Research is my profession, and everlasting passion. I work on Machine Learning, Computer Vision, Optimization, as well as their interdisciplinary applications.

I have had the unbelievable fortune to work with a group of ROCKSTAR students, who are much better than myself! My group, Visual Informatics@Texas, Austin (VITA), regularly publishes in CVPR/ICCV/ECCV, NeurIPS/ICML/ICLR, etc.

As of 08/2020, my top-5 favorite papers are (with my admittedly very biased and time-varying taste; and they're embarrassingly on five different topics):

• T. Chen, S. Liu, S. Chang, Y. Cheng, L. Amini, and Z. Wang, “Adversarial Robustness: From Self-Supervised Pretraining to Fine-Tuning”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
• H. Wang, T. Chen, Z. Wang, and K. Ma, "I am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively", International Conference on Learning Representations (ICLR), 2020.
• X. Gong, S. Chang, Y. Jiang, and Z. Wang, “AutoGAN: Neural Architecture Search for Generative Adversarial Networks”, IEEE International Conference on Computer Vision (ICCV), 2019.
• J. Liu, X. Chen, Z. Wang, and W. Yin, “ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA”, International Conference on Learning Representations (ICLR), 2019.
• B. Li, W. Ren, D. Fu, D. Tao, D. Feng, W. Zeng, and Z. Wang, “Benchmarking Single Image Dehazing and Beyond”, IEEE Transactions on Image Processing (TIP), 2019.