Accomplished and innovative data scientist with 7+ years of experience combining computational and quantitative analysis skills to translate complex datasets into actionable insights. Demonstrated success in deploying data-driven strategies to optimize processes by applying advanced data analysis and machine learning techniques in R and Python.
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|Master's in Applied Mathematics
Arizona State University | May 2019| - | :------ | ------: |
|Bachelor's in Mathematics & Biology
University of NC, Greensboro | May 2017|
- Developed a multi-faceted scoring system in Python to pinpoint selectors whose activity was highly correlated with current targets, increasing efficiency of new target discovery by 68%.
- Conducted time-series analyses to detect anomalous adversarial activity and classify future data points.
- Uncovered key connections by using graph theoretical methods to analyze and identify patterns within large-scale networks representing target traffic.
- Trained natural language processing model to accurately annotate and extract pertinent information from unstructured text input for downstream utilization in machine learning pipelines.
- Leveraged big data analysis methods in Apache Spark to load, clean, and summarize an 80+ TB cybersecurity dataset into digestible insights, resulting in improved prioritization of customer targeting efforts.
- Built interactive HTML data visualizations to effectively summarize key findings from complex information.
- Facilitated recurring team meetings to encourage collaboration and created internal website to serve as a central repository for team resources.
- Constructed analysis pipelines to streamline the identification of cancer risk-associated variants from multi-dimensional genomic data.
- Created custom Bash and R scripts for cleaning, extracting, formatting, and processing 10+ TB of complex data.
- Applied high-performance computing techniques to optimize efficiency of jobs submit to the NIH compute cluster.
- Harmonized publicly available and internal data sources.
- Employed advanced data visualization tools to communicate key results to collaborators and other non-experts.
- Trained colleagues on proper implementation of shared scripts.
I have done many things, but here are a select few.
Here is a personal project that I did one day because I was bored.
This is an animation showing the process of finding the line of best fit for a least squares linear regression. The code used to generate this graphic and more details can be found here.
This is another thing I did because I was bored. More details and the code used can be found here.
- Long, E., Patel, H., Golden, A., Antony, M., Yin, J., Funderburk, K., ... & Choi, J. (2024). High-throughput characterization of functional variants highlights heterogeneity and polygenicity underlying lung cancer susceptibility. The American Journal of Human Genetics, 111(7), 1405-1419.
- Funderburk, K., Bang-Christensen, S. R., Miller, B. F., Tan, H., Margolin, G., Petrykowska, H. M., ... & Elnitski, L. (2023). Evaluating Stacked Methylation Markers for Blood-Based Multicancer Detection. Cancers, 15(19), 4826.
- Long, E., Yin, J., Funderburk, K. M., Xu, M., Feng, J., Kane, A., ... & Choi, J. (2022). Massively parallel reporter assays and variant scoring identified functional variants and target genes for melanoma loci and highlighted cell-type specificity. The American Journal of Human Genetics, 109(12), 2210-2229.
- DeLory, T., Funderburk, K., Miller, K., Zuluaga-Smith, W., McPherson, S., Pirk, C. W., ... & Rueppell, O. (2020). Local variation in recombination rates of the honey bee (Apis mellifera) genome among samples from six disparate populations. Insectes sociaux, 67, 127-138.
- Kaur, A., Funderburk, K., Campaña, A. L., Puente, P., & Ríos-Soto, K. R. (2019). A household model of German cockroach infestations and their effects on symptoms of atopic asthma. Letters in Biomathematics, 6(2), 1-26.
- Funderburk, K. M., Auerbach, S. S., & Bushel, P. R. (2017). Crosstalk between receptor and non-receptor mediated chemical modes of action in rat livers converges through a dysregulated gene expression network at tumor suppressor Tp53. Frontiers in genetics, 8, 157.