Overview: I aim to understand how organisms adapt in response to the environment using data science methodologies. I lead research projects that utilize a variety of data types - including genomic, image (2D, 3D, microscope and morphometrics), text, and geospatial data. I also have a strong background in advocating, training, and implementing data science strategies (open tools, data, and education) to fuel scientific access and innovation. Always interested in collaboration.
Data Science Practice · Bio and Environmental data · Genome Evolution · Genetic Regulation
Genetic Regulation of Plant Morphology: Underlying much of my research has been the question - How do organisms get their shape? One of my main interests has been attempting to understand the evolutionary forces which regulate plant architecture including flower morphology, wood development, leaf evolution, and phyllotactic patterning. I use both molecular and data intensive techniques to quantify both global genetic patterning and the harnessing of high-throughput phenotyping techniques.
Genome Evolution: As a Postdoc in Michael Eisen’s lab at UC Berkeley, I employed comparative computational genomic analysis and confocal microscopy to understand the evolutionary constraints acting on DNA promoters and enhancers. How do these mysterious DNA noncoding regions, such as enhancers, function in controlling spatiotemporal gene transcription, and ultimately to direct organismal morphology and development? Taking advantage of the rapid generation time, small genome size, and ease of genetic engineering, I used Drosophila (fruit flies) as a system to explore how DNA sequences are grammatically and syntactically defined. I also created strategies to microscopically image Drosophila species, including creating novel histological protocols and building several bioengineered Drosophila imaging lines for 4D confocal microscope imaging and analysis.
Increasing the Usability and Design of Biological and Environmental Data Collections: All my research interests converge on my excitement for how data can explain the world around us. Increasingly data is being curated for public use and these open data collections can allow unprecedented scientific exploration. This data, coupled with the increase in computational power, allows researchers for the first time to model emergent trends in biological systems and patterns at unprecedented rates and accuracy. My goal is to increase the usability, sustainability, and value of this data. I do this by researching how the information is presented, designed, and used for the users of this data. In addition, I aim to use these collections in my own research practice as much as possible. I am especially interested in building research projects that combine multidisciplinary research domains. I have worked with research teams, software developers, designers, and natural history museums to use and value this data for the immense resource they are.
Data Science as a Practice: How is Data Science actually performed in a research setting? How does the influx of data affect our research communities? And how do we best leverage this data to fuel our research? We as a research community may not agree on the exact definition of what data science, even means, but we all clearly see how the increase in data is changing how research is performed. With this monumental shift we must understand and adapt our research practices. I have worked to establish standards and practices within data science communities including data management and ethical data science training. I am an open science advocate and actively work to make my research and the communities I work with strive for a more inclusive, transparent, and reproducible future in data.