Claire McKay Bowen
(See professional bio in next section.)
I am currently a senior fellow and the Statistical Methods Group Lead at the Urban Institute, researching methods of data privacy and confidentiality.
I grew up in the beautiful state of Idaho for most of my life and graduated from Salmon High School in Salmon, Idaho (yes, the town is named after the fish). I attended Idaho State University for my bachelors before obtaining my masters and PhD at the University of Notre Dame. In 2021, the Committee of Presidents of Statistical Societies identified me as an emerging leader in statistics for my technical contributions and leadership to statistics and the field of data privacy and confidentiality. I am also a member of the Census Scientific Advisory Committee, a committee member of the National Academies of Sciences, Engineering, and Medicine Approaches for Data Governance and Protecting Privacy, an advisory board member of the Future of Privacy Forums, and an adjunct professor at Stonehill College.
When I am not doing research, I enjoy participating in endurance races such as long distance triathlons. My current goal is to finish a gravel race and run all the world major marathons. When I am not trying to be a crazy person (as my spouse describes my endurance training), I love hiking, snowboarding, knitting, and playing with my pets.
Professional Photos: Click on image to view larger version.
Photos from 2023
Photos from 2014 – 2022
Professional Bio and CV
Claire McKay Bowen (she/her) is a senior fellow in the Center on Labor, Human Services, and Population and leads the Statistical Methods Group at the Urban Institute. Her research focuses on developing technical and policy solutions aimed at safely expanding access to confidential data for advancing evidence-based policy-making. She also has interest in improving science communication and integrating data equity into the data privacy process. In 2024, she became an American Statistical Association Fellow “for her significant contributions in the field of statistical data privacy, leadership activities in support of the profession, and commitment to mentoring the next generation of statisticians and data scientists.” Further, she is a member of the Census Scientific Advisory Committee and several other data governance and data privacy committees as well as an adjunct professor at Stonehill College.
Bowen holds a Honors BS in mathematics and physics from Idaho State University and an MS and PhD in statistics from the University of Notre Dame. After completing her PhD, she worked at Los Alamos National Laboratory, where she investigated cosmic ray effects on supercomputers.
My Bucket List
Run a marathonUpdate: Run all six world marathon majors (Berlin, Boston, Chicago, London,NYC, and Tokyo)Complete an Ironman before 30- Eat/drink iconic foods in each of the 50 States (in progress!)
- Travel to all 7 continents (Africa, Antarctica,
Asia, Australia,Europe,North America, South America) Write a book (doesn’t have to be published)Turns out I can write a book that someone wants to publish?!Become an American Statistical Association FellowWHAT?! Done in 2024!
Career Story
Significance Magazine Interview published in June 2021
“I realized that what I liked most [about science] was the analysis part…”
I started out studying physics because I wanted to know how the world worked. But then, within the first year of my studies, I realized that mathematics is the language of science, so I pursued a dual degree in mathematics and physics at Idaho State University. I got involved in a lot of different projects while at Idaho State: I was in a radiation physics lab for a bit, so I was analyzing samples from the environment and looking at radiation levels; I worked in a biophysics lab, playing with really cool lasers and looking at DNA–RNA interactions. I conducted some education research too, and I got into STEM [science, technology, engineering and mathematics] outreach and education.
It was after getting to try all these different kinds of things and talking about them with my spouse, who I was dating at the time, that I realized that what I liked most was the analysis part of research. To adapt that famous quote from John Tukey about statisticians: I like playing in other people’s backyards. I applied to both physics and statistics programs for graduate school and ended up on a statistics program at the University of Notre Dame in Indiana. There I completed a master’s in applied and computational mathematics and statistics before pursuing my PhD, which is when I became interested in data privacy.
My dissertation was on “Data Privacy via Integration of Differential Privacy and Data Synthesis”. Differential privacy had only been out for a few years at that point, so it was still very new theoretical work, and I was trying to do something more applied with it, mixing it with synthetic data. I had no idea differential privacy was going to become such a hot topic, but the fact that it did made it easier to find a job when I graduated.
I currently work at the Urban Institute, a bipartisan non-profit public policy research institution. We try to “elevate the debate” on public policy issues to help inform public policy-makers, such as the United States Congress, on making decisions that are very much evidence-based. We have various public policy centers within the Urban Institute, 12 in total, focusing on health policy, justice policy, tax policy, and other areas. I am part of the data science team within Urban’s Technology and Data Science Office, and our role is to both lead research and to assist in research across Urban, using data science techniques.
Because I am a specialist in data privacy and confidentiality, I am specifically looking at the question of how to release data that is meaningful and powerful for making policy decisions while still protecting the privacy of individuals. So, for example, in the United States we have just passed a $1.9 trillion stimulus package. That is quite a bit of money, and for it to be distributed effectively, it would be great to access taxpayer data to figure out, for example, who needs it most based on the impacts of Covid-19 over the past year. Now, taxpayer data contains a lot of sensitive information, so you would not want to know who specifically is in that data, but you want to know enough to make those kinds of policy decisions with something like the stimulus package. So, a project that I am working on right now is a collaboration with the Internal Revenue Service to figure out if we can create a synthetic data set – a data set with pseudo records that should be statistically representative of the original data, based on some sort of underlying model. Then, if somebody proposes a new tax policy and wants to know how it might affect the average American, you could use this data set to simulate the effects, to adjust the model based on, say, income tax going up or down for different groups of people.
I have found that communication skills are very important in the job that I do. Most people get that there is a tension or balance between data privacy and data utility, and that this is what I am interested in exploring. But when it comes to something like differential privacy, you might find people saying, “Okay, so you’re using this methodology that uses fake numbers … How does that work?”, and I have to figure out how to explain it to different lay audiences. So if I had one piece of advice for someone interested in a role like mine, it would be: do not neglect the “soft skills”.