The brain is a remarkably complex network of neurons, and many functions and dysfunctions of the mind cannot be localized to any particular part of the brain. Further, network activity changes in time at every spatial scale, from molecular dynamics of single synapses to coordinated oscillations across brain areas to circadian rhythms. Distilling spatial-temporal coherent patterns from large scale, noisy measurements is vital to understanding how networks of neurons give rise to behavior.
I am inspired by system-level questions in neuroscience: How can we describe the multi-scale connective topology of brain areas? What functional metrics differentiate a neuronal network before and after learning? To tackle these and related questions, I leverage recent mathematical advances in the fields of dimensionality reduction and compressive sensing.
2006, B.S. in Biology, California Institute of Technology
2012, Ph.D. in Molecular Biology and Neuroscience, Princeton University