CV

PhD

University of California, Los Angeles

Statistics

• Dissertation: Population Size Estimation using Multiple Respondent-Driven Samples

BA

Amherst College

Mathematics & Philosophy

• Graduated with Honors in Mathematics
• Thesis: Using Mode Identification Clustering Methods with Pitch F/X Data

Co-Director, Social Data Science Major

College of Behavioral and Social Sciences (BSOS), University of Maryland

• Led development of a new interdisciplinary major in Social Data Science and acted as the founding BSOS co-director, including managing the budget, setting up infrastructure, and working with partner units on campus.
• Launched and oversaw innovative pedagogical projects, such as a “sidecar” course program in which students take a supplementary 1-credit course alongside existing statistics and data science courses based on performance on a pre-test.
• Developed computational infrastructure with a cloud computing environment and continually manage and oversee the computing environment to support students within BSOS.
• Led a team of students and staff members to build a data repository hosting classroom-ready datasets and providing resources to support instructors; and launched the undergraduate Data Curation Fellowship in which students identify, obtain, clean, and curate datasets.
• Served as chief data scientist for the ongoing campus-wide internal BEACON (Building Early Alerts for Campus Outreach Needs) project, which used student data from the Registrar, Dean of Students Office, campus surveys, and learning management systems with machine learning techniques to predict student outcomes, identify opportunities for intervention, and provide actionable insight for advisors.

Associate Research Professor

Social Data Science; Joint Program in Survey Methodology

• Taught classes on introductory statistics, data science, machine learning, AI, natural language processing, and other topics for both undergraduate and graduate students, incorporating innovative pedagogical techniques and utilizing traditional and flipped classroom formats in online, in-person, and HyFlex modes.
• Built and taught new courses for the Social Data Science major and the Survey and Data Science graduate program, such as SDSB233: Data Science for Social Science, SDSB326: Python Programming for Social Science, SURV673: Introduction to Python and SQL, and SDSB123: Social Data Science: Pathways and Applications.
• Advised and mentored students; engaged graduate and undergraduate students in collaborative research and publications.
• Wrote grant proposals and executed research plans.

Director of Graduate Studies

Joint Program in Survey Methodology (JPSM), University of Maryland

• Oversaw updates to the Master’s in Survey and Data Science program, such as incorporating neural networks and LLMs into course material.
• Assisted graduate students in finding funding through grants or assistantship opportunities on campus.
• Chaired the Master’s Admissions committee and managed recruitment efforts.

Consultant/Facilitator

The Coleridge Initiative

• Provided guidance as a facilitator with the Applied Data Analytics program for teaching data science and programming topics to working professionals in public policy.
• Led workshops on record linkage, text analysis, network analysis, and machine learning.
• Consulted on NSF NCSES projects using Survey of Earned Doctorates, Survey of Doctorate Recipients, Higher Education Research and Development, and IPEDS data.
• Developed modularized lessons and notebooks for introductory statistics and programming in Python, R and SQL.

Statistical Consultant

UCLA Institute for Digital Research and Education

• Assisted graduate students and professors in cleaning, merging, and preparing data for analysis using R, Python, Stata, SPSS, and other statistical software.
• Helped clients with their dissertations, publications, and other projects using methods such as mixed modeling, principal components analysis, simulation, and more.

Instructor (Undergraduate Courses)

University of Maryland

• SDSB180: Introduction to Data Management (Fall 2024, Spring 2025, Fall 2025, Spring 2026, Fall 2026)
• SDSB123: Social Data Science: Pathways and Applications (Fall 2024, Spring 2025, Fall 2025, Spring 2026, Fall 2026)
• SDSB326: Python Programming for Social Science (Spring 2024)
• SDSB233: Data Science for Social Sciences (Fall 2019, Spring 2020, Spring 2022, Fall 2022, Spring 2023)
• INST 314: Statistics for Information Science (Fall 2018)
• INST 354: Decision Making for Information Science (Spring 2019)

Instructor (Graduate Courses)

University of Maryland

• SURV 613: Machine Learning and AI for Social Sciences (Fall 2020, Fall 2021, Spring 2023, Spring 2024, Spring 2025, Spring 2026)
• SURV 622: Fundamentals of Data Collection (Spring 2020, Spring 2021, Spring 2022)
• SURV 673: Introduction to Python & SQL (Fall 2018, Spring 2019, Summer 2019, Fall 2019, Fall 2020, Summer 2020, Summer 2021)
• SURV 699M: Review of Statistical Concepts (Summer 2018, Summer 2019, Summer 2020, Summer 2021)
• SURV 701: Analysis of Complex Sample Survey Data (Spring 2021)
• SURV 727: Fundamentals of Computing and Data Display (Fall 2022, Fall 2023, Fall 2024, Fall 2025)
• SURV 736: Introduction to Web Scraping with R (Summer 2025)

Instructor

University of California, Los Angeles

• Stat 98T: Six Degrees of Separation: Studying the World Through Social Networks (Winter 2017)

Teaching Assistant

University of California, Los Angeles

• Stat 10: Introduction to Statistical Reasoning (Fall 2013, Winter 2014, Spring 2014, Fall 2014, Winter 2015, Spring 2015, Fall 2015, Winter 2016, Spring 2016)

Using a Stopping Rule to Optimize Cost-Quality Tradeoffs in a Large, Mixed-Mode Survey: A Simulation Study

J. Wagner, B. West, B. Kim, D. Suolang, C. Engstrom, J. Sinibaldi

Journal of Official Statistics. 41 (1), 329-364

Hidden Population Size Estimation and Diagnostics Using Two Respondent-Driven Samples with Applications in Armenia

B. Kim, L. G. Johnston, T. Grigoryan, A. Papoyan, S. Grigoryan, K. R. McLaughlin

Biometrical Journal, 65, 2200136

Using Geographical Data and Rolling Statistics for Diagnostics of Respondent-Driven Sampling

B. Kim, M. Ogwal, E. Sande, H. Kiyingi, D. Serwadda, W. Hladik

Social Networks. 69, 74-83

Population Size Estimation Using Multiple Respondent-Driven Sampling Surveys

B. Kim, M. Handcock

Journal of Survey Statistics and Methodology. 9(1), 94-120

Easy-to-Use Cloud Computing for Teaching Data Science

B. Kim, G. Henke

Journal of Statistics Education. 29:sup1, S103-S111

Partnering with a global platform to inform research and public policy making

F. Kreuter, N. Barkay, A. Bilinski, A. Bradford, S. Chiu, R. Eliat, J. Fan, T. Galili, D. Haimovich, B. Kim, S. LaRocca, Y. Li, K. Morris, S. Presser, T. Sarig, J. A. Salomon, K. Stewart, E. A. Stuart, R. Tibshirani

Survey Research Methods, 14(2), 159-163

sspse: Estimating Hidden Population Size using Respondent Driven Sampling Data

M. Handcock, K. Gile, B. Kim, K. McLaughlin

R package version 1.0.3, Los Angeles, CA.

The University of Maryland social data science center global COVID-19 trends and impact survey, in partnership with Facebook

J. Fan, Y. Li, K. Stewart, A. R. Kommareddy, A. Garcia, J. O’Brien, A. Bradford, X. Deng, S. Chiu, F. Kreuter, N. Barkay, A. Bilinski, B. Kim, T. Galili, D. Haimovich, S. LaRocca, S. Presser, K. Morris, J. Salomon, D. Vannette

University of Maryland

The Value of Science: Special Theme

J. Lane, B. Kim, F. Kreuter, A. Nunez

Harvard Data Science Review. Issue 4.2

Scaling Up Data Science for the Social Sciences

B. Kim

Harvard Data Science Review. Issue 3.2

Workbooks

B. Kim, C. Kern, J. Morgan, C. Hunter, A. Kumar

Big Data and Social Science, 2nd Edition. Chapman and Hall/CRC Press. 333-339

A Hybrid Household and Respondent-Driven Sampling Strategy for Reaching Underrepresented Groups

B. Kim, A. Gard

Joint Statistical Meetings, Toronto, CA

Machine Learning Model Selection for Complex Sample Survey Data

B. Kim

Symposium on Data Science and Statistics

Data 8 Adoption at University of Maryland

B. Kim

National Workshop on Data Science Education, Berkeley, CA

Population Size Estimation Using Multiple Respondent-Driven Sampling Surveys

B. Kim, M. Handcock

Joint Statistical Meetings, Denver, CO

Teaching Data Science Using Jupyter Notebooks and Binder

B. Kim

Symposium on Data Science and Statistics, Bellevue, WA

Assessing Respondent-Driven Sampling Using Geographical Data

B. Kim, M. Ogwal, E. Sande, H. Kiyingi, D. Serwadda, W. Hladik

North American Social Network Conference, Washington, D.C.

Advancing Computational Expertise in Neuroscience and Psychology (co-PI)

UMD Artificial Intelligence Interdisciplinary Institute: Course Development Grant

• Funding: $10,000

Cloud-based Active Learning for BSOS Statistics and Data Science Courses (PI)

UMD Teaching and Learning Transformation Center: Experiential Learning Grant

• Funding: $135,573.72

An Evaluation of Sampling Methodologies for Reaching Underrepresented Groups (PI)

University of Maryland BSOS Dean’s Research Initiative

• Funding: $19,999

Global Privacy Monitor (GPM): Learning from External Privacy Stakeholders

Facebook, Inc.

• Funding: $2,994,731

Modernizing NCSES Data Collection Approaches (co-PI)

National Science Foundation, NCSES BAA

• Funding: $88,802

Collaborative Research: Impacts of Hard/Soft Skills on STEM Workforce Trajectories (co-PI)

National Science Foundation, ECR: 1956114

• Funding: $401,716

Cooperative Agreement: Linked Dataset and Virtual Research Data Center (co-PI)

National Agricultural Statistics Service

• Funding: $653,490

Teaching Innovations Grant

University of Maryland

• Funding: $18,000

Teaching Innovations Grant

University of Maryland

• Funding: $5,000

Developing a New Course in Data Science: Introduction to Data Science for Social Sciences (PI)

UMD Year of Data Science

• Funding: $17,500

Feasibility Testing of a Respondent-Driven Sampling Approach to Recruit Human Trafficking Victims (co-PI)

Governor’s Office of Crime Control and Prevention

• Funding: $30,000

Guest Editor

• Harvard Data Science Review, Special Theme: Value of Science. Issue 4.2.

Reviewer

• Journal of Statistics and Data Science Education
• Journal of Survey Statistics and Methodology
• Journal of Statistical Software
• Public Opinion Quarterly
• Harvard Data Science Review
• Computational Statistics and Data Analysis
• Journal of Official Statistics