Nicholas Ketz

Contact info

Information and System Sciences Lab
HRL Laboratoires
Building 254, 3G32
3011 Malibu Canyon Rd.
Malibu, CA 90265


nick [dot] ketz [at] gmail [dot] com

I am a recent graduate from the Computational Cognitive Neuroscience Lab at the University Colorado Boulder with Randy O'Reilly. I did my undergraduate at the University of Minnesota in Minneapolis where I studied Physics, and got involved in computational modeling with Chad Marsolek. I then spent a few years at New York University with Lila Davachi using fMRI to explore the neurobiology of episodic memory. Right now I'm mostly doing work related to cognitive control over memory and its implementation in computational models of the Hippocampus and Prefrontal Cortex.

Find out more through my publications and CV, or more general skills in my resume.

Research Interests

In general I've been working to understand different learning and memory systems and how they can be related to underlying neurobiology. In particular, I'm interested in how the brain encodes new information, how its able to both index and retrieve that information and exert control over these processes in a willful way. This involves several sub-domains of psychology, neuroscience, and cognitive science including memory, semantics, perception, cognitive control, reward and decision making.

I try to use data collected from human participants to inform computer simulations of the neural systems that support the empirically observed behaviors. This includes data collected from behavior in various psychological tasks, functional Magnetic Resonance Imaging (fMRI), and Electroencephalography (EEG). The models I use to simulate these measures are generally neural networks constrained by the known architecture of biological systems.

I'm also more generally interested in applying my research towards understanding learning algorithms outside of biological systems. The various neural networks I use in my work can be applied to a variety of data intensive problems. Similarly, dimensionality reduction is one of the corner stones for how memory encoding is believed to work. Applying the knowledge gained from biological memory systems can provide new approaches to the searching and indexing of information in general.