Scholar
Yeseul Jeon
Google Scholar ID: SCF5a_cAAAAJ
Texas A&M University
Citations & Impact
Citations
70
H-index
3
i10-index
2
Publications
18
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Korean International Statistical Society (KISS) - Outstanding Student Paper Award, January 2023
- Southern Regional Council on Statistics (SRCOS) - Student Travel Award, October 2022
- Joint Statistical Meetings (JSM) - Winning the Text Analysis Interest Group Presentation Competition Interest Group of the American Statistical Association (ASA), August 2021
Research Experience
- Postdoctoral Researcher, Department of Statistics, Texas A&M University, and Department of Epidemiology & Biostatistics, University of California, San Francisco, September 2024 -
- Visiting Scholar, Department of Bioinformatics, The Ohio State University, June 2022 - July 2023
- AI Researcher, Laboratory for Artificial Intelligence, Vive Company, February 2016 - February 2021
Education
- Ph.D. in Statistics, Yonsei University, 2024 (Thesis advisors: Dr. Jaewoo Park, co-advised by Dr. Ick-Hoon Jin)
- B.A. in Statistics, Sungshin Women's University, 2016
Background
- Research interests include explainable Bayesian deep learning models that integrate the interpretability of statistical frameworks with the representational power of deep learning. The goal is to enhance both predictive performance and interpretability while providing principled uncertainty quantification grounded in Bayesian theory.
Miscellany
- During her Ph.D., her research centered on the development of methodological tools in three main areas: (i) interpretable Bayesian deep learning for high-dimensional and correlated data, (ii) latent space models for complex networks, and (iii) spatial modeling approaches for transcriptomic data. She proposed Bayesian convolutional neural network-based generalized linear models that adapt to structured input data such as images and geospatial features. She also developed novel Bayesian network models to capture latent interactions within graphs, with applications to topic modeling and psychometrics. Lastly, she designed Bayesian spatial modeling frameworks to enhance statistical inference in emerging biomedical data, such as spatial transcriptomics.
Co-authors