Pre Print

Pre-print research currently explores diverse applications of pre- and post-processing data analysis techniques across various fields. Key areas of focus include improving the accuracy and robustness of machine learning models, particularly in noisy data environments, and enhancing human-AI interaction through methods like differential outcomes training and language-based conventions. These advancements have implications for diverse applications, ranging from medical diagnosis and treatment monitoring to safer infrastructure and improved assistive robotics. The development of efficient algorithms and model architectures, such as those based on Inception and TransMorph networks, are central to achieving these goals.