Qixing Huang
Assistant Professor
Department of Computer Science
The University of Texas at Austin


Qixing Huang is an assistant professor of Computer Science at the University of Texas at Austin. He obtained his PhD in Computer Science from Stanford University. He was a research assistant professor at Toyota Technological Institute at Chicago before joining UT Austin.

Dr. Huang's research spans the fields of computer vision, computer graphics, and machine learning, and publishes extensively in venues such as SIGGRAPH, CVPR, ICCV, ECCV, NeuriPS, ICML, and etc. 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 provides theoretical foundation for his research. He also received the best paper award at the Symposium on Geometry Processing 2013, the best dataset award at the Symposium on Geometry Processing 2018, and the most cited paper award of Computer-Aided Geometric Design in 2010 and 2011.

News

  • Aug. 2020: I will serve on the Technical Papers Committee of SIGGRAPH 2021.

  • Aug. 2020: Checkout a code repository for multi-scan registration. It includes reference implementations of two widely used multi-scan registration techniques and our SIGGRAPH 2020 paper for uncertainty quantification.

  • Aug. 2020: Checkout a new benchmark dataset for the task of shape classification of mechanical parts.

  • July 2020: Two papers accepted by European Conference on Computer Vision (or ECCV) 2020.

  • June 2020: The recorded videos of my CVPR 2020 workshop and tutorial talks are online. Please refer to the links in talks.

  • May 2020: I will give an invited talk on hybrid 3D representations at the First Workshop on Deep Learning Foundations of Geometric Shape Modeling and Reconstruction of CVPR 2020. I will co-organize a tutorial and a workshop.

  • May 2020: The journal version of "GAN-SRAF: Sub-Resolution Assist Feature Generation using Conditional Generative Adversarial Networks" was accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

  • Apr. 2020: One paper on Uncertainty Quantification for Multi-Scan Registration was accepted by SIGGRAPH 2020.

  • Mar. 2020: I served on the Technical Papers Committee of SIGGRAPH 2020.

  • Feb. 2020: Two papers (hybrid pose and hybrid relative pose((oral))) on 3D recogntion under hybrid representations were accepted by CVPR 2020.

  • Sep. 2019: One paper one joint learning of neural networks was accepted by NeurIPS 2019.

  • Jul. 2019: I will serve as a program co-chair of Symposium on Geometry Processing 2020, an advisory boarder member of Eurographics 2020, and a Senior-PC of AAAI 2020.

  • Jul. 2019: One paper on transformation synchronization of symmetric objects was accepted by ICCV 2019.

  • Mar. 2019: Two papers conditionally accepted by SIGGRAPH 2019. Many thanks for my colleagues, students and summer interns.

  • Mar. 2019: Six papers accepted by CVPR 2019, including 3 Orals. Many thanks for my students and collaborators.

  • Feb. 2019: Our tutorial (together with Xiaowei Zhou, Junyan Zhu and Tinghui Zhou) on "Map Synchronization: from Object Correspondences to Neural Networks" was accepted by CVPR 2019.

  • Feb. 2019: Our paper on "GAN-SRAF: Sub-Resolution Assist Feature Generation using Conditional Generative Adversarial Networks" was accepted by Design and Automation Conference (or DAC) 2019.

  • Jan. 2019: Our workshop (together with Angel Chang, Daniel Ritchie and Manolis Savva) on "3D Scene Generation" was accepted by CVPR 2019.

  • Nov. 2018: I will serve as Area Chairs of ICCV 2019 and CVPR 2019.

  • Jul. 2018: Four papers accepted by European Conference on Computer Vision (or ECCV 2018).

  • Jun. 2018: Arjun defended his undergraduate thesis on multi-view supervision for 3D inference tasks.

  • May 2018: One paper accepted by International Conference on Machine Learning (or ICML 2018).

Contact

  • Office: Gates Dell Complex, Room 5422

  • Mailing address: 2317 Speedway, Austin, Texas 78712

  • Email: huangqx at cs dot utexas dot edu