**Signal and Noise in the Nervous System** //Abstract://\\ The nervous system is a surprisingly noisy place. For example, if one presents the exact same stimulus to an animal many times, and records the activities of their sensory neurons, the responses of those neurons show high levels of trial-to-trial variability. Similar levels of variability are observed elsewhere in the nervous system. At the same time, we have the experience of having robust thoughts and perceptions. So how do our brains generate this robustness from systems of inherently unreliable components? In my talk, I will discuss my work on the retina, the visual cortex, and the hippocampus, each of which reveals strategies that the nervous system appears to use in solving this problem. Along the way, I'll highlight the implications of these results for other neuronal systems, and for the creation of biomimetic technologies. Importantly, I will assume no specialized knowledge on the part of the listener. //Bio://\\ During my undergraduate studies in Physics at Simon Fraser University (Canada), I published papers in inorganic chemistry, nuclear physics, and physics education, before receiving the B.Sc. degree in 2008. Supported by a Fulbright Science and Technology PhD fellowship, I then moved to UC Berkeley to pursue my PhD in Physics. I spent the first 2 years of my graduate training studying cosmology, before transitioning into neuroscience. My early work in neuroscience won me a student research fellowship from the Howard Hughes Medical Institute, which supported my final (4th) year of doctoral studies. I received my PhD from UC Berkeley in 2012, and then took up my current position as Acting Assistant Professor at the University of Washington. In my research, I combine tools from information theory, physics, and computer science, to reveal the circuitry underlying the robust perception and memory functions of the nervous system.