Dylan Snover serves as a Principal Scientist at BAE Systems, Inc., where he plays a pivotal role in the Cognitive RF group within BAE FAST Labs. With a robust background in research and a Master's degree focused on machine learning for physics-based applications from the...
Dylan Snover serves as a Principal Scientist at BAE Systems, Inc., where he plays a pivotal role in the Cognitive RF group within BAE FAST Labs. With a robust background in research and a Master's degree focused on machine learning for physics-based applications from the University of California San Diego, Dylan is at the forefront of integrating advanced machine learning and artificial intelligence techniques into signal processing and sensor exploitation. His expertise in deep learning frameworks such as TensorFlow and PyTorch, combined with his proficiency in Python programming, allows him to develop innovative solutions that enhance the capabilities of radio frequency (RF) systems.
Dylan’s current projects involve leveraging convolutional neural networks (CNNs) and autoencoders to improve the accuracy and efficiency of signal detection and classification in complex environments. His work not only addresses the challenges of digital signal processing but also explores the potential of GPU computing to accelerate model training and inference, making real-time applications feasible. Additionally, his familiarity with tools like ArcGIS and Google Earth enables him to incorporate geospatial data into his analyses, further enriching the context of his research.
As a thought leader in the field, Dylan is dedicated to pushing the boundaries of what is possible in sensor technology, ensuring that BAE Systems remains at the cutting edge of defense and security solutions. His contributions are instrumental in transforming theoretical concepts into practical applications, ultimately enhancing the operational effectiveness of RF systems in various defense scenarios.
