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Summary

I have been researching Monte Carlo integration for physically based rendering and its intersection with machine learning. My work focuses on designing Monte Carlo estimators and sampling strategies that are adaptive and tailored to each problem’s specific structure, in order to maximize efficiency and reduce variance. My most recent research works include: Stratified Histogram sampling for reservoir Resampling (SIGGRAPH 2025), Importance sampling and multiple importance sampling for gradient estimation (ICPRAM 2025), Perceptual error distribution for animated rendering (SIGGRAPH Asia 2023), Filtered sliced optimal transport sampling (SIGGRAPH Asia 2022), and Regression based Monte Carlo (SIGGRAPH 2022). In addition to research, I am actively involved in teaching physically based rendering, sharing both the theoretical foundations and practical implementation techniques with students and practitioners.

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