I began my academic journey studying Archaeology and Anthropology at the University of Cambridge, where I developed an early interest in human biological variation. I continued this work during my Ph.D. in Biological Anthropology at Penn State University, focusing on developing quantitative, reproducible methods for studying hair morphology and pigmentation. During my postdoctoral training in Quantitative and Computational Biology at the University of Southern California, I started work on forensic genetic genealogy to investigate how human demography affects the identification of relatives via forensic and direct-to-consumer DNA databases. I continue to expand this work alongside my projects on phenotypic evolution. Throughout my career, I have also been committed to science communication, including collaborations with PBS and ongoing efforts to make research more accessible through social media and public outreach.
The most significant scientific contribution I would like to make is to establish a rigorous, reproducible, and biologically grounded framework for studying complex human phenotypes. By developing scalable, quantitative methods for traits such as hair morphology and skin pigmentation as model systems, I aim to replace outdated subjective classification approaches and enable more accurate investigations of human biological variation and the evolutionary processes that shape it.
I am excited about how data-driven methods allow us to move beyond subjective descriptions of human traits and toward biologically meaningful, reproducible measurements. Beyond my primary research, I am particularly invested in exploring how AI can accelerate and improve the research process itself. I have collaborated with the University of Michigan’s Center for Academic Innovation to develop a series of applied GenAI courses designed to help students and other learners become proficient with these new tools and technologies. I am also working with colleagues across the university to build AI tools that improve project organization, data management, and dissemination, all in support of more collaborative and open science.