5 months with Andalucia Tech in Malaga (Spain) at the same time he was working as full-time professor in Marbella International University Centre. From 2018 to 2020, he was employed at the University of Cadiz as part of a Talent Attraction Programme, working as an ASCETI researcher in big data and machine learning projects involving well-known companies such as Airbus, Navantia, Acerinox or Cepsa. Since March 2020, he joined the University of Burgos and, currently, he is doing a third post-doctoral internship of 6 months of duration within the Institute of Computer Science at the University of Tartu (Estonia).

His research career has been focused on the practical application of his machine learning skills, expertise and research findings into medical areas. Part of his Ph.D. studies included the development of an Oncology Information System (OIS), named Galen, that was successfully deployed in several hospitals of Malaga (Spain), thus being a technological transfer of research results to public society. Galen incorporates clinical and more recently genomic data of patients with cancer, offering clinicians a simple and user-friendly interface to query, manipulate and analyze the information stored. During his post-doctoral training in Edinburgh, he collaborated with public and private entities of biomedical areas such as the Universities of Edinburgh or Glasgow, the Western General Hospital in Edinburgh or the Queen Elizabeth University Hospital in Glasgow, the MRC Centre for Regenerative Medicine in Edinburgh, the Erasmus Medical Center in the Netherlands, and companies like Genos Ltd. or Stratified Medicine of Scotland. He was mainly involved in data analysis tasks, having worked in projects related to obesity, colon cancer, major depressive disorder, rheumatoid arthritis, type II diabetes and inflammatory bowel disease.

Daniel has a strong publication record in relevant journals (>20 papers) and international conferences (>25 papers), having edited four Springer books. His current research interests are optimization algorithms and deep learning-based models for small data problems and their applications in different fields such as biomedicine, cybersecurity, transportation/logistics, environmental sustainability, etc. He is currently Guest Editor of four special issues in relevant journals as well as member of the program committee of several international conferences, from which a top-level conference in AI such as IJCAI can be highlighted. Furthermore, he has been member of the organizing committee in OLA’2020 and Co-Chair of the organizing committee in SOCO’2020, CISIS’2020 and ICEUTE’2020.