Dr Miguel Molina-Solana is currently a Marie Curie Research Fellow at the Data Science Institute (DSI) at Imperial College London, working on the DATASOUND project. Before, he was a Research Associate at the DSI, and a postdoc researcher at the Department of Computer Science and Artificial Intelligence of University of Granada. His research experience comprises work in the areas of Data Mining, Machine Learning and Knowledge representation applied in different areas such as Music, Energy management and Healthcare.
He holds a PhD and a MSc in Computer Science from University of Granada, and a M.Sc. in Soft Computing and Intelligent Systems. He has been awarded with several research grants from University of Granada, Spanish Government, Spanish National Research Council and the European Commission. He completed Intermediate studies in piano.
Miguel has done research stays at the Artificial Intelligence Research Institute (IIIA-CSIC) (Barcelona, Spain) in 2007, the Department of Computational Perception at JKU University (Linz, Austria) in 2009, the Department of Computing at Goldsmiths, University of London (United Kingdom) in 2010, and the School of Electronic Engineering and Computer Science at Queen Mary University of London (United Kingdom) in 2014.
In addition to lecturing at University of Granada and Imperial College, Miguel has authored more than 30 publications and participated in several research projects (of European, national and regional scope).
Amador J, Oehmichen A, Molina-Solana M, Characterizing Political Fake News in Twitter by its Meta-Data
Molina-Solana M, Birch D, Guo Y-K, 2017, Improving data exploration in graphs with fuzzy logic and large-scale visualisation, Applied Soft Computing, Vol:53, ISSN:1568-4946, Pages:227-235
et al., 2017, Data science for building energy management: A review, Renewable & Sustainable Energy Reviews, Vol:70, ISSN:1364-0321, Pages:598-609
et al., 2017, Information fusion from multiple databases using meta-association rules, International Journal of Approximate Reasoning, Vol:80, ISSN:0888-613X, Pages:185-198
et al., 2016, Visualizing Dynamic Bitcoin Transaction Patterns, Big Data, Vol:4, ISSN:2167-6461, Pages:109-119