About Me
Hi! I am a Ph.D. candidate in Statistics at The Ohio State University, advised by Prof. Yuan Zhang.
My research develops principled and scalable methods for modern AI systems, especially where statistical theory can make black-box models more reliable, interpretable, and accountable. I work on data-centric AI, explainable AI, data valuation, Shapley-based attribution, and uncertainty quantification.
The common thread in my work is simple: modern AI systems depend on data, model outputs, and human preferences that are often structured, strategic, noisy, or expensive to evaluate. I design methods that respect that structure while remaining computationally feasible.
Research Focus
- Data-centric AI and data valuation: quantifying the value of data points, data groups, and contributors in machine learning systems.
- Explainable AI and attribution: developing Shapley-based methods that account for priority, precedence, and structured dependencies.
- Uncertainty quantification: building conformal prediction tools that are statistically valid and computationally efficient.
- Statistical foundations for AI: turning theoretical guarantees into algorithms that scale to modern AI applications, including generative AI and LLM-related settings.
Selected Highlights
- Priority-Aware Shapley Value (ICML 2026): introduces PASV, a Shapley-based framework that incorporates precedence constraints and contributor-specific priority weights for more structure-faithful attribution.
- Faithful Group Shapley Value (NeurIPS 2025; ICML 2025 DataWorld Workshop Best Paper Award Honorable Mention): develops a robust method for group-level data valuation that is resistant to strategic group splitting.
- Leave-One-Out Stable Conformal Prediction (ICLR 2025): uses leave-one-out algorithmic stability to make conformal prediction faster while maintaining statistical validity.
See the full list on my Publications page or my Google Scholar profile.
Experience
Statistics Researcher Intern, United Airlines (May 2026 - Present) Working on applied statistical and machine learning problems in an industry setting.
Graduate Research Assistant, The Ohio State University (Aug 2025 - Present) Researching data valuation, attribution, and uncertainty quantification for modern AI systems.
Graduate Teaching Assistant, The Ohio State University (Aug 2023 - Present) TA for 11 courses including Advanced Theory of Statistics, Statistical Computation, and Statistical Machine Learning.
Statistical Consultant, The Ohio State University (May 2025 - Jul 2025) Advised 6 clients across psychology, agricultural science, civil engineering, and related fields on statistical modeling and analysis.
Education
The Ohio State University, Columbus, OH Ph.D. in Statistics; Minor in Computer Science (Aug 2022 - Expected Jun 2027) Advisor: Dr. Yuan Zhang
Korea University, Seoul, South Korea Master in Statistics (Mar 2020 - Aug 2021) Advisor: Dr. Taeryon Choi Thesis: Fully Bayesian Semiparametric Two-stage Meta-analysis
Korea University, Seoul, South Korea Bachelor of Statistics (Mar 2014 - Feb 2020)
Honors & Awards
- Gold Reviewer Award, ICML 2026 (Jun 2026) Awarded to the top 25% of reviewers based on area-chair ratings of submitted reviews.
- Best Paper Award Honorable Mention, ICML 2025 DataWorld Workshop (Jul 2025) Awarded to Faithful Group Shapley Value as one of two honorable mentions in the workshop’s Best Paper Awards.
- Ransom & Marian Whitney Award for Research, Department of Statistics, The Ohio State University (2026) Recognizes independence, creativity, originality, progress, and potential for publication/application in Ph.D. research. One of two awardees.
- University Fellowship, The Ohio State University (2022-2023)
- Academic Excellence Scholarships, Korea University (2015-2019)
Skills
- Languages/Tools: Python, R, LaTeX, SQL, C++, MATLAB, SAS, Julia
- ML/AI: Scikit-learn, PyTorch, TensorFlow, Keras