About

Matt Molinare is a highly skilled Machine Learning Engineer with expertise in algorithm development, computer vision, physics modeling, digital signal processing, remote sensing, and software engineering. He recently completed his Master of Science degree in Computer Science, specializing in machine learning, from the Georgia Institute of Technology. Currently working at electromagnetic systems, inc. as a Machine Learning Engineer, Matt is responsible for implementing state-of-the-art convolutional neural networks (CNNs) for one-shot detection of terrestrial combatants and other targets of interest in wide-area synthetic aperture radar (SAR) imagery. He utilizes data augmentation, self-supervised learning, network regularization, and his knowledge of radar phenomenology to mitigate domain bias between simulated and collected data. Prior to his current role, Matt worked as a Scientist II at areté, where he developed a real-time celestial navigation algorithm for use on submarines in GPS-denied environments. He also conducted first principles analyses to aid the development of physics-based, synthetic image generation software capable of simulating atmospheric optics, ocean wave surfaces, and underwater radiometric effects. Matt's educational background includes a Bachelor of Science degree in Physics from the University of California, Los Angeles, and a Master of Science degree in Computer Science from the Georgia Institute of Technology. With 4.95 years of relevant experience, Matt possesses a strong technical skill set that includes ML, research scientist, supervised learning, computer vision, software engineering, and image processing. He is a results-oriented professional with a proven track record of delivering high-quality solutions to complex problems.