Yuting Yang

35 Olden Street

Princeton, NJ 08540-5233


About me

I am a first year Ph.D. student in the Computer Science Department at Princeton University. My research is focused on compiler techniques for smoothing, optimizing, and learning parameters for graphics and vision programs.

Before joining Princeton, I received 3 years of Ph.D. training at the University of Virginia. My advisor at UVa is Dr. Connelly Barnes.

Here is my CV.

Research Publications (Google Scholar)

Approximate Program Smoothing Using Mean-Variance Statistics, with Application to Procedural Shader Bandlimiting

Yuting Yang, Connelly Barnes

Eurographics 2018

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.

[ project page ] [ paper ] [ supplemental ] [ video (Vimeo) ]

VizGen: Accelerating Visual Computing Prototypes in Dynamic Languages

Yuting Yang, Sam Prestwood, Connelly Barnes


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.

[ project page ] [ paper ]

Intersection monitoring from video using 3D reconstruction

Yuting Yang, Camillo Taylor, Daniel Lee

ITS International January February 2016

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.

[ article ]


M.S., Electrical Engineering

I received my master degree at the University of Pennsylvania in May 2015. My thesis was "Road Intersecting Monitoring from Video with 3D Reconstruction", advised by Dr. Daniel Lee and Dr. Camillo Taylor.

B.S., Electronics and Information Engineering

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.

Theme uses Bootstrap. Inspired by Fuwen Tan's homepage.