CV

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Contact Information

Name Brian Kim
Email kimbrian@umd.edu

Experience

  • 2021 - present

    College Park, MD

    Co-Director, Social Data Science Major
    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 (https://bsos-data.umd.edu).
    • Coordinated across colleges with the College of Information Co-Director to set up advising, teaching workload, and major requirements for students.
  • 2024 - present

    College Park, MD

    Director of Graduate Studies, Joint Program in Survey Methodology
    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.
  • 2024 - present

    College Park, MD

    Associate Research Professor
    Joint Program in Survey Methodology, University of Maryland
    • 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.
    • As part of an NSF grant, managed and oversaw the development and day-to-day operations of data analytics training courses for working professionals and graduate students at the National Center for Science and Engineering Statistics (NCSES) at the National Science Foundation (NSF).
  • 2020 - 2024

    College Park, MD

    Assistant Research Professor
    Joint Program in Survey Methodology, University of Maryland
  • 2017 - 2020

    College Park, MD

    Lecturer
    Joint Program in Survey Methodology, University of Maryland
  • 2020 - 2022

    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.
  • 2014 - 2016

    Los Angeles, CA

    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.
  • 2015 - 2017

    UCLA

    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) 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.

Education

  • 2012 -

    Los Angeles, CA

    2017-10
    University of California, Los Angeles
    Statistics
    • Dissertation: Population Size Estimation using Multiple Respondent-Driven Samples
  • 2008 - 2012

    BA
    Amherst College
    Mathematics & Philosophy
    • Graduated with Honors in Mathematics
    • Thesis: Using Mode Identification Clustering Methods with Pitch F/X Data

Research Interests

  • Data Science Education; Social Network Analysis; Network Sampling Methods; Respondent-Driven Sampling; Population Size Estimation: Capture-Recapture and Multiple List methods; Social Science Applications of Machine Learning

Publications

Software and Data

Book Chapters and Non-Refereed Publications

Selected Conference Presentations

Grants

Awards

  •

Other Academic Work

Teaching

Mentorship

Service

  • Faculty Coordinator, Joint Program in Survey Methodology Short Course Program
  • Co-Chair, Social Data Science Program Committee
  • Co-Chair, Department of Psychology Lecturer Search Committee
  • Chair, Social Data Science Curriculum Committee
  • Chair, Search Committee for Executive Director of the Center for Advances in Data and Measurement
  • Member/Chair, JPSM/MPSM Masters Admissions Committee
  • Member, School of Information Studies Lecturer Search Committee
  • Member, Social Data Science Advisor Search Committee
  • Member, Social Data Science Steering Committee
  • Member, JPSM/MPSM Teaching Assignment Committee

Technical Strengths

Statistical Software: Highly skilled in R, SQL and Python, including various packages such as Rcpp, caret, tidyverse, tidymodels, and more (in R); and pandas, scikit-learn, nltk, tensorflow, and more (in Python).
Statistics: Highly skilled in a variety of methods, including but not limited to hypothesis testing, regression, mixed models, clustering analysis, social network analysis, social network models, network sampling methods, Monte Carlo simulation, Bayesian models, Machine Learning, text analysis, web scraping, and more.
Other Software: Skilled or proficient in the use of many other programs, including, but not limited to, Microsoft Word, Excel and Powerpoint; LaTeX.