Data-driven Discoveries in Neuroscience and Behavior

 

ABSTRACT
Discoveries in modern biology are increasingly driven by quantitative understanding of complex data. The work in my lab lies at an emerging, fertile intersection of computation and biology. I develop data-driven analytic methods that are applied to, and are inspired by, neuroscience questions. Discovering principles of neural computation is of fundamental importance in biology: How does a collection of neurons and their interconnections give rise to such richness and flexibility of function? Projects in my lab explore neural computations in diverse organisms.  We work with theoretical collaborators on developing methods, and with experimental collaborators studying insects, rodents, and primates. The common theme in our work is the development of methods that leverage the escalating scale and complexity of neural and behavioral data to find interpretable patterns. In this talk, I will highlight three research threads. The first focuses on a mathematical framework for spatiotemporal decomposition of large-scale data. The second tackles the challenge of understanding human neural activity "in the wild," outside traditional experimental conditions. The third seeks to uncover principles of hyper-efficient sensing supporting agile flight in winged insects.