I am a research scientist on machine learning at Snap Research.
If you are interseted in working at Snap, please send the inquiries about internship or full-time positions to my university email address with your resume and a short description of your research/engineering experience. Thanks!
"Nothing is more practical than a good theory." --Vladimir Vapnik
I am mostly interested in theoretical computer science and different aspects of mathematics, especially functional analysis and geometry. From time to time I contribute to statistical machine learning theory, including theory and application of deep learning, subspace clustering, manifold learning, sparse representation and compressive sensing, nonparametric models, probabilistic graphical models and generalization analysis of classification, semi-supervised learning and clustering.
In my early years I also conducted research on computer vision and computer graphics. Click to see the details
View more details in my CV:
Urbana, IL, 61801
Email: yyang58 -AT- illinois.edu
Honors and Awards
2016 ICML Scholarship (Travel Award)
2012 AAAI Scholarship
2010 Carnegie Institute of Technology Dean's Tuition Fellowship
2009 "Lu Zeng Yong" CAD&CG High-Tech Award(for six researchers in China who have made distinguished achievements in Computer-Aided Design&Computer Graphics)
Before 2005: Bronze medal in National Senior High School Mathematics Competition, First Prize of National Junior High School Mathematics Competition
Professional Services & Activities
Program Committee Member: International Joint Conferences on Artificial Intelligence (IJCAI) 2017, IJCAI 2015
Reviewer: Journal of Machine Learning Research (JMLR), IEEE Transactions on Image Processing (TIP), Knowledge and Information Systems (KAIS), Pattern Recognition, Machine Vision and Applications (Springer Journal)
Reviewer and Organizer: ACM Multimedia Workshop on Surreal Media and Virtual Cloning, in conjunction with ACM Multimedia 2010
Ten years of experience in C/C++ programming and software design.
Research Intern, Microsoft Research at Redmond, WA. May 2015 to Aug. 2015. online probabilistic topic models for large-scale application with CUDA C/C++ programming.
Research Intern, Microsoft Research at Redmond, WA. May 2014 to Aug. 2014. Developed parallelized and accelerated probabilistic topic models with CUDA C/C++ programming.
Research Intern, Hewlett-Packard Labs, Palo Alto, California. May 2011 to Aug. 2011. Efficient markerless augmented reality with C/C++ programming.
Please refer to the details of my projects here.
Recent Publications (Full List)
Yingzhen Yang, Jiashi Feng, Jiahui Yu, Jianchao Yang, Pushmeet Kohli, Thomas S. Huang.
Neighborhood Regularized L1-Graph.
Proc. of Conference on Uncertainty in Artificial Intelligence (UAI) 2017.
Yingzhen Yang, Jiahui Yu, Pushmeet Kohli, Jianchao Yang, Thomas S. Huang
Support Regularized Sparse Coding and Its Fast Encoder.
Proc. of International Conference on Learning Representations (ICLR) 2017.
Yingzhen Yang, Jiashi Feng, Nebojsa Jojic, Jianchao Yang, Thomas S. Huang
L0-Sparse Subspace Clustering.
Proc. of European Conference on Computer Vision (ECCV) 2016. (Oral Presentation, Among 11 Best Paper Candidates) [Paper] [Supplementary] [Slides] [Code (Both CUDA C++ for extreme efficiency and MATLAB)]
Our work establishs almost surely equivalence between L0 sparsity and subspace detection property, under the mild condition of i.i.d. data generation and nondegenerate distribution. This is much milder than previous conditions required by L1 sparse subspace clustering literature. Click to see the key points in the talk
Yingzhen Yang, Zhangyang Wang, Zhaowen Wang, Shiyu Chang, Ding. Liu, Honghui Shi, Thomas S. Huang.
Epitomic Image Super-Resolution.
Proc. of AAAI Conference on Artificial Intelligence (AAAI) 2016 (Best Poster/Best Presentation Finalist for Student Poster Program). [Project&Code]