Welcome to My Homepage!

I'm Yikai Wang, currently pursuing a Ph.D. degree in the Statistics and Operations Research Department (STOR) at the University of North Carolina at Chapel Hill (UNC).
I received my B.S. degree in Computer Science from Zhejiang University, China.
My research interests include large language models, reinforcement learning and optimization.
Outside research, I enjoy playing Weiqi, astronomy, history, movies, music, all kinds of sports, and the many curious facts and stories that make the world endlessly interesting. I also play the bamboo flute and guitar.
Research
Why Agentic Design is Necessary for Data Analytics
Calibrating Conditional Risk
Risk Profiling and Modulation for LLMs
REMAST Real-time Emotion-based Music Arrangement with Soft Transition
SongDriver Real-time Music Accompaniment Generation without Logical Latency nor Exposure Bias
Blogs
tmux Is Not a Shortcut Cheat Sheet. It Is a Terminal Workflow.
March 18 Group Meeting Notes: Agentic Vending, Pre-Training Data, and Constitutional Classifiers
verl, vLLM, and FlashAttention: How the Stack Actually Fits Together
GRPO and Its Variants: What Actually Changes, and Why It Matters
PPO Is Not Just a Clip Trick
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Teaching
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STOR 113: Decision Models for Business and Economics
Terms: 2023 Fall | Role: Teaching Assistant
An introduction to multivariable quantitative models in economics. Mathematical techniques for formulating and solving optimization and equilibrium problems are developed, including elementary models under uncertainty.
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STOR 455: Methods of Data Analysis
Terms: 2024 Spring, 2024 Fall | Role: Teaching Assistant
Review of basic inference; two-sample comparisons; correlation; introduction to matrices; simple and multiple regression (including significance tests, diagnostics, and variable selection); analysis of variance; use of statistical software. Honors version available.
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STOR 155: Introduction to Data Models and Inference
Terms: 2024 Summer, 2025 Summer | Role: Instructor
Data analysis; correlation and regression; sampling and experimental design; basic probability (random variables, expected values, normal and binomial distributions); hypothesis testing and confidence intervals for means, proportions, and regression parameters; use of spreadsheet software. Honors version available.
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STOR 120: Foundations of Statistics and Data Science
Terms: 2025 Spring, 2025 Fall | Role: Lab Instructor
The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design. Honors version available.