PhD candidate, Neural Computation and Machine Learning, Carnegie Mellon University
Like my PhD advisor, Robert E. Kass, my work is in Statistics and Machine Learning methodology applied to Neuroscience problems. My thesis is on graphical network modeling of brain region interactions during working memory tasks. I am also interested in the cognitive science of learning and I’m part of the CMU Teach Stat research group.
About my work:
Complex behaviors like memory recall require the coordination of multiple brain regions; I study cross-region coordination using network graphs, where the nodes represent noisy activity from different brain regions. I develop methods for graphical model structure learning for high-dimensional data.
Graduate teaching assistant and invited lectures for 36-759, CMU course on Statistical models of the brain.
Research Scientist: National University of Singapore and Johns Hopkins University
B. S. in Electrical Engineering, Washington State University
Programming languages: Matlab, R, Python, LaTeX
IRB and IACUC research protocols with human and non-human subjects
Completed joint Neural Computation/Machine Learning PhD coursework at CMU
CMU grant, GuSH cross-walk, for a statistics education research project 2019
CMU Presidential Fellowship, 2016-17, 2017-18.
Winner of three minute video slides, Torus Graphs for multivariate phase coupling, retreat 2018, Center for the Neural Basis of Cognition, CMU.
President’s Honor Roll at Washington State University, Fall 2008 - May 2012
Academic excellence scholarship from El Salvador to study abroad, 2008-2012. Only three students were awarded this scholarship in 2007, based on academic record and future promise.