To understand the brain, powerful imaging and algorithmic tools need to be developed to meet the unique signal processing and machine learning challenges posed by neurophysiological data, neural imaging, and computational neuroscience. My lab aims to create the next generation of imaging systems and analysis tools capable of overcoming the difficulties posed by the high dimensionality and complexity of neural activity. This goal spans the development both of advanced recording technologies via collaborative designs of hardware and algorithms, and computational and theoretical frameworks for understanding biological and artificial neural systems.
My lab approaches these topics by performing highly collaborative research that draws on both theory- and data-driven philosophies. Specific lab interests span 1) advancing the capabilities of specific technologies, for example multi-photon calcium imaging, via new data science and signal processing methods 2) the theoretical analysis and development of important models in neuroscience, such as recurrent neural networks, and 3) building off these areas to create more general-purpose data science advances with broader impact in applications beyond neuroscience.
- Assistant Professor, Biomedical Engineering
Affiliated Centers & Institutes
- Post-doctoral training, Princeton Neuroscience Institute, 2015-2020
- PhD, Electrical and Computer Engineering, Georgia Institute of Technology, 2015
- ME, Electrical and Computer Engineering, The Cooper Union, 2009
- BE, Electrical and Computer Engineering, The Cooper Union, 2009
June 8, 2023Visualizing connections between nerve cells in the brain could yield insights into how our brains change with learning, aging, injury, and disease.
February 5, 2021Adam Charles joined Hopkins BME as an assistant professor in July 2020. In this interview, Charles discusses his research developing mathematical models and algorithms to understand the brain, his goals for the future, and career advice for students.