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Nastaran Mohammadian Rad is a PhD student in computer science at the university of Trento, working in MPBA lab at Fondazione Bruno Kessler (FBK), Italy. She received her bachelor degree from Ferdowsi university of Mashhad-Iran in computer-hardware engineering. For her bachelor thesis, she worked on positioning acoustic source in a heterogeneous environment using Time Difference of Arrival (TDOA) algorithm. She continued her study in artificial intelligence at the university of Tabriz, Iran. As her master's thesis entitled “Multiple Sclerosis (MS) Lesions Detection on Magnetic Resonance Images (MRI)”, brain segmentation method alongside a classification method based on principal component analysis is employed in order to improve the performance of MS lesion detection. Within her PhD study, she has mainly focused on applying machine learning algorithms, specifically deep learning algorithms, on Inertial Measurement Units (IMU) data in order to detect Stereotypical Motor Movements (SMM) in autistic children. Her research interests are including of Machine Learning (particularly deep learning), wearable sensors, and ubiquitous computing.
Mohammadian Rad, Nastaran; Kia, Seyed Mostafa; Zarbo, Calogero; van Laarhoven, Twan; Jurman, Giuseppe; Venuti, Paola; Marchiori, Elena; Furlanello, Cesare,Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders,in «SIGNAL PROCESSING»,vol. 144,2018, pp. 180 -191
Rad, Nastaran Mohammadian; Kia, Seyed Mostafa; Zarbo, Calogero; Jurman, Giuseppe; Venuti, Paola; Furlanello, Cesare,2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW),IEEE,2016, pp. 487-494, (2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW),Barcelona,