Thanawat Sornwanee

Thanawat Sornwanee

PhD Student, Stanford GSB

I’m a second-year Ph.D. student in Economic Analysis & Policy group at Stanford Graduate School of Business (GSB) with Ph.D. minor in Electrical Engineering. My Ph.D. fields are in Advanced Theory, Advanced Econometrics, and Market Design.

Previously, I received an M.S. degree in Computational and Mathematical Engineering (ICME) and a B.S. degree in Symbolic Systems (with honors) from Stanford University. I am honored to have Prof. Yuliy Sannikov as the advisor and Prof. Robert Wilson as the second reader for my undergraduate thesis.

ธนวัฒน์ ศรวณีย์

Research Interests

Economic Theory

Working Papers

1-Dimensional Normal Competitive Market Equilibrium
Thanawat Sornwanee.
 Abstract: We introduce a new microeconomics foundation of a specific type of competitive market equilibrium that can be used to study several markets with information asymmetry such as commodity market, credit market, and insurance market.
Score-Debiased Kernel Density Estimation, presented at ICLR Frontiers in Probabilistic Inference Workshop 2025.
Elliot Epstein*, Rajat Dwaraknath*, Thanawat Sornwanee*, John Winnicki, and Jerry Liu.
 Abstract: We propose a novel method for density estimation that leverages an estimated score function to debias kernel density estimation (SD-KDE). In our approach, each data point is adjusted by taking a single step along the score function with a specific choice of step size, followed by standard KDE with a modified bandwidth. The step size and modified bandwidth are chosen to remove the leading order bias in the KDE. Our experiments on synthetic tasks in 1D, 2D and on MNIST, demonstrate that our proposed SD-KDE method significantly reduces the mean integrated squared error compared to the standard Silverman KDE, even with noisy estimates in the score function. These results underscore the potential of integrating score-based corrections into nonparametric density estimation.
Reinforced Social Learning, presented at INFORMS Annual Meeting 2024.
Thanawat Sornwanee.
 Abstract: I present a knowledge-free mechanism to ensure the zero asymptotic average regret in a sequential social learning framework with binary state of the world and homogeneous preference. This mechanism also ensures that the state of the world can be asymptotically learned, regardless of the choice of Nash equilibrium employed. The asymptotic optimality of the mechanism will also shown to be preserved under different economic scenarios, such as when each agent may be blinded from the history of actions and/or received a utility shock.


Teaching & Services

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Awards

Talks & Presentations

2026

2025

2024

Contact

Email: tsornwanee [at] stanford.edu