Academic Summary

My first immersive neuroscience research experience was in the National Center for Microscopy and Imagine Research (NCMIR, UCSD) under Prof. Mark Ellisman. I studied astrocytes and got my first exposure to quantitative tools enabling large scale comparisons not otherwise possible.

I pursued a PhD in Neurobiology and Behavior under Prof. Horacio de la Iglesia at the University of Washington, Seattle. I was interested in understanding changes in the brain that precede changes in behavior, and his models of circadian and ovarian rhythms provided an ideal system to explore predictable modulation of neural circuits and associated behaviors.

In Horacio's lab, faced with complex time series patterns, I began to seriously build my own skills in computational biology, based on the role models I'd seen and collaborated with in Mark's lab.

In Horacio's lab I also came to recognize how far we are from complete, equitable knowledge. I really couldn't believe how little was published about women or female subjects. I still can't. Nor did I understand why "women's health" was viewed as a niche field when it pertains to half the population. I still can't.

I was a postdoc under Prof. Lance Kriegsfeld at UC Berkeley. I worked to expand my understanding of neuroendocrine systems while also expanding my computational analytic skills. I was fortunate to be part of many collaborations, and to meet and gain exposure to many approaches, fields, and view points.

Lance also introduced me to Prof. Irving Zucker, who was leading a series of efforts providing scientific and numerical support for inclusion of female subjects in research, and for analysis of sex as a biological variable (as opposed to throwing every subject into the same analyses blindly). I continue to work along this avenue, and encourage others to consider numerical analyses of all forms of diversity. 

Humanity is no longer data poor. Categories like "sex" and "race" - perhaps necessary when we did things on paper - do not capture nearly enough of the differences between us to support precision public health or precision medicine. Better data-driven approaches abound, and simplistic binary or box models don't fit the complexity of natural surfaces well enough to make us all healthy and happy.

At Berkeley I received two federal grant reviews that motivate me to work harder on tech education and promoting health equity:

     1. "He keeps using this word, 'data science'. I think he made it up." 

2. "This project [about pregnancy] seems important, but as a young researcher, you should know that working with female subjects can be hard. Have you considered doing this project in males?"

Anyone reading this is likely intelligent and well meaning, like these reviewers were. Yet we are all more ignorant than we are knowledgeable, because the world is big. There can never be enough openness and exchange of perspectives for us to get everything right, but more is better.