Suk, Youmi (ys2952)

Youmi Suk

Assistant Professor of Applied Statistics

Office Location:

552 GDodge

Educational Background

Ph.D., Educational Psychology (Quantitative Methods), University of Wisconsin-Madison, 2021

M.S., Statistics, University of Wisconsin-Madison, 2019

M.A., Education (Educational Measurement and Evaluation), Seoul National University, 2016

B.S., Earth Science Education, Seoul National University, 2014

Scholarly Interests

Youmi Suk is an Assistant Professor in the Measurement, Evaluation, and Statistics program in the Department of Human Development at Teachers College, Columbia University. Prior to joining Teachers College, she was an Assistant Professor in the School of Data Science at the University of Virginia. Dr. Suk’s research areas include causal machine learning, multilevel modeling, optimal treatment regimes, algorithmic fairness, and analysis of process data. Her current research projects fall into four categories: (i) robust machine learning for causal inference in multilevel observational studies, (ii) optimal treatment regimes in education for data-driven, personalized learning, (iii) evaluating testing accommodations with quasi-experimental devices and process data, and (iv) evaluating algorithmic fairness in educational settings. Dr. Suk has received awards and grants for her work, including the National Science Foundation (NSF) and American Educational Research Association (AERA). Also, she has taught courses on linear regression, data visualization, and programming for data science.

Selected Publications

Lyu, W., Kim, J.-S., & Suk, Y. (2022). Estimating heterogeneous treatment effects within latent class multilevel models: A Bayesian approach. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986221115446

Suk, Y., Steiner, P. M., Kim, J.-S., & Kang, H. (2022). Regression discontinuity designs with an ordinal running variable: Evaluating the effects of extended time accommodations for English-language learners. Journal of Educational and Behavioral Statistics. 47(4), 459-484. https://doi.org/10.3102/10769986221090275

Suk, Y., & Kang, H. (2022). Tuning random forests for causal inference under cluster-level unmeasured confounding. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2021.1994364

Suk, Y., & Kang, H. (2022). Robust machine learning for treatment effects in multilevel observational studies under cluster-level unmeasured confounding. Psychometrika, 87(1), 310–343. https://doi.org/10.1007/s11336-021-09805-x

Suk, Y., Kang, H., & Kim, J.-S. (2021). Random forests approach for causal inference with clustered observational data. Multivariate Behavioral Research, 56(6), 829-852. https://doi.org/10.1080/00273171.2020.1808437

Suk, Y., Kim, J.-S., & Kang, H. (2020). Hybridizing machine learning methods and finite mixture models for estimating heterogeneous treatment effects in latent classes. Journal of Educational and Behavioral Statistics, 46(3), 323-347. https://doi.org/10.3102/1076998620951983

01/2023-12/2025 | Principal Investigator, Tailoring Personalized Mathematics Education for High School Students Using Dynamic Treatment Regimes, National Science Foundation (NSF), $349,995. https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225321&HistoricalAwards=false

06/2022-05/2024 | Principal Investigator, A Within-Group Approach to Random Forests for Evaluating Educational Programs in Multilevel Studies, American Educational Research Association-National Science Foundation (AERA-NSF), $35,000. https://www.aera.net/Newsroom/AERA-Highlights-E-newsletter/AERA-Highlights-February-2022/AERA-Announces-Dissertation-and-Research-Grant-Awardees

03/2021-06/2022 | Principal Investigator, Regression Discontinuity Design with an Ordinal Discrete Running Variable: Evaluating the Effects of Extended Time Accommodations for English Language Learners, American Educational Research Association (AERA) Division D, $5,000.

Fall 2022 | HUDM 5122 Applied Regression Analysis, Dept of Human Development, Teachers College, Columbia University.

Spring 2022 | DS 6999 Independent Study, School of Data Science, University of Virginia.

Spring 2022 | DS 2001 Programming for Data Science, School of Data Science, University of Virginia.

Spring 2022 | DS 3003 Communicating with Data, School of Data Science, University of Virginia.

Fall 2021 | DS 3003 Communicating with Data, School of Data Science, University of Virginia.     

Fall 2020 | ED PSY 763 Regression Models in Education, Dept of Educational Psychology, University of Wisconsin-Madison.

Spring 2020 | ED PSY 763 Regression Models in Education, Dept of Educational Psychology, University of Wisconsin-Madison.

Fall 2019 | ED PSY 763 Regression Models in Education, Dept of Educational Psychology, University of Wisconsin-Madison.

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