osu statistics phd

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Program Guide

The Department of Statistics program guide below contains requirements of each of our academic programs including the following:

• Master of Applied Statistics Program
• Master of Science in Statistics Program
• PhD in Statistics Program
• Graduate Minor in Statistical Data Analysis Program
• Graduate Minor in Statistics Program

(Refer to the individual program website for details on the Interdisciplinary PhD Program in Biostatistics .)

Current Program Guide

[pdf] - Some links on this page are to Adobe .pdf files requiring the use of Adobe Reader. If you need them in a more accessible format, please contact  [email protected] .

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Annual Statistical Report

The College of Engineering Annual Statistical Report is available through a series of dynamic dashboards. This report is a compilation of statistical data regarding enrollment of undergraduate and graduate students, degrees granted, new student profiles, college programs and outcomes, faculty and staff profiles, and research expenditures within the College of Engineering and its affiliate programs.

Student Data: At a Glance | Enrollment | New First-Year Student Profile | Graduate Admissions, New Students and Appointments | Degrees and Minors | Honors & Scholars Involvement | Undergraduate Retention & Graduation | Global Education  | Financial Aid Profiles  | Career Services

Faculty & Staff Data: Faculty & Staff Profiles

Research: Research Expenditures

Student Data

At a glance.

Brief overview of student enrollment and degrees offered by each department and program.

Student enrollment status (currently enrolled, transfer, new freshman, etc.), area of study, gender, ethnicity and campus. This data is based on the University Registrar’s Fifteenth Day Enrollment Report for the respective autumn term.

New First-Year Student Profile

Academic profile (ACT and SAT scores, high school rank) of each entering freshman class. High school rank, and ACT and SAT test data were obtained from Ohio State Undergraduate Admissions.

Graduate Admissions, New Students and Appointments

Profile of applicants, admitted students and students enrolled in a graduate program. A breakdown of graduate student appointments for enrolled students is also provided. All undergraduate GPAs, and test data for the GRE and TOEFL, were obtained from the Ohio State Undergraduate Admissions. Additional data was also collected to determine acceptance rates for graduate programs.

Degrees and Minors

Degrees and minors dashboards.

Honors & Scholars Involvement

Student involvement in the university’s Honors and Scholars programs.

Undergraduate Retention & Graduation

College retention and graduation rates and counts for each entering freshman class.

Global Education        

Profile of students participating in international programs and locations visited.

Education Abroad dashboards

Financial Aid Profiles

Career Services

Engineering Career Services strives to collect data on all graduates through web-based surveys, employer reports, input from faculty/staff and personal inquiries with students.

Faculty & Staff Data 

Demographic data for tenure-track faculty, research faculty and research personnel, reported per guidelines established by the American Society for Engineering Education (ASEE) and the U.S. News & World Report annual surveys. This data is gathered using faculty/staff salary reports, tenure reports, and staff and faculty analytics reports. Additionally, the list of National Academy of Engineering (NAE) members is updated annually using the NAE website, and feedback from associate deans and department chairs.

Faculty and Staff Tiles

Research Expenditures

The college’s fiscal year research expenditures, reported using the guidelines established by the American Society for Engineering Education (ASEE) and the U.S. News & World Report annual surveys.

For More Information

The annual report is a compilation of statistical data for the College of Engineering, including the Knowlton School. Comments or questions regarding the report can be directed to:

David Tomasko ( [email protected] )

Austin Mack ( [email protected] )

Barbara Holt ( [email protected] )

Historical reports prior to 2014 may be requested by contacting Austin Mack ( [email protected] ).

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Data and Assessment

Utilizing data and creating areas for assessment based on that data are important parts of successful graduate program coordination and success.

Here at the Graduate School we are here to support programs in their data collection and assessment initiatives. For information on specific data availability through the Graduate School, see the outcomes dashboards below or reach out to the us so we can see if there is support we can provide. For more information on program assessment visit the below area on program assessment to find more information to support your program.

