I am a Ph.D. student in the Computer Science Department at Princeton University working with Prof. Adam Finkelstein. My research is focused on compiler techniques for smoothing, optimizing, and learning parameters for graphics and vision programs.
Here is my CV.
We present a fully automatic system to optimize the parameters of black-box hardware and software image processing pipelines according to any arbitrary (i.e., application-specific) metric. We leverage a differentiable mapping between the configuration space and evaluation metrics, parameterized by a convolutional neural network that we train in an end-to-end fashion with imaging hardware in-the-loop. Unlike prior art, our differentiable proxies allow for high-dimension parameter search with stochastic first-order optimizers, without explicitly modeling any lower-level image processing transformations.
This paper introduces a general method to approximate the convolution of an arbitrary program with a Gaussian kernel which facilitates smoothing the program. We give several approximations including a novel adaptive Gaussian approximation that is accurate up to the second order in the standard deviation of the smoothing kernel. We construct a compiler framework that automatically chooses approximations for different parts of the program. We then apply this framework to bandlimit procedural shaders.
This paper introduces a novel domain-specific compiler, which translates visual computing programs written in dynamic languages to highly efficient code. We define "dynamic" languages as those such as Python and MATLAB, which feature dynamic typing and can be useful for rapid prototyping, but introduces significant overheads in program execution time. We introduce a compiler framework for accelerating visual computing programs written in general-purpose dynamic languages. Our compiler allows frequently orders of magnitude performance gains over general compilers for dynamic languages by specializing the compiler for visual computation.
Traffic information can be collected from existing inexpensive roadside cameras but extracting it often entails manual work or costly commercial software. We proposed a method that tracks and counts vehicles in the video and to use the tracking information to compute a 3D model for the vehicles and visualise the 2D road situation into 3D. The 3D model can provide feedback on the tracking and counting model for future research.
I received my bachelor degree at Huazhong University of Science and Technology in June 2013. My thesis was "Motion Detection from Video Surveillance", advised by Dr. Hao Wen.