- BSc, Telecommunications Engineering, University of Sevilla, 2006
- MSc, Electronics, Signal Processing and Communications, University of Sevilla, 2008
- PhD, Electronic and Electrical Engineering, University of Sevilla, 2011
Carlos comes from a Castilian family with a proud tradition in engineering. “My great grandfather counseled King Alfonso 13th to build the first line of Madrid’s metro at age 31, which is my age now. I am very honored that the current circumstances are empowering my family to make another contribution to our people.”
Carlos S. Mendoza earned his doctoral degree in May 2011, after conducting research in segmentation of computed-tomography images for surgical planning, in shape analysis for retrieval of bone allografts with application to allotransplantation surgery, and in texture characterization of dermoscopic images for computer-assisted diagnosis of malignant melanoma. During his doctoral studies, Carlos spent nine months at the Surgical Planning Lab and the Applied Chest Imaging Lab – Brigham and Women’s Hospital, Harvard Medical School. His research at the Brigham included the development of a novel methodology for fully automatic assessment of pulmonary emphysema in COPD patients, resulting in the discovery of new genetic factors and epidemiologic traits in COPD.
His PhD thesis title was “Image Processing in Medicine – Advances for Phenotype Characterization, Computer-Assisted Diagnosis and Surgical Planning”. Between January 2012 and February 2013, Carlos held a Postdoctoral Fellowship at Sheikh Zayed Institute for Pediatric Surgical Innovation – Children’s National Medical Center (Washington DC, USA). His research at Children’s included automatic assessment of cranial dysmorphology in young infants using statistical shape models, and computer-assisted diagnosis of renal pathologies from the automatic segmentation of renal structures in echographic images.
As an M+Visión Fellow, Carlos wants to focus on the translational aspects of biomedical image computing. “I’m interested in using statistical methods to characterize human anatomy and geometry; using artificial intelligence to distinguish good movement from pathological movement.”
Of the Fellowship experience so far, Carlos said that he “didn’t expect … to be given that degree of independence to do whatever you want with your project with whatever combination of research teams and locations.”