Ph.D. in Electrical and Computer Engineering | |
University of Texas at Austin | |
Degree conferred 08/2023 | |
B.S. in Electronic Engineering | |
Tsinghua University | |
Degree conferred 06/2018 |
Senior Machine Learning Engineer | |
Qualcomm Technologies Inc. | |
07/2023- · San Diego |
Applied Scientist Intern | |
Amazon.com Services Inc. (AWS AI) | |
Advisor: Dr. Aston Zhang, Dr. Mu Li, Dr. Alex Smola | |
06/2022-02/2023 · Santa Clara | |
Applied Scientist Intern | |
Amazon.com Services Inc. (AWS AI) | |
Advisor: Dr. Aston Zhang, Dr. Mu Li, Dr. Alex Smola | |
05/2021-02/2022 · East Palo Alto | |
Research Intern | |
NVIDIA Corporation (AI Platform) | |
Advisor: Dr. Zhiding Yu, Dr. Anima Anandkumar | |
05/2020-08/2020 · Santa Clara | |
Research Intern | |
Kwai Inc. (Ytech AI Lab) | |
Advisor: Dr. Ji Liu | |
05/2019-08/2019 · Seattle |
Haotao Wang, Junyuan Hong, Aston Zhang, Jiayu Zhou, and Zhangyang Wang. “Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork.”
In Advances in Neural Information Processing Systems (NeurIPS), 2022. [pdf] [code] |
Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex Smola, and Zhangyang Wang. “Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition.”
In International Conference on Machine Learning (ICML), 2022. Long presentation [pdf] [code] |
Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, and Zhangyang Wang. “Removing Batch Normalization Boosts Adversarial Training.”
In International Conference on Machine Learning (ICML), 2022. [pdf] [code] |
Junyuan Hong, Haotao Wang, Zhangyang Wang, and Jiayu Zhou. “Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization.”
In International Conference on Learning Representations (ICLR), 2022. [pdf] [code] |
Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang. “AugMax: Adversarial Composition of RandomAugmentations for Robust Training.”
In Advances in Neural Information Processing Systems (NeurIPS), 2021. [pdf] [code] |
Haotao Wang*, Tianlong Chen*, Shupeng Gui, Ting-Kuei Hu, Ji Liu, and Zhangyang Wang. “Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free.”
In Advances in Neural Information Processing Systems (NeurIPS), 2020. [pdf] [code] *Equal contributions. |
Zhenyu Wu*, Haotao Wang*, Zhaowen Wang, Hailin Jin, and Zhangyang Wang. “Privacy-Preserving Deep Visual Recognition: An Adversarial Learning Framework and A New Dataset.”
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020. [pdf] [project homepage] [code and dataset] *Equal contributions. |
Haotao Wang, Shupeng Gui, Haichuan Yang, Ji Liu, and Zhangyang Wang. “GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework.”
In European Conference on Computer Vision (ECCV), 2020. Spotlight Oral [pdf] [code] |
Haotao Wang, Tianlong Chen, Zhangyang Wang, and Kede Ma. “I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively.”
In International Conference on Learning Representations (ICLR), 2020. [pdf] [code] |
Shupeng Gui*, Haotao Wang*, Haichuan Yang, Chen Yu, Zhangyang Wang, and Ji Liu. “Model Compression with Adversarial Robustness: A Unified Optimization Framework.”
In Advances in Neural Information Processing Systems (NeurIPS), 2019. [pdf] [code] *Equal contributions. |
Sina Mohseni, Haotao Wang, Zhiding Yu, Chaowei Xiao, Zhangyang Wang, and Jay Yadawa. “Practical Machine Learning Safety: A Survey and Primer.”
ACM Computing Surveys, 2022. [pdf] |