Sabine Segaloff Brings an Archaeologist's Eye to Data Science

Kirsten Samuels

September 18, 2026

Topics

Admissions

Machine Learning

PhD

Research

Student Spotlight

Sabine Segaloff

Hometown
Norfolk, Virginia

Education
Ph.D. in Data Science, University of Virginia
M.S. in Data Science 2026, University of Virginia
B.S. in Anthropology 2024, Virginia Commonwealth University

Before she ever enrolled in a data science program, Sabine Segaloff was asking questions about how knowledge is created, preserved, and interpreted. As an archaeology research assistant, she taught herself deep learning tools to map settlement patterns in Zambia, only to discover that the most challenging part of the process was not the technology itself, but the human decisions required to create the data.

Now a member of UVA's Ph.D. in Data Science cohort, Segaloff brings an uncommon blend of archaeology, mathematics, and data science to her work. Her research interests explore how datasets are formed, what information survives as data moves through technical systems, and what gets lost in the translation. She hopes to build a career as a professor working at the intersection of data engineering, machine learning, and anthropology while helping students think critically about the relationship between people and data.

Q: When and how did you become interested in data science?

As an archaeology research assistant, I taught myself about deep learning in ArcGIS Pro to map settlement patterns in Zambia — eventually becoming my department's go-to resource on these tools. But I hit a wall: The training data did not exist. For weeks, my "data science" was manually drawing boxes around farm plots.

Unsettled by the subjectivity of my labeling, I soon realized that all data captures this kind of human bias. This taught me that the most critical part of the data pipeline is not the algorithm; it is the human decisions that construct "ground truth." The experience drove me to study data and the way we interact with it formally.

Q: Choosing a doctoral program is a big decision. Why did you choose UVA's Ph.D. in Data Science?

I am an archaeologist and a mathematician and a data scientist. I approach the social and the technical as a unified landscape. UVA's structure is built around the premise that data science is already an interdisciplinary practice.

I loved being part of the School during my residential master's, and I'm looking forward to staying in that community as a contributor, collaborator, and colleague.

I want to stretch my ideas, put them in a blender with people who think differently than I do, and come out with better questions than I started with. The faculty I'll be working with span socio-technical theory, epistemology, and technical rigor. Whatever I build will get pushed on from every angle, and I think that's exactly what it needs.

Q: What areas of research interest you and why?

I am interested in dataset formation. I've come to think about that process in terms of loss and solidification: something is always discarded as material gets pushed through a schema, and whatever survives gets hardened and carried downstream.

I see that pairing show up wherever context gets flattened into structure: in ETL pipelines, in tokenization, in any process that converts something thick and nuanced into something clean and countable. I don't yet know which of those sites will anchor my dissertation; part of what I'm doing now is figuring out where the pairing is most tractable to actually measure. What draws me to all of them is the same question: What gets lost in the translation?

Q: What do you hope to do with your data science degree?

I intend to join a small liberal arts college as a professor teaching at the intersection of data engineering, machine learning, and anthropology.

Q: What advice would you give to prospective students considering a Ph.D. in Data Science? What do you wish you had known before starting?

I'll pass on this advice I actually did receive before starting: Answering "Why are you doing a Ph.D.?" isn't about justifying the choice to anyone else, or to an academic body. It's about knowing your own motivation, honestly, so it can fuel you. Know your engine before you need it. Mine isn't frustration at bad practice — it's wanting to build something solid.

Q: What are your initial impressions of the school, faculty, and other students? What are you most excited about, and what challenges do you anticipate?

I have enjoyed the school and its community throughout my master's program. I look forward to discovering and researching alongside its faculty and students as a peer. I am excited to develop better questions.