Deciphering how the brain works requires new methods for acquiring and interpreting activity from large neural populations at single-neuron resolution. In this talk I will focus on new methods that address the statistical challenges in both the acquisition and modeling of neural data. First I will discuss practical methods to further the capabilities of one important and widely used neural recording modality, calcium imaging using two-photon microscopy (TPM). Specifically I will present recent methodological advances for increasing the quality and quantity of inferred neural activity from TPM recordings. These methods include 1) improved data models for robust inference of neural activity from imperfect and missing regressors caused by the challenging noise conditions in TPM data, and 2) a new computational microscopy technique that can image much larger neural populations via stereoscopic projections of neural volumes. Second I will discuss a theoretical analysis of a canonical data model prevalent throughout machine learning and neuroscience: Recurrent neural networks (RNNs). RNNs integrate complex streaming data over time into informative quantities for classification and prediction. Despite their uncanny computational abilities, the properties that impart these abilities are poorly understood. I will present results on one proxy of computational ability, the short-term memory (STM), or the number of recent inputs that can be recovered from the RNN nodes. Using random matrix theory I will show that the network STM can be surprising long (much larger than the network size) indicating that RNNs can perform functions that require access to longer time-scales than previously demonstrated. Bio: Adam Charles received both a B.E. and M.E in Electrical and Computer Engineering in 2009 from The Cooper Union in New York City. He received his Ph.D. in Electrical and Computer Engineering in 2015 working under Dr. Christopher Rozell at The Georgia Institute of Technology, where his research was awarded a Sigma Xi Best Doctoral Thesis award as well as an Electrical and Computer Engineering Research Excellence award. Currently, Adam is a post-doctoral associate at the Princeton Neuroscience Institute with Dr. Jonathan Pillow, where he studies computational neuroscience and data analysis methods. His research interests include neural imaging technologies, inference and tracking of sparse and structured signals, and mathematical modeling and analysis of neural networks.