I come from a small town in Bulgaria and moved to the US about 10 years ago to be a scientist. I went to college at DePauw University in the little town of Greencastle, IN. There I learned how big a corn field really can be! I spent some time doing research in Boston at the Harvard Medical School Immunology labs, which helped direct me towards grad school. I received my PhD from Baylor University in good ol’ Texas and moved to UC Davis as a postdoc to find a place with rigorous population genetic and data science resources.
As a postdoc I have cultivated a strong affinity for big data. There’s nothing like generating or finding a large dataset and then diving in to try and understand what it all means. That being said, I am still drawn by the original questions that got me into science. Those I break down into several categories:
Impacts of rapid evolution
Previous and current work
I am thrilled to study the process of evolution and a little more generally, how population respond to stressors. During my dissertation I focused on the rapid adaptation of Gulf killifish (left) to anthropogenic pollution with halogenated aromatic hydrocarbons (HAHs). I studied the physiological divergence between population of killifish and coupled that with genomic data to understand the mechanism and source of adaptive genetic information (see CV for more).
Currently, I focus on understanding the population genomics of population decline in Pacific herring from Prince William Sound (right). I am using genetic data in a time series from before and after the collapse for serveral physically and genetically proximate populations to understand the genomic impacts of an event of such magnitude.
I would like to further continue understanding impacts of rapid adaptation across species. My future directions will involve exploring genetic admixture as a source of adaptive divergence in short time-scales. I’d like to use a combination population genetic models and machine learning algorithms to better understand the genomic architecture of rapid adaptation and the impacts it has on gene flow between populations and species.
Previous and current work
My interests here have encompassed the understanding of how contamination affects natural populations. I have used multi-matrix measurements of chemical contamination and linked it to physiological shift in contaminatin resistance in the Gulf killifish model. Thus, we built an impact map of chemical contamination and how far do the population-wide effects that we can measure extend away from contaminated zones.
In addition, I’ve worked on further understanding how early life contamination affects tolerance (see CV for more info). Briefly, this project shows how priming of detoxification pathways allows further tolerance for the duration of exposure.
I would like to explore the genetic basis of individual sensitivities to classes of contaminants. Current methods for human health models rely on GWAS to identify targets, but GWAS methods often can have limitations (see awesome article by Graham Coop about polygenic scores). Thus, I aim to use methods that would control for the limitations of GWAS in order to attempt pinpointing genomic region associated with contaminant sensitivity.
I very much enjoy working with large datasets. Be it genomic data or not, I love to delve into and tease apart data. I am greatly in debt to all of the fantastic data scientists at UC Davis for all that I have learned and continue to learn on a daily basis. Thus, my hope is that I continue educating and doing fun analyses on big data. For some current examples please see the “Powerful data” tab here for some of the hobby work I am doing.
My hope is that once I leave my postdoc and initiate a lab, I will concentrate a core of that lab on large data analysis. In addition, I am a Data Carpentry certified instructor and I hope to bring with me a set of workshops and classes that I can use to strengthen the data science initiative at the university I join.
What free time I have I spend on 5 things: family; hiking; lifting heavy things; photography; fun data analysis. For some examples, please see the Powerful data and “Capturing nature” sections of this website.