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Mario Malavé
Machine Learning Video Engineer at Apple
Dr. Mario Malavé is a highly accomplished professional with a Ph.D. in Electrical Engineering from Stanford University. Mario's expertise lies in image and video signal processing, focusing on acquisition methods, iterative techniques for reconstruction, and deep learning applications.
Throughout his career, Mario has successfully implemented deep learning models in Python using TensorFlow and has specialized in designing optimal undersampling patterns for artifact reduction in iterative reconstructions using MATLAB and EPIC.
One of Mario's significant achievements was enhancing reconstruction speeds by 20x on CPU and 3x on GPU through a deep learning project, surpassing existing implementations on the Berkeley Advanced Reconstruction Toolbox (BART). His work on 3D undersampling designs showcased reduced artifacts compared to previous models.
His notable projects involved utilizing deep learning for the reconstruction of undersampled 3D non-Cartesian MRA datasets, aiming to streamline reconstruction processes and eliminate the need for parameter adjustments in compressed sensing approaches. Mario also excelled in designing efficient 3D non-Cartesian sampling patterns for acquiring cardiac images, enhancing artifact denoising using a regularized low rank algorithm.
In addition to his academic achievements, Mario has a rich educational background that includes a Master's and Bachelor's degree in Electrical Engineering from Georgia Institute of Technology, along with additional studies in Mathematics and Physics.
Mario has held diverse roles in prestigious organizations, including Apple, Samsung Electronics America, and Insight Data Science, where he contributed as a Machine Learning Video Engineer, Senior Research Engineer, and Artificial Intelligence Fellow, respectively. He has also served in various research and teaching positions at esteemed institutions such as Stanford University, Texas Instruments, and NASA.
His wealth of experience and expertise equips Mario to excel in image processing, deep learning, signal reconstruction, and related fields, positioning him as a valuable asset in academic, research, and industry settings.