David Williams

David Williams is an Assistant Teaching Professor at Pennsylvania State University, specializing in underwater robotics and machine learning applications. He holds an MFA from the University of Nebraska and a BA from Cornell University. His research focuses on synthetic aperture sonar (SAS), autonomous underwater vehicles (AUVs), and deep learning techniques for target recognition in complex underwater environments. Key areas include underwater object detection, environmental adaptation of sensors, and the development of robust algorithms for mine countermeasures and sediment analysis. Recent work emphasizes improving ATR (Automatic Target Recognition) through spectral partitioning, tone mapping, and transfer learning across sensors.

Dr. Williams has contributed to the design of SAS systems and datasets, environmental modeling for sensor performance prediction, and the integration of seabed context into classification pipelines. His publications span over two decades, addressing challenges in sonar data fusion, false alarm reduction, and the use of 3D convolutional networks for volumetric data analysis. Collaborative projects include NATO Alliance initiatives for autonomous mine countermeasures and UXO (Unexploded Ordnance) remediation, leveraging both simulation and real-world data.

His technical expertise bridges signal processing and robotics, with a focus on enabling autonomous systems to operate effectively in data-starved or complex underwater scenarios. Key innovations include Mondrian detection algorithms, POMDP-based multi-view classification frameworks, and adaptive survey techniques for AUVs.