Graduate Assessment

Graduate programs throughout The Ohio State University are engaged in collecting and analyzing graduate program assessment data to support the highest-quality programs for our students. Not only is this work important to OSU’s accreditation process with the Higher Learning Commission, understanding how and how well our graduate program faculty and staff engage with assessment data is a first step in helping our programs achieve academic and scholarly excellence. This work also aims to improve the way that our graduate programs operate overall.

gradSERU Graduate Student Experience Survey

In spring 2023, the Graduate School launched the Graduate Student Experience Survey, the first comprehensive survey of its kind in over five years. Visit the link below to find the video of the high-level overview presentation delivered by the Dean of the Graduate School, Mary Stromberger in November 2023, its accompanying PowerPoint and access to the survey results. The Graduate School is excited to partner with colleges on initiatives to reinforce and improve graduate student experiences where needed. We believe these insights will be a powerful tool to support your graduate students now and into the future. 

Coalition for Next Generation Life Science

The Ohio State University is committed to providing comprehensive and transparent information on graduate and post-doctoral study to help students, faculty, staff, and external audiences understand the performance of each program within the university.

The goal of providing this performance information is to help graduate students and post-doctoral trainees understand current metrics on what various degrees can do to improve their career growth but also provide additional information on alternative career paths outside of academia.

In order to help provide this information in a clear and attainable manner, Ohio State has joined the  Coalition for Next Generation Life Science  which is a central host of data from various schools around the United States. 

Graduate School Student and Program Data Visualizations Dashboards

The Ohio State University Graduate School, through collaborations with many partners, has brought together a set of data visualization dashboards that provide insight into current trends of graduates students pursuing degrees at The Ohio State University and outcome trends for PhD and Master's Alumni as well as Post Doctoral Students.

Student and Program Reports

The Student and Program Reports contain information on admissions, enrollments, retention, and graduation for Ohio State graduate students, with data going back to 2009. The data can be filtered by year and program within each tab.

PhD and Master's Alumni Outcomes Reports

The PHD and Master's Alumni Outcomes Reports contain information on Alumni from Ohio State PhD and Master's programs. PhD data is available from 2005 and Master's data is available from 2017 forward. The data detail employment outcomes, listing industry, salary estimates, and location, both national and international. The data can be filtered by year and program within each tab. This data was collected and produced by Academic Analytics.

Post-Doc Alumni Outcomes Reports

The Post-Doc Alumni Outcomes Reports contain information on Alumni from Ohio State Post-Docs programs and administrative departments (I.e., Office of Academic Affairs), with data going back to 2004. The data detail employment outcomes, listing industry, salary estimates, and location, both national and international. The data can be filtered by year and program within each tab. This data was collected and produced by Academic Analytics.

Dashboard Highlights and User Instructions

This user guide is intended for researchers of all levels who wish to explore the  Student and Program Reports  dashboards made available through The Ohio State University Graduate School. 

Student Program Reports Dashboard User Guide

Academic Analytics Insight Overview

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Quantitative Research, Evaluation, and Measurement (EDUCST-PH, QREM)

Specialization Leading to Doctor of Philosophy in Educational Studies

Program Requirements (6 Hours)

Common core.

Core 1 Course (3 hours)

• EDUCST 6891 Proseminar in Educational Studies (3)

Core 2 Course: Choose from one of the following courses (3 hours)

• EDUCST 5765 Grant Writing (3)
• ESEPSY 7404 College Teaching (3)
• ESEPSY 8404 Academic Writing in Education: Reviewing the Literature (3)
• ESEPOL 7100 Writing for Educational Policy Audiences (3)
• ESPHE 6403 Ethics and the Professional Context of Teaching (3)
• EDUCST 6892 Educational Policy and Inequality in Social and Cultural Context: Integrating Research Traditions (3)

Specialization Requirements (36 Hours)

Required courses (24 hours).

• ESQREM 6621 Introduction to Educational Evaluation (3)
• ESQREM 6641 Introduction to Educational Statistics (4)
• ESQREM 6661 Introduction to Educational Measurement (3)
• ESQREM 7635 Advanced Research Methods (3)
• ESQREM 7648 GLM I: Introduction to Regression and ANOVA (4)
• ESQREM 7658 GLM II: Advanced Regression and ANOVA (4)
• ESEPHL 7414 Examining Knowledge, Truth, and Objectivity (3)

Elective Requirements (choose four, minimum 12 hours)

Two courses from the following list (5-9 hours):

• ESQREM 8648 Multivariate Analysis (3)
• ESQREM 8657 Factor and Cluster Analyses (3)
• ESQREM 7643 Categorical Data Analysis (3)
• ESQREM 8658 Applied Multilevel Data Analysis (3)
• ESQREM 8659 Structural Equation Modeling (3)
• ESQREM 8895 Seminars: QREM (e.g., analysis of longitudinal data; meta-analysis) (2-5)

One course from the following list:

• ESQREM 7627 Sampling Design and Survey Research Methods (3)
• ESQREM 7631 Applied Evaluation Designs (3)
• ESQREM 8895 Seminars: QREM (e.g., advanced evaluation designs) (2-5)
• ESQREM 7661 Instrument Construction (3)
• ESQREM 7663 Test Reliability and Validity (3)
• ESQREM 7667 Authentic Assessment (3)
• ESQREM 8674 Scaling and Item Response Theory (3)
• ESQREM 8895 Seminars: QREM (e.g., advanced IRT) (2-5)

Research Apprenticeship (9 hours)

Research apprenticeship hours represent work you do under the guidance of your faculty advisor to enhance your research skills, particularly those that lead to publication and/or presentations at conferences.  Consult with faculty advisor to discuss content and scheduling of hours.

Dissertation Requirement (3 hours minimum)

Consult with faculty advisor prior to enrollment

• ESQREM 8999 Dissertation or Thesis Research: QREM

Progression

Post-MA/MS: Minimum of 54 hours (Total: 84 hours) Expected to sit for candidacy: After completing all required coursework Maximum Hours post Candidacy: 3 credits per semester

Note: Students exact curriculum may vary depending upon program of study determined by student and advisor. 

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Interdisciplinary PhD in Biostatistics

• Program Information
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Directory and research interests

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Faculty List

Rebecca Andridge, PhD               University of Michigan, 2009               Position:  Associate Professor, CPH Division of Biostatistics              Research interests:  Developing and evaluating statistical methods for handling missing data and for group-randomized trials

Dena Asta, PhD               Carnegie Mellon University, 2015               Position:  Assistant Professor, ASC Department of Statistics              Research interests: Geometric approaches to network inference and and non-parametric non-Euclidean methods

Guy Brock, PhD               The University of New Mexico, 2003               Position:  Research Associate Professor, College of Medicine, Biomedical Informatics              Research Interests: statistical genetics, bioinformatics, and genomics, integrated analysis of miRNA-mRNA data, competing risks and multi-state models, and missing value estimation in microarray data.

Lo-Bin Chang PhD                 Brown University, 2010               Position:  Assistant Professor, ASC Department of Statistics              Research interests:  Computer vision, mathematical and statistical finance, pattern theory, machine learning

Dongjun Chung, PhD               University of Wisconsin - Madison, 2012               Position:   Associate Professor, College of Medicine, Department of Biomedical Informatics               Research interests:  Development of statistical and computational methods for integrative analysis of genetic and genomic data with biomedical big data.

Elly Kaizar, PhD               Carnegie Mellon University, 2006               Position:  Professor, ASC Department of Statistics              Research interests:  Meta-analysis and research synthesis, Bayesian hierarchical modeling and analysis of sample surveys

Eben Kenah, PhD               Harvard School of Public Health, 2008               Position:  Associate Professor, CPH Division of Biostatistics              Research interests:  Survival analysis, Epidemiologic methods, Stochastic processes, Phylogenetics and casual inference

Laura Kubatko, PhD               The Ohio State University, 1999               Position:  Professor, ASC Department of Statistics              Research interests: Statistical genetics, including phylogenetics, population genetics, and genetic linkage studies

Sebastian Kurtek, PhD               Florida State University, 2012               Position:  Associate Professor, ASC Department of Statistics              Research interests: Statistical image analysis, statistical shape analysis, medical imaging, functional data analysis, computational statistics

Lang Li, Ph.D               University of Michigan, 2001               Position:  Professor and Chair of the Department of Biomedical Informatics              Research interests:   drug interaction and precision medicine; his lab uses biomedical informatics, systems pharmacology and statistical approaches to investigate drug interactions and genomics effects on drug efficacies and adverse drug events

Shili Lin, PhD               University of Washington, 1993               Position:  Professor, ASC Department of Statistics              Research interests:  Statistical genetics, genetic epidemiology, and bioinformatics

Bo Lu, PhD               University of Pennsylvania, 2002               Position:  Professor, CPH Division of Biostatistics              Research interests:  Causal inference, propensity score matching, missing data and survey sampling

Andy (Ai) Ni, PhD               University of North Carolina-Chapel Hill, 2015               Position: Assistant Professor, CPH Division of Biostatistics              Research Interests: High-dimensional variable selection with survival outcomes, Individualized treatment regimen estimation with survival outcomes; Outcome-dependent sampling designs; High-dimensional variable selection with measurement error

James Odei, Ph.D               Utah State University, 2014               Position:  Clinical Assistant Professor, CPH Division of Biostatistics              Research Interests:  Spatial, temporal, and spatio-temporal Statistics; environmental & ecological Statistics; Bayesian hierarchical modeling; statistical data   visualization; collaborative research in behavioral medicine, psychology, cancer, diabetes, and infectious diseases (HIV/AIDS, TB, etc.)

Subhadeep Paul, PhD               University of Illinois, Urbana-Champaign, 2017                Position: Assistant Professor, ASC Department of Statistics                 Research Interests: My research focuses on statistical inference in complex networks, including their multi-layer and dynamic variants. I aim to develop computationally feasible model based as well as spectral and matrix factorization based methods to analyze networks. I am also interested in the analysis of neuroimaging data obtained from both functional MRI and in-vivo Calcium imaging and much of my current research is motivated by such applications.

Michael Pennell, Ph.D               University of North Carolina-Chapel Hill, 2006               Position:  Associate Professor, CPH Division of Biostatistics              Research interests:  Design and analysis of group randomized trials, first hitting time models for survival analysis, Bayesian nonparametrics, toxicological risk assessment

Matthew Pratola, PhD               Simon Fraser University, Vancouver, Canada, 2010               Position:  Associate Professor, ASC Department of Statistics              Research interests:  Computer model calibration, uncertainty quantification, and scalable Bayesian non-parametric regression models applied to environment, climate and environmental health problems.

Grzegorz A. Rempala, D.Sc. Habilitatus                 Post-doctoral degree, Warsaw Technical University, Warsaw,  Poland, 2007               Position:  Professor, CPH Division of Biostatistics              Research interests:  Genomics, bioinformatics, mathematical statistics, mathematical epidemiology, classical probability theory, stochastic processes

Patrick Schnell, PhD               University of Minnesota, 2017               Position:  Assistant Professor, CPH Division of Biostatistics              Research interests:  Bayesian inference, clinical trials, treatment effect heterogeneity, simultaneous inference, and complex diseases.

Abigail Shoben, PhD               University of Washington, 2010               Position:  Associate Professor, CPH Division of Biostatistics              Research interests:  Group sequential methods, longitudinal data, epidemiologic applications, clinical trials

Jennifer Sinnott, PhD               Harvard University, 2012               Position:  Associate Professor, ASC Department of Statistics

Fernanda Schumacher, PhD               University of Campinas (Brazil), 2021               Position: Assistant Professor, CPH Division of Biostatistics              Research Interests: Robust models, models for censored data, longitudinal data, EM algorithm, and scale mixture of skew-normal distributions

Asuman Turkmen, PhD               Auburn University, 2008               Position:  Associate Professor, ASC Department of Statistics              Research interests:  Robust statistics, outlier detection, dimension reduction and its applications in genome-wide association studies

Emeritus Faculty

Jason Hsu, PhD               Purdue University, 1977               Position:  Professor Emeritus, ASC Department of Statistics              Research interests:  Multiple comparisons and statistical computing

Stanley Lemeshow, Ph.D               University of California, Los Angeles, 1976               Position:  Professor Emeritus, CPH Division of Biostatistics              Research interests:   Genetics of PTEN in Cowden and related syndromes and familial cancer, genetic alterations in the epithelial and stromal compartment of prostate adenocarcinomas, innate immunity: elucidation and modulation cancer therapy, genetics of PTEN and molecular-based patient care, reducing cervical cancer in Appalachia, breast cancer prevention through nutrition, stress, aging and wound healing

H.N. Nagaraja, PhD               Iowa State University, 1980               Position: Professor Emeritus, CPH Division of Biostatistics              Research interests:  Order and record statistics, measuring agreement, stochastic modeling of biomedical data, biostatistical applications

Thomas Santner, PhD               Purdue University, 1973               Position:  Professor Emeritus, ASC Department of Statistics              Research interests:  Computer experiments (with special emphasis on statistical applications in orthopedics), statistical applications in biomechanics and biomaterials

Elizabeth Stasny, PhD               Carnegie Mellon University, 1983                Position:  Professor Emeritus, ASC Department of Statistics

Joe Verducci, PhD               Stanford University, 1982               Position:  Professor Emeritus, ASC Department of Statistics

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Statistics PhD Student Explores Putting Data Science to Work for the Social Good

Andrew Landgraf is in an enviable position. He can do math AND analyze big data—very well. This should shorten the length of his job search considerably when he receives his PhD in statistics in the upcoming academic year. His only dilemma might be in choosing from the many areas of human endeavor desperate for his skills.

Landgraf, who has an undergraduate degree from Ohio State in actuarial science and worked in that field for a while, said, “I’ve always liked to do math, from the time I was quite young. And I especially like data science, which allows me to blend my statistical ability with computer-science skills. So, this led me back to Ohio State’s statistics graduate program. I like the idea of being able to apply statistics to have tangible effects on real-world problems.”

While Landgraf has not settled precisely on the arena where he might want to put those skills to work, he knew it would be a good idea to expand his options.

He heard about the Eric & Wendy Schmidt Data Science for Social Good Fellowship Summer Program at the University of Chicago and thought it seemed like "a unique opportunity to explore something I would like to do—one that I might not be able to have after I graduate.”

He filled out the paperwork that included an essay on why he was interested in improving the social good and was one of 48 Fellows chosen from around the country to participate.

This three-month program, initiated in 2013, is basically an internship designed for aspiring data scientists to work on data mining, machine learning, big data and data science projects that have social impact.

Landgraf heads to Chicago on June 2 to begin work.

“We will be working in small teams, taking on real-world problems, analyzing dozens of datasets; in such areas as education, health, energy and transportation,” Landgraf said, “and we will be working closely with professionals from government and nonprofit organizations.”

Actually, each team is led by full-time mentors who serve as project leaders and technical advisors. They help the Fellows brainstorm ideas and manage the relationship between the teams of Fellows and the project partners.

“They provided a list of potential projects and partners in a wide range of categories that include nonprofits and philanthropy, energy, environment, transparency, health, international development, public transportation, emergency response, education, food waste, urban economic development, social services and workforce development,” Landgraf said.

“And while I do not know yet which project I might be assigned to, I do know that I would be happy to work on any of them.“

Whatever that project might be, Landgraf will be working in a fast-paced environment, applying his exceptional coding and analytics skills, collaborating with the other Fellows to provide possible solutions and learning from decision-makers on the front lines of public policy.

The program is led by Rayid Ghani from the Computation Institute & Harris School of Public Policy at the University of Chicago, and former chief data scientist of the 2012 Obama campaign.

It is organized by an interdisciplinary team from the Computation Institute , a joint initiative between the University of Chicago and Argonne National Laboratory.

Landgraf already has some major street cred for data analytics skills. He was one of a team of five Ohio State statistics graduate students who won the 2013 national Capital One Data Modeling Competition late last year. https://artsandsciences.osu.edu/news/statistics-graduate-students-win-national-data-analyticsmodeling-competition

--Sandi Rutkowski, Arts and Sciences Communications

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• Master of Applied Statistics

Statistics Master of Applied Statistics

The Department of Statistics offers several graduate degree programs, including the MS and PhD in Statistics and the Master of Applied Statistics (MAS) degree. It jointly administers a unique Interdisciplinary PhD Program in Biostatistics  with the Division of Biostatistics in the College of Public Health.

The department aims to contribute to virtually all areas of statistical science, including the development of novel statistical theory and methodology. Specific areas of excellence include Bayesian statistics, spatio-temporal statistics, statistical learning and biostatistics.

Research is directed toward modern and emerging areas of interest. A large portion of the department’s faculty and students are involved in interdisciplinary research and make significant scientific contributions beyond the field of statistics. Faculty members are highly successful at securing competitive grants from various research funding agencies, including the National Science Foundation (NSF) and National Institutes of Health (NIH). In addition, the department is a partner in administering the NSF-funded  Mathematical Biosciences Institute  on the Ohio State campus.

Master of Applied Statistics (MAS)

The goal of the Master of Applied Statistics (MAS) is to prepare graduate students to enter positions in applied statistics in business, industry, and government. The program is typically a two year degree, which culminates with a graduation examination.

For more information, see stat.osu.edu .

Optional Practical Training (OPT)

International graduates of this major are approved by the Department of Homeland Security for three (3) years of work permission in the United States after graduation. Visit the Office of International Affairs website for more information.

If you have a disability and experience difficulty accessing this content, please contact [email protected] .

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• Biostatistics

Beyond crunching numbers

Understanding disease risks and uncovering health trends requires a lot of data, and biostatisticians make sense of it. Biostatisticians combine insights from biology, mathematics and statistical analysis to look at health data in new ways, sifting meaningful information from useless “noise” and discovering patterns that shape the health of individuals and communities. From analyzing genetics to designing clinical trials to determining racism’s impacts on health, biostatisticians address critical emerging health problems. Alumni go on to work in universities, private research, hospitals, pharmaceutical companies, health departments, the Centers for Disease Control and Prevention and more.

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Campus: Columbus

College: Arts and Sciences

Statistics is the science of decision making under uncertainty. By understanding statistics, you can make sound decisions based on data and models of the world.

The study of statistics encompasses study design and the collection, assessment and curation of data; the building of formal statistical models to capture an abstraction of reality; and the rules of probability upon which those models rest.

In Ohio State's statistics program, students will explore each of these areas in their course work. They will also develop statistical, computational and numerical skills that are applicable in fields including agriculture, business, education, science and engineering, finance, medicine, and the social sciences.

Get started

Upon admission to the university, students can declare a major in statistics within the College of Arts and Sciences. Students should then contact a statistics advisor to map out an appropriate program of courses. 

Students interested in the statistics major should take Calculus I and II and Introduction to Computer Programming as prerequisites for the major. Students are also encouraged (but not required) to take Introduction to Statistical Reasoning or a general education data analysis course.

The requirements for the major are broken down into two components: core fundamentals and a selection of electives. All students take courses covering the fundamentals of statistics, including:

• Calculus III
• Linear Algebra
• Introduction to Probability for Data Analytics
• Introduction to Statistical Interference for Data Analytics
• Statistical Modeling for Discovery I
• Statistical Modeling for Discovery II
• Principles of Data Collection and Analysis
• Advanced Statistical Inference
• Computational Statistics

Student can then tailor the major to their unique interests by selecting at least 15 hours of elective course work. 

Possible careers

Graduates of the statistics major will be well-suited for jobs requiring statistical analyses of data and the ability to provide theoretically grounded insights. Statisticians can work in a variety of fields, including pharmacy, finance, consumer science, medicine, environmental science, agriculture, business, education,  engineering and the social sciences. Locally, Columbus companies such as Nationwide Insurance, JPMorgan Chase, Root Insurance, Upstart and Battelle Memorial Institute regularly employ Ohio State's statistician alumni.

Graduates will also be prepared for further study at the graduate level, which can lead to career opportunities in academia, government and other industries. 

Salary estimates

According to the U.S. Bureau of Labor Statistics, in 2019 the national median salary for statisticians was $91,160. This includes workers specializing in fields such as biostatistics, agricultural statistics, business statistics and economic statistics.

Program objectives

Upon completion of the statistics program, students will:

• be proficient at exploratory data analysis using graphical and computational tools;
• understand mathematical concepts relevant to statistical theory, and will be adept at applying these concepts to statistical models for data, such as those arising in business and the physical, biological and social sciences; 
• be able to assess whether hypotheses are supported by data and to quantify uncertainty in parameter estimates and models; 
• be able to develop, fit, assess and compare statistical models in a formal probabilistic framework;
• be able to communicate the results of a statistical analysis (in both oral and written formats) in a manner appropriate to the audience.

Future students 

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June 2024: Dr. Kathryn D. Coduto (COM)

• By: Shannon Landis
• June 1, 2024

What made you decide to be a social scientist/ why does social science matter to you?

I love being able to answer questions about the world; I originally wanted to be a journalist for that exact reason. As I went through my undergraduate program, I discovered social scientific research and realized just how much I could do within this field. I also feel empowered by social science; there was a time when the idea of statistics was so intimidating to me. But now, I am confident in my ability to navigate complex questions  and   their solutions through a variety of tools and techniques, all stemming from social scientific training and exploration.

Can you tell us about a recent research project that you’re excited about?

Dr. Allison McDonald, from the Faculty of Computing and Data Sciences, and I have been working on a project investigating sexting behaviors in romantic relationships. We specifically have asked people about how they negotiate, if they do, the sharing of this highly personal information with partners. I’m especially excited about this project, though, because we are also investigating what people do with sexual material once they’ve broken up. There isn’t much research in this area yet, and I think we’re getting really rich insights into how people are thinking about handling sensitive content throughout the lifetime of a relationship. I recently presented this work at the CHI 2024 conference and got some great feedback on potential design implications for improving technologies in these instances. We have another part of the study we’re hoping to launch soon, too.

What is the best piece of professional advice you ever received?

I had great advisors for both my master’s degree (Dr. Danielle Coombs at Kent State University) and my doctoral degree (Dr. Jesse Fox at Ohio State University). They both always encouraged me to follow my interests, and I always felt supported in exploring the ideas I was most passionate about. I think following your interests is critical to staying interested in and excited by the work you do; I love thinking about research and so much of that is because I study the things I am most interested in. I now pass that advice along to my own advisees, too—follow your interests!

What is your favorite course you’ve taught at BU?

I love teaching Communication Research (CM 722). Comm Research is many students’ first experience with social scientific research, especially in a communication context, and I love helping them realize how much they can do with research. I am always so excited when I see different concepts click into place for students, whether that be perfecting a research design, conducting a statistical analysis, or crafting implications from their research. It is always so rewarding.  

Tell us a surprising fact about yourself.

Before coming to BU, I was an assistant professor at South Dakota State University in Brookings, SD. My favorite place in the world is still Badlands National Park in western South Dakota—I haven’t been anywhere that can top it!

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