ABOUT ME

I'm a Senior Manager at Evidation Health, where I lead our Data Science and Analytics Engineering teams for our Enterprise programs. I have a PhD in Dynamical Neuroscience from the University of California, Santa Barbara, where I was a proud member of the Miller Memory Lab.  Before graduate school, I attended the New Mexico Institute of Mining and Technology where I double majored in Biology and Psychology and minored in Chemistry.

I have received several prestigious fellowships and awards, including the National Science Foundation’s Graduate Research Fellowship, the Doctoral Scholars Fellowship from the UCSB Graduate Division, the SAGE Center’s Graduate Student Fellowship, and the Macey Scholarship. I was also selected to attend and present at the 65th annual Nobel Laureate Conference in Lindau, Germany.

My husband and I moved from Santa Barbara to Albuquerque in 2019, and have since had two energetic boys. I like reading, home reno and design, data visualization, and — most recently — learning about the cellular mechanisms of chronic disease.


These are the current flow models of 15 participants. For each participant, we collect anatomical scans of their brains, then use Freesurfer to create 3D meshes of their brains. Then we select brain targets (for my experiments, we target the left an…

These are the current flow models of 15 participants. For each participant, we collect anatomical scans of their brains, then use Freesurfer to create 3D meshes of their brains. Then we select brain targets (for my experiments, we target the left and right inferior frontal gyrus) and set up an electrode montage to maximally stimulate those targets. Lastly, we simulate current flow in participants' brains given their unique electrode montages with SimNIBS. As you can see above, there is a lot of variability when it comes to the amount of current delivered to the cortex. The advantage of modeling current flow is that we can account for these differences in current intensity at the cortex when analyzing the results of tDCS.

ABOUT MY RESEARCH

We often form beliefs based on information that is incomplete, uncertain, ambiguous, and that changes with time. There's an incredible amount of variability in how people interpret information and integrate it into their belief systems, and beliefs often form the basis of decisions that have far reaching consequences. (Take for example the fact that jurors' beliefs that a defendant is guilty can send an innocent person to jail, which has happened in at least 353 cases.)

I investigate how frontal neural networks contribute to belief formation and belief updating. I use high-definition transcranial direct current stimulation (HD-tDCS) to simultaneously increase activity in one frontal lobe and decrease activity in the opposite frontal lobe as participants complete tasks in which they evaluate and integrate evidence to make decisions. In one task, participants report their opinions on different ballot measures as they are presented with arguments in favor or in opposition to the measures. In another task, participants hear evidence from real criminal court cases to decide whether a defendant is guilty or innocent. 

Based on previous studies on different patient populations, we expect that artificially biasing activity to the left or right hemisphere will influence how people update their beliefs. We expect that biasing activity to the left hemisphere will make people more certain in their beliefs while biasing activity to the right hemisphere will make people more sensitive and responsive to conflicting evidence.


ABOUT THE BLOG

Now that I’ve been a manager for a few years, I’ve missed working directly with data and feeling the satisfaction of a telling a story with a solid graph.

I’ve also become deeply curious about what factors contribute to a long healthspan and I’ve been changing my lifestyle bit by bit in accordance with what I learn.

This blog is an outlet for my interests in data visualization, health, research, self-experimentation … and maybe a few sketches here and there.