Haowen Wang | 王皓雯

I'm Haowen Wang, a master student at Department of Electrical Engineering, Stanford University majoring in Electrical Engineering.

I previously worked as a research intern at Microsoft, Beijing. During my undergraduate study, I'm honored to work with Prof. Mingsheng Long, Prof. Yue Gao, and Prof. Xibin Zhao at Tsinghua University. In addition, I am fortunate to be a research visitor at University of California, San Diego, working with Prof. Zhuowen Tu last summer.

My research interests lie in LLM , Computer Vision, AI4Science, and generative AI. My research goal is to help machines see and understand this world through scientific and interpretable ways.

Email  /  CV  /  Google Scholar  /  Github

Publications (* indicates equal contribution)

We investigate how closed-set generative data augmentation can improve image classification, providing empirical guidelines on the amount of synthetic data needed to match real-data augmentation across different datasets.

We presents the Universal PDE solver (Unisolver) capable of solving a wide scope of PDEs by leveraging a Transformer pre-trained on diverse data and conditioned on diverse PDEs.

We designed a physics-informed model to solve partial differential equations (PDEs), learning intrinsic physical states for better performance.

We proposed a hypergraph-guided disentangled spectrum transformer network for near-infrared facial expression recognition.

Selected Awards

Misc

The source code is stolen from Jon Barron. Thanks for his sharing! 🙏🏻