Ariel Hernan Curiale, Ph.D.
|University of Buenos Aires, Argentina||Bs.Sc ||06/2009|| Computer Science|
|University of Valladolid, Spain||M.Sc.||10/2011||Information Technology and Telecommunications Engineering|
|University of Valladolid, Spain||Ph.D.||2015||Information Technology and Telecommunications Engineering |
2010 - 2012
Research Formation Scholarship
2012 - 2014
Research Formation Scholarship (FPI-Uva)
2016 - 2017
PAR Abstract Scholarship
2023 - 2024
Pilot and Feasibility grant award
Ariel Hernan Curiale graduated from the University of Buenos Aires, Argentina, where he obtained his B.Sc in computer science, in 2009. In 2011 he received the M.S. degree in Information and Telecommunication Technology from University of Valladolid, Valladolid, Spain. He joined the the Laboratory of Image Processing, University of Valladolid, as a Researcher in 2010 with a Collaboration Fellowship granted by the Argentine Ministry of Education and the Caroline Foundation. He also received a scholarship from the University of Valladolid and has been a student visitor at the Erasmus Medical Center, Rotterdam for three months starting in September 2013. In 2015 he received the International Ph.D. degree on information technologies and telecommunications of the Official College of Telecommunications Engineering, University of Valladolid, Valladolid, Spain.
"His research interest are focused on medical applications of image analysis. This includes machine learning and local image structure using tensor analysis, image segmentation, and image registration."
The primary focus of his research has been in the area of signal processing and computer science on the field of medical image analysis, especially developing new algorithms and their application in clinical applications. Major applications of the research have included the areas of image segmentation, image registration and detection of thin-layered structures such as valvular structures. Currently, his work is focused on the field of neural network, especially on Deep Learning techniques for image classification, quantification and prognosis.
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(Ariel H. Curiale)
Jul 1, 2023 - Jul 30, 2024
AI approaches to define emphysema progression risk in PiMZ and PiMS heterozygous subjects
Role Description: The main goals of this project are (1) to define prognostic markers (image density and mechanical) of emphysema progression in subjects at risk of alpha-1 antitrypsin deficiency (AATD), and (2) studying the prognostic value of image-based deep learning approaches in subjects at risk of AATD.
Role: Principal Investigator
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