As a neuroscience data engineer and project manager, I facilitated multi-lab consortia in deploying automated scientific workflows for large-scale research projects, while managing timelines, budgets, and deliverables and providing mentorship to fellow team members.
My last academic position was as a postdoc, researching the development of attentional mechanisms for visual category learning at The Ohio State University. I've previously worked on computational models of infant looking behavior and human actions and visual motion processing as a postdoc at the University of California, Los Angeles. I received my PhD in Developmental Cognitive Neuroscience from the University of Houston, with a focus on the development of visual attention in infancy and early childhood. My research is largely interdisciplinary, and I try to incorporate methods from different fields, including child development, visual psychophysics, cognitive modeling, machine learning, and statistics.
Domain general, bottom-up driven influences on attentional development
Dyadic social interactions during early development
Selective attention for object perception and word learning
Guided attention for learning spatial contexts and during temporally ordered learning
Top-down biological motion processing of human actions
Developing depth-sensor motion capture software
Use of head-mounted and standalone eye tracking systems
Analytical approaches toward comparing eyegaze scanpath behaviors
Image processing & computer vision techniques for models of visual saliency
Hierarchical Bayesian statistical models
Signal processing techniques for estimating brain/behavioral oscillations during priming tasks