Presentation, American Medical Informatics Association (AMIA), Atlanta, GA
In this study, we applied 10 different machine learning models to predict EoCRC, and compared their performance with advanced large language models (LLM), using patient conditions, lab results, and observations within 6 months of patient journey prior to the CRC diagnoses.
Presentation, Joint Statistical Meeting (ASCO), Portland, OR
This study used advanced Large Language Models (LLM), like GPT-4, to predict EoCRC in a population comprised of multiple health systems across the United States.
Presentation, Joint Statistical Meeting (JSM), Portland, OR
This study compare the efficacy and accuracy of Large Language Models (LLMs) to tree-based Machine Learning (ML) algorithms in the context of student essay grading.
Presentation, American Educational Research Association (AERA), Philadelphia, PA
This study investigated the performance of extreme gradient boosting (xgboost), a machine learning algorithm, in comparison to traditional factor retention criteria such as Kaiser criteria, parallel analysis, and empirical Kaiser criterion.
Presentation, Association for Education Finance & Policy (AEFP), Baltimore, MA
This study investigates the efficacy and accuracy of Large Language Models (LLMs) in comparison to tree-based Machine Learning (ML) algorithms in the context of student essay grading.
Presentation, International Meeting of Psychometric Society (IMPS), College Park, MD
This paper examined exploratory approaches for measurement modeling with Lasso, Adaptive Lasso, and minimax concave penalty (MCP).
Presentation, National Council on Measurement in Education (NCME), Chicago, IL
This paper examined exploratory approaches for measurement modeling.
Presentation, American Education Research Association (AERA), San Diego, CA
This paper examined students’ experiences on racism and discrimination during the COVID-19 pandemic.
Presentation, National Council on Measurement in Education (NCME), Online conference
This paper proposes network analysis modeling as a novel approach for quantifying item process data at student, item, and student-item hybrid levels.
Presentation, American Education Research Association (AERA), Online conference
This study involves a longitudinal statewide analysis of factors that impact the retention and mobility of teachers of color.
Presentation, American Education Research Association (AERA), Online conference
This study investigated the effect of perceived academic achievement and psychosocial factors on adolescent depression across five years in a nationally representative sample of 1653 Korean adolescents.
Presentation, American Education Research Association (AERA), Online conference
This paper examined students’ experiences on racism and discrimination during the COVID-19 pandemic.