About Me
Welcome to my personal webpage! I am currently a Genomic Data Science Fellow at AncestryDNA.
I am trained in computational biology. I completed a postdoctoral stint at the University of Chicago Department of Human Genetics. I completed my dissertation at University of Illinois at Chicago.
My research interests span integrating distinct ’nomic datasets (genomics and medical imaging), scientific computing, and high-dimensional statistics. I am also deeply interested in cancer genetics, statistical genetics, precision medicine and how we can use methods in population genetics to blend theory with burgeoning datasets.
Interests
- Multimodal Datasets
- Unsupervised Learning
- Medical Imaging
- Genetic Variation
Education
PhD Bioengineering
University of Illinois at Chicago
BS Electrical Engineering
University of Illinois at Urbana Champaign
📚 My Research
As a computational biologist with a robust background in statistical genetics, medical imaging, and population genetics, I enjoy combining distinct genetic/health datasets with an aim toward early detection of cancers and providing a more biologically meaningful representation of disease phenotypes (what mutations are actually the cause of a cancer).
Having completed my PhD, where I focused on the modern statistical and machine learning methods to create a link between medical imaging and corresponding somatic mutation datasets. I transistioned into population genetics for my postdoctoral to learn about the evolutionary forces that affect allele frequencies and effect size in traits in GWAS, with the goal to use that theory in evolutionary dynamics of tumors cancer (it remains quite the open question).
I’m now a Genomic Data Science Fellow at AncestryDNA creating models for recent admixture history (going back to the 1600s) of communities linked by a common ancestor.
Looking forward, my goal is to apply my skills in genomic data science to the ever-growing fields of clinical genetics and medical imaging. These areas are rich in distinct datasets and collating the datasets provides the oppurtunity to provide a fuller picture of the differencs in disease phenotypes, leading to stronger and more biologically meaningful genetic findings for populations at a whole and separately. Overall clinical genetics and medical imaging promise exciting advancements in health care and medicine, and I’m eager to contribute to these developments.
Having completed my PhD, where I focused on the modern statistical and machine learning methods to create a link between medical imaging and corresponding somatic mutation datasets. I transistioned into population genetics for my postdoctoral to learn about the evolutionary forces that affect allele frequencies and effect size in traits in GWAS, with the goal to use that theory in evolutionary dynamics of tumors cancer (it remains quite the open question).
I’m now a Genomic Data Science Fellow at AncestryDNA creating models for recent admixture history (going back to the 1600s) of communities linked by a common ancestor.
Looking forward, my goal is to apply my skills in genomic data science to the ever-growing fields of clinical genetics and medical imaging. These areas are rich in distinct datasets and collating the datasets provides the oppurtunity to provide a fuller picture of the differencs in disease phenotypes, leading to stronger and more biologically meaningful genetic findings for populations at a whole and separately. Overall clinical genetics and medical imaging promise exciting advancements in health care and medicine, and I’m eager to contribute to these developments.