Profesor Roberto Rosas
Professor Roberto Rosas-Romero received a Ph. D. Degree in Electrical Engineering from the University of Washington (Seattle, Washington, U. S. A.) in 1999. He has been full-time Professor at the Department of Electrical & Computer Engineering, Universidad de las Américas-Puebla (Puebla, México) since 2000. He was a Visiting Professor at the Department of Diagnostic Radiology at Yale University (New Haven, Connecticut, U. S. A.) in 2012. He has been a Fulbright Scholar twice, as a student at the University of Washington and as visiting professor at Yale, respectively. He undertook short-term visits for lecturing and research at the Department of Computer Science in University College London (London, United Kingdom), Department of Computer Science in Durham University (Durham, United Kingdom), CHU Sainte-Justine Research Center in Université de Montréal (Montréal, Quebec, Canada) and Department of Sustainable Technology in Appalachian State University (Boone, North Carolina, U. S. A.).
Professor Rosas was a recipient of funds from the Mexican Government to increase the coverage area of the Telecommunications Network in the State of Puebla in Mexico by introducing multiple wireless links. As a result of this project, internet services for data, voice, and video are reaching isolated communities with different applications such as in education and health. He collaborated with faculty and students from Appalachian State University to provide a health clinic in a rural community (Puebla, México) with technology to transform solar radiation into energy for hot water and electricity. He has been involved with people from research groups such as the Image Processing and Analysis Group at Yale and the Vascular Imaging Lab at the University of Washington.
His research interests are Signal Processing, Computer Vision, Pattern Recognition, Machine Learning, and Medical Image Analysis. His research has been applied to ultrasound image segmentation, forest fire detection from video signals, micro-aneurysm detection in fundus eye images to assist in the diagnosis of diabetic retinopathy, recognition of human actions in video signals, predictive models for time series in finance (stock market), prediction of epileptic seizures based on brain waves, detection of deafness in newborn cries, alpha matte extraction from green screen images, detection of micro-calcifications on mammograms as a pre-diagnosis tool of breast cancer, transiting exo-planet identification, Parkinson's disease detection at early stages by analyzing voice, classification of magnetic resonance images to assist Parkinson's disease diagnosis, classification of skin burns in color images, forecasting models of the number of daily Covid-19 cases, monitoring of the dehydration process of apple snacks. His research results have appeared in the following selected publications and research projects: