Alessia Sarica is Assistant Professor of Applied Medical Technology and Methodology at the Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy. She was fro three years a Post-doc Research Fellow at the Neuroscience Research Center, Magna Graecia University of Catanzaro, Italy. She was for three years a Post-Doc at the Neuroimaging Unit, Institute of Bioimaging and Molecular Physiology-CNR, University of “Magna Graecia”, Catanzaro, Italy. She’s a Lecturer Professor of “Esercitazioni Informatiche e Telematiche”, I Anno, I Semestre, from the 2015, Scuola di Farmacia e Nutraceutica, Università “Magna Graecia” di Catanzaro.
She received the Bachelor degree in Biomedical Engineering from the “Magna Graecia” University of Catanzaro, Italy, in 2011 and the Master degree (with honors) in Biomedical and Computer Science Engineering from the same university in 2009. She received the PhD in Biomedical and Computer Engineering at the “Magna Graecia” University of Catanzaro in March 2015, with the thesis “Advanced Machine Learning and Data Mining techniques for Knowledge Discovery from Neuroimaging”. During her PhD, she has been a visiting Scholar at the School of System Engineering of the University of Reading, UK, from the September 2013 until the July 2014.
Alessia was students’ Tutor in 2011 for the course “Fundamentals of Informatics” of Computer Science and Biomedical Engineering at the Magna Graecia University of Catanzaro, Italy, and She performed teaching activities for the course “Technical of Research in Systems Management of Knowledge-based Evolved System” at the University of Calabria, Cosenza, Italy, Department of Mathematics, and for the course “Expert in the design and development of services and open source software in the context of Smart Cities” at the University of Calabria, Cosenza, Italy, Department of Electronics, Informatics and Systems Theory.
She published more than 40 works and her research interests are focused on Neuroscience. In particular, they include structural and functional neuroimaging, feature extraction methods, pattern recognition and machine learning, data mining algorithms for heterogeneous data, data processing strategies, high dimensionality reduction and advanced feature selection.
In 2014, Alessia Sarica designed, developed ad publicly released a novel tool for the analysis and management of data from neuroimaging, called K-Surfer (Sarica, A.,Di Fatta, G., & Cannataro, M. (2014, August). K-Surfer: A KNIME Extension for the Management and Analysis of Human Brain MRI FreeSurfer/FSL Data. In International Conference on Brain Informatics and Health (pp. 481-492). Springer International Publishing; Sarica, A., Di Fatta, G., & Cannataro, M. (2014). K-Surfer: A KNIME-based tool for the management and analysis of human brain MRI FreeSurfer/FSL Data. Frontiers in Neuroiriformatics, (3)).
She participated at the CADDementia challenge, a competition of machine learning algorithms for the classification of the Alzheimer’s disease (Bron, E. E., Smits, M., van der Flier, W. M., Vrenken, H., Barkhof, F., Scheltens, P., … Sarica, A., . . . , Klein, S. (2015). Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge. NeuroImage, 111, 562-579). Alessia participated to another important competition on the neuroimaging field, the ISMRM 2015 Tractography challenge, in which the state-of-the-art tractography pipelines were evaluated and compared (Maier-Hein, K., Neher, P., Houde, J. C., Cote, M. A., Garyfallidis, E., Zhong, J., …, Sarica, A., … & Hilgetag, C. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nature Communications, 8, 1349.)
Her work on the application of the Random Forest algorithm for the classification of the Amyotrophic Lateral Sclerosis from diffusion tract profile data, was published by Human Brain Mapping (Sarica, A., Cerasa, A., Valentino, P., Yeatman, J., … & Quattrone, A. (2017). The corticospinal tract profile in amyotrophic lateral sclerosis. Human brain mapping, 38(2), 727-739.). Indeed, she has a wide expertise in ensemble learning and in particular in Random Forest and recently presented a systematic review about this algorithm for the prediction of Dementia (Sarica, A., Cerasa, A., Quattrone, A. (2017). Random Forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review. Frontiers in Aging Neuroscience, 9, 329).
In the last year, Alessia Sarica was the organizer and Special Guest Editor of a Special Issue hosted by the Journal of Neuroscience Methods, titled “A Machine learning neuroimaging challenge for automated diagnosis of Mild Cognitive Impairment” (Sarica et al., 2018). In this competition, the scientific community was invited to apply their machine learning approaches on a pre-processed set of morphological magnetic resonance images (MRI) obtained from the international Alzheimer’s disease neuroimaging initiative (ADNI) database. The aim was to evaluate the accuracy in predicting the conversion from the Mild Cognitive Impairment to the Alzheimer’s disease.
She owns a European patent for the elaboration of clinical, biochemical and 3D ultrasound data for the prediction of the ovarian age of woman, and with this new methodology she came third in a regional competition for the best proposed commercial idea (StartCup Calabria 2013). In 2010, she proudly won the Italian national contest for Bachelor degree thesis in Computer Science Engineering concerning Java language programming, organized by Javaday IV Rome 2010, Faculty of Informatics Engineering University of Roma Tre, in collaboration with IBM Italy.