I'm a second-year master's student at Nanjing University, working with Prof. Jie Guo. I also work remotely with Prof. Ling-Qi Yan on various research projects. Prior to that, I received my bachelor's degree in Computer Science & Technology from Southeast University in 2023.
My research interests involve generating realistic images. Previously, I worked on physically-based rendering, with a focus on path sampling techniques, including specular light transport and path guiding. My research revolves around physical constraints, exploring both analytical formulations and fitted/learned data-driven solutions.
I will graduate in June 2026 and am actively seeking a Ph.D. position for Fall 2026.
Deriving vertex position and irradiance bounds for each triangle tuple based on specular polynomials and Bernstein bounds for rational functions, reducing the search domain for specular light transport.
Combining the estimates generated in each guiding iteration by leveraging the importance distributions from multiple guiding iterations.
A polynomial formulation of specular constraints (multivariate & bivariate), converted into univariate polynomials using resultants, then efficiently solved.
Importance sampling light path derivatives using a deterministic mixture of primal and differential distributions, with the optimal mixture weight conditioned on the BSDF of each vertex in the path prefix.
Importance sampling specular chains with seed placement using continuous probability distributions reconstructed from historical samples.