**Faculty Candidate - Joint Search with MIT Institute for Data, Systems, and Society (IDSS)**

There is a recent renaissance in the development of new tools for interrogating the brain's structure and function at finer spatial resolutions, with faster readouts of the activity of more neurons, and over larger brain volumes. As a result, we are now faced with a neural data deluge: raw data is generated at rates that most labs are not equipped to process, let alone extract biological insights from.

In this talk, I will describe my efforts in developing frameworks and algorithms to tackle this deluge of data. First, I will describe my work in developing data analysis pipelines to parse TB-scale X-ray microtomography datasets from large unsectioned brain volumes. Second, I will show how unsupervised learning approaches can distill high-dimensional neural recordings down into a simpler format where neural structure and function are revealed. Throughout, I will provide examples from a variety of cellular-level imaging methods, including: X-ray microtomography, serial two-photon tomography, and electron microscopy. I will conclude with my plans to develop computational approaches for integrating X-rays, light, and electrons to interrogate the structure and function of large brain volumes at the nano and mesoscale.