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 the development of a new major in Social Data Science (officially launched Fall 2022), including working across departments to build an appropriate curriculum, managing the budget, and setting up infrastructure.
• Developed new courses to be part of Social Data Science, including SDSB233: Data Science for Social Sciences and SDSB326: Python Programming for Social Science.
• Launched a new undergraduate Data Curation Fellowship experience in which students identify, obtain, clean, and curate datasets to be used within social science courses, with a public-facing data resource webpage.
• Coordinated across colleges with the College of Information Co-Director to set up advising, teaching workload, and major requirements for students.

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, including adding SURV613: Machine Learning for Social Science as a required course and incorporating neural networks and LLMs into course material.
• Assisted graduate students in finding funding through grants or teaching assistant opportunities on campus.
• Chaired the Master’s Admissions committee and coordinated with the Director of JPSM on recruitment efforts.

Associate Research Professor

Social Data Science; Joint Program in Survey Methodology

• Developed and implemented innovative teaching assignments and tools including: cloud-based programming workbooks using JupyterHub (multiple classes); a board game decision analysis project for an undergraduate class (INST 354: Decision Making for Information Science); a data disclosure-proofing project (SURV 622: Fundamentals of Data Collection); and a custom R package containing tutorials (SURV 613: Machine Learning for Social Science).
• Taught classes on introductory statistics, data science, machine learning, and programming for both undergraduate and graduate students, utilizing traditional and flipped classroom formats in online, in-person, and HyFlex modes.

Assistant Research Professor

Social Data Science; Joint Program in Survey Methodology

Lecturer

Joint Program in Survey Methodology, University of Maryland

Consultant/Facilitator

The Coleridge Initiative

• Provided guidance as a facilitator with the Applied Data Analytics program for teaching record linkage, text analysis, network analysis, and machine learning to working professionals in public policy.
• Consulted on a research projects using machine learning for imputation with large scale transaction-level purchasing 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 Psychology, Economics, Education, and other fields in cleaning, merging, and preparing data for analysis.
• Helped clients with their dissertations, publications, and other projects using methods such as mixed modeling, principal components analysis, simulation, and more using a variety of statistical software.

Academic Mentor

UCLA Institute for Pure and Applied Mathematics

• Mentored a team of four undergraduate students with a research project for the Research in Industrial Projects for Students (RIPS) summer program in which an industry sponsor provides a real problem of interest.
• Provided a cooperative team environment for research by encouraging collaboration between students and advising students throughout the full research lifecycle.

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, 00, 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

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

Governor’s Office of Crime Control and Prevention

• Funding: $30,000

Dissertation Year Fellowship

University of California, Los Angeles

Collegium of University Teaching Fellows

University of California, Los Angeles

Graduate Dean’s Scholar Award

University of California, Los Angeles

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

Instructor

University of Maryland

• SDSB180: Introduction to Data Management (Fall 2024, Spring 2025, Fall 2025, Spring 2026)
• SDSB123: Social Data Science: Pathways and Applications (Fall 2024, Spring 2025, Fall 2025, Spring 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)
• SURV 613: Machine Learning 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)