Mengxi Wu 武梦溪

I am a Ph.D. student in the Department of Computer Science at the University of Southern California, advised by Prof. Mohammad Rostami. I received my Master of Science in Computer Science from New York University, where I was advised by Prof. Yi-Jen Chiang, Prof. Christopher Musco, and Prof. Yi Fang. Before that, I was an undergraduate student in EECS at the University of Michigan, Ann Arbor.

Research Highlights

My research focuses on machine learning in data-scarce environments with theoretical analysis. I am particularly interested in transfer learning, which helps bridge the gap between natural and artificial intelligence. Specifically, my work centers on domain adaptation, continual learