Dr. Kate Metcalf has been appointed as an assistant professor at UC Berkeley’s School of Information. She began her role on July 1, 2026.
Previously, Metcalf was a postdoctoral alum of the Social Media Collective at Microsoft Research New England, and received her Ph.D. in Communication and Science Studies from the University of California San Diego. She also holds an M.A. in American Culture Studies from Bowling Green State University, and a B.A. in Literary Studies from Beloit College. Metcalf will be teaching courses on topics relating to data in the context of life sciences, and the ethical implications of technology.
Metcalf’s research focuses on the production of health and science knowledge through data, and she often pulls from disciplines such as science & technology studies, critical data studies, and the history and sociology of genetics. Her work has earned her a technology grant from the Berkeley Economy and Society Initiative (BESI) for the upcoming year to study the political economy of technology.
We spoke to Professor Metcalf about data practices, her interests outside of teaching, and more.
You previously worked at Microsoft Research New England as a postdoctoral researcher. What drew you to the I School and UC Berkeley?
I’m thrilled to be working in an interdisciplinary department, and particularly one where I get to work with graduate students. As a qualitative researcher, collaborating and being in community with quantitative researchers and practitioners really enriches my understanding of the sociotechnical systems that I study. I’m also an alum of the UC myself, and I’m grateful I get to stick around in the greatest public university system in the country.
What will you be teaching?
In the fall, I’ll be teaching a seminar course I’m calling Data and the Body (listed as INFO 290: Special Topics). That course will focus on the politics and social impacts of our efforts to quantify aspects of the human body, ranging from precision medicine and personal wearables to the state and military technologies used to datafy large populations.
In the spring, I’ll be teaching Social Issues of Information (INFO 203).
You have recently published research about data pipelines, analytics, and processing errors. Tell us more about your current research.
For the last several years, I’ve been working on a project about biobanks: research infrastructures that produce and manage the large volumes of health data that are used in contemporary genetic research. Biobanks are interesting to me because they set the terms of knowledge production in human genetics. They make decisions about who gets recruited as research participants and how data are collected about them, which in turn shape what kinds of research questions can be asked and which answers can be found. A lot of my work to this point has focused on the social and scientific consequences of that mediating role — some of those publications are already online, and more is included in my forthcoming book.
I’m also starting to develop my next major project, which focuses on a set of predictive tools that rely on a technique called “polygenic scoring.” Unlike other forms of genetic testing, polygenic scoring makes probabilistic assessments of traits that you might (or might not!) go on to develop. These technologies have the potential to provide real and actionable insights, but — like other predictive technologies — also have the potential to make inferential mismatches that compound existing inequities. I’m interested in understanding the politics of polygenic scoring as it’s used in the clinic, but also as it disperses into other domains like police forensics, direct-to-consumer health technologies, and assisted reproduction.
What interests you most about the intersection of data and knowledge in the health/life sciences?
Health data are both uniquely intimate and uniquely impactful. These aren’t just clinical records: they include all kinds of information about our bodies, behavior, and life-course that we use for making sense of human biology. At the same time, though, health data — like any data — aren’t simply records of the world as it exists. Our tools for making data shape what we can know. Sometimes this takes obviously malignant forms, as in cases of bias and underrepresentation in health datasets. At other times its effects can be harder to identify, but they still shape how we develop health technologies and who can access good care. I’m interested in studying how health data mediate knowledge-making so that we can create data ecosystems that better support human flourishing, including through governance mechanisms as well as the on-the-ground work of building and using data infrastructures.
What’s a fun fact people may not know about you?
I’ve been a huge fan of college basketball since I was a kid, and I usually take the first long weekend of March Madness as a personal holiday. My hometown team is the Illinois Fighting Illini, but I plan to get season tickets to root for the Golden Bears this year too. (They play in different conferences, so it’s not cheating!)
What are you reading or watching right now?
Because we drove from Boston to Berkeley (45 hours!), I’ve recently finished a few Steinbeck and Didion audiobooks—good Californian novelists for a California-bound roadtrip.
