Bio

I am a 3rd year Ph.D. student in the Computer Science department at the Johns Hopkins University in Baltimore, Maryland. I work with Prof. Gregory Hager on reinforcement learning for robotics control and Prof. Mathias Unberath on computer vision for medical applications. I completed my undergraduate program at the Duke University in Durham, North Carolina with double major in Computer Science & Statistics and worked with Prof. Lawrence Carin in deep generative models.

Between grad school and college graduation, I worked at Mckinsey & Company as Business Analyst in Greater China Region, focused on TMT related strategy/management consulting. During undergrad, I also did a software enginnering internship at Facebook and a research internship at UC Berkeley with Prof. Dawn Song and Prof. Bo Li.

Works in computer vision, reinforcement learning and robotics

1. Learn Proportional Derivative Controllable Latent Space from Pixels Weiyao Wang , Marin Kobilarov, and Gregory D. Hager. Preprint. Paper

2. How You Act Tells a Lot: Privacy-Leaking Attack on Deep Reinforcement Learning Xinlei Pan, Weiyao Wang , Xiaoshuai Zhang, Bo Li, Jinfeng Yi, and Dawn Song. Accepted by AAMAS, 2019. Paper

Works in deep generative models

1. JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets Yunchen Pu, Shuyang Dai, Zhe Gan, Weiyao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence Carin. Accepted by ICML, 2018. Paper

2. Adversarial symmetric variational autoencoder Yuchen Pu, Weiyao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin. Accepted by NeuralPS, 2017. Paper

3. Triangle generative adversarial networks Zhe Gan, Liqun Chen, Weiyao Wang, Yuchen Pu, Yizhe Zhang, Lawrence Carin. Accepted by NeuralPS, 2017. Paper