About Me
Ph.D. in Applied Statistics at Columbia University. My research focuses on machine learning, reinforcement learning, causal discovery, GraphRAG, and AI agents, with an emphasis on developing reliable and interpretable AI methods for complex decision making.
Recent News
Paper accepted to NeurIPS 2026.
Received the Helen M. Walker Scholarship from Columbia University.
Paper accepted to AAAI 2026.
Received the Provost's Grant for Doctoral Research from Columbia University.
Paper accepted to CogSci 2025.
Presented research at the NCME 2025 Annual Meeting.
Education
2022–2026
Ph.D. in Applied Statistics, Columbia University
Focus: ML, GraphRAG, RL, Causal Discovery, AI
Advisors: James E. Corter (primary), Lawrence T. DeCarlo
Focus: ML, GraphRAG, RL, Causal Discovery, AI
Advisors: James E. Corter (primary), Lawrence T. DeCarlo
2020–2022
M.S. in Applied Statistics, Columbia University
2020
B.A. in Economics and Finance, University of Aberdeen
Publications
Published
Preprints
Under Review
Manuscripts currently under review.
Teaching
- Sep 2025HUDM 5059: Psychological measurementTeaching Assistant, Teachers College, Columbia University
- Sep 2024HUDM 4120: Introduction to StatisticsTeaching Assistant, Teachers College, Columbia University
- May 2024HUDM 5123: Linear Models Experimentl DsgnTeaching Assistant, Teachers College, Columbia University
- Sep 2022HUDM 6055: Latent Structure AnalysisTeaching Assistant, Teachers College, Columbia University
CV
View Full CV →Research Experience
Spring 2025 — Present: Graduate Research Assistant
- Institution: Teachers College, Columbia University
- Project: Education Leadership Data Analytics (ELDA)
- Research Focus: Conducted correspondence analysis on a large-scale dataset of state education records aligned with 16 NASEM equity indicators to uncover latent dimensions of equity representation across states.
Fall 2024 — Present: Ph.D. Researcher
- Institution: Columbia University, New York, NY
- Project: LLM-RAG for Automated Grading
- Research Focus: Developed a retrieval-augmented generation (RAG) framework for automated short-answer grading and feedback, integrating LLMs with psychometric and causal inference principles.
Jan 2023 — Jun 2024: Research Assistant
- Institution: Columbia University, New York, NY
- Project: NSF-Funded Course Recommendation-Causal Inference
- Research Focus: Processed large-scale NCES datasets to model student math course pathways across grade levels. Applied causal machine learning methods (TMLE, Causal Forests) to estimate heterogeneous intervention effects and design optimal course recommendation rules that promote fairness and maximize student outcomes.
Jan 2021 — Feb 2022: Master's Researcher
- Institution: Columbia University
- Project: Machine and Deep Learning Research
- Research Focus: Built predictive models for crime data using principal component regression and model selection, improving predictive accuracy from 55% to 90%. Developed NLP pipelines for sentiment analysis and topic modeling, and analyzed classroom interaction networks using centrality and community measures to examine peer influence and group dynamics.
Skills
Tools
- Python
- R
- SQL
- SPSS
Machine Learning & AI
- Machine Learning
- LLM-RAG
- LLM-Causal Inference
Academic Service
- Forty-First AAAI Conference on Artificial Intelligence (AAAI-27) Program Committee Member, Main Technical Track and Special Track on AI for Social Impact2027
- Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS) Reviewer2026
- AAAI-26 Undergraduate Consortium Program Committee Member2026
- Annual Conference of the Cognitive Science Society (CogSci) Reviewer2025, 2026
Professional Memberships
- 2024–2025American Educational Research Association (AERA)Student Member
- 2024–2025National Council on Measurement in Education (NCME)Student Member
- 2025Cognitive Science Society (CogSci)Member
- 2025Society for Research on Educational Effectiveness (SREE)Member
- 2025–2026Association for the Advancement of Artificial Intelligence (AAAI)Student Member
Awards
- Helen M. Walker Scholarship 2025
- Provost's Grant 2025
- Doctoral Fellowship 2023
Selected Coursework
An applied course on reinforcement learning for large language models, covering the full workflow from autoregressive sampling and policy-gradient foundations to hardware-aware rollout systems and modern group-based policy optimization methods.
Advanced seminar exploring supervised learning, unsupervised learning, learning theory, reinforcement learning.
Introduction to reinforcement learning course series by MIT Online.
This is an introductory and applied course in Casual Inference .