Research


Overview: I aim to understand how organisms adapt in response to the environment using data science methodologies. I have lead research projects that utilize a variety of data types - including genomic, image (microscope and morphometrics), text, and geospatial data. I also have a strong background in advocating, training, and implementing open science strategies (open tools, data, and education) to fuel scientific reproducibility, access, and innovation. Always interested in collaboration!



Main Research Areas

Data Science Practice · Bio and Environmental data · Genome Evolution · Genetic Regulation


Increasing the Usability of Biodiversity and Environmental Data: All my research interests converge on my excitement for public biodiversity, biological, and environmental data. The value of this data is immense. This data, coupled with the increase in computational power, allows researchers for the first time to model emergent trends in biological systems and patterns of biodiversity at unprecedented rates and accuracy. My goal is to increase the usability, sustainability, and value of these data types through through research, data integration, community building, training and establishment of data standards. I am especially interested in the intersection in building research projects that combine multidisciplinary scientific domains and data types. I have worked with natural history museums, classrooms, and intern training programs to help achieve these goals.

Data Science as a Practice: How is Data Science performed for research within academia? How does the influx of data affect our research communities and how do we best leverage this data to fuel our research? We as an research community may not agree on the exact definition of Data Science, but we all clearly see how the increase in data is changing how research is performed. With this monumental shift we in research must understand and adapt our research practices. I have worked to establish standards and practices within our community including computational reproducibility, data management, career paths, and education. 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 scientific future.

Genome Evolution: As a Postdoc in Michael Eisen’s lab at UC Berkeley, I employ comparative genomics techniques and confocal microscopy to understand the evolutionary constraints acting on enhancers. How do noncoding regions, such as enhancers, function in controlling spatiotemporal gene transcription? I use Drosophila as a system to explore how enhancer DNA sequences are syntactically defined and investigate the mysterious evolutionary forces guiding enhancer divergence between species. I also created strategies to microscopically image Drosophila, including creating several Drosophila imaging lines and build and employed confocal image analysis techniques using open source tools.

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 harness high-throughput phenotyping techniques.