Aidan Charles

Adam Charles is an Assistant Professor at Johns Hopkins University whose research focuses on developing advanced imaging systems and computational methods to address the unique challenges of neurophysiological data and neural imaging. His work lies at the intersection of data science, signal processing, machine learning, and computational neuroscience, with the goal of understanding complex neural activity through both theoretical and data-driven approaches. His lab designs next-generation recording technologies by jointly developing hardware and algorithms, enabling more effective measurement and interpretation of high-dimensional neural signals. A central focus of his research is advancing neural imaging techniques, such as multi-photon calcium imaging, through novel signal processing and data analysis methods. In parallel, he investigates theoretical models of neural systems, including recurrent neural networks, to better understand the principles governing biological and artificial intelligence. By integrating these efforts, his research contributes not only to neuroscience but also to broader data science methodologies with applications across imaging, engineering, and computational analysis.