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Qixing Huang

Assistant Professor
The University of Texas at Austin

GDC 5'422, Austin, Texas 78751

Email: huangqx at cs.utexas dot edu



Short Bio

Qixing Huang obtained his PhD in Computer Science from Stanford University in 2012. From 2012 to 2014 he was a postdoctoral research scholar at Stanford University. From 2014 to 2016 he was a research assistant professor at Toyota Technological Institue at Chicago. He received his MS and BS in Computer Science from Tsinghua University. He has also interned at Google Street View, Google Research and Adobe Research. His research spans computer vision, computer graphics, computational biology and machine learning. In particular, his recent focus is on developing machine learning algorithms (particularly deep learning) that leverage Big Data to solve core problems in computer vision, computer graphics and computational biology. He is also interested in statistical data analysis, compressive sensing, low-rank matrix recovery, and large-scale optimization, which provide theoretical foundation for much of his research.

Curriculum Vitae


Google Scholar

My Research Group

Teaching and Reading Groups

  • Spring, 2017: Advanced Geometry Processing (Graduate Course).
  • Fall, 2016---: Machine Learning and Statistics Reading Group.

    Recent News

  • 3/2017: A paper was accepted by CVPR 2017.
  • 1/2017: A paper was accepted by AISTATS 2017.
  • 8/2016: Our course on "Data-Driven Shape Analysis and Processing" is accepted by SIGGRAPH ASIA' 16.
  • 8/2016: Our paper on "Normalized Spectral Map Synchronization" is accepted by NIPS' 16.
  • 8/2016: Our workshop on "3D Deep Learning" is accepted by NIPS' 16.
  • 7/2016: Our papers on "Unsupervised Texture Transfer from Images to Model Collections" and "A Scalable Active Framework for Region Annotation in 3D Shape Collections" are conditionally accepted by SIGGRAPH ASIA' 2016.
  • 7/2016: Our paper on "Capturing Dynamic Textured Surfaces of Moving Targets" is accepted by ECCV' 2016.
  • 5/2016: Our paper on "City-Scale Map Creation and Updating using GPS Collections" is accepted by KDD' 2016.
  • 4/2016: Our paper on "Connected Fermat Spirals for Layered Fabrication" is accepted by SIGGRAPH' 2016.
  • 3/2016: Our RECOMB 2016 paper "Joint alignment of multiple protein-protein interaction networks via convex optimization" is accepted by Journal of Computational Biology.
  • 3/2016: Two ORAL papers accepted at CVPR 2016. Both of them are on dense correspondences using CNN. One tackles the problem of correspondence representation, and another tackles the problem of training data.
  • 3/2016: Upcoming visits: Purdue University, University of Texas, Austin, University of California Riverside, Washington University at St. Louis, University of Toronto, University of Southern California.
  • 12/2015: The source code of my SIGGRAPH'15 paper on single-view reconstruction via joint analysis of image and shape collections can be found here.
  • 12/2015: A paper titled "Joint alignment of multiple protein-protein interaction networks via convex optimization" has been accepted for presentation in RECOMB 2016. This is a joint work with Somaye Hashemifar and Jinbo Xu from TTI Chicago.

    Last update: Jan. 24, 2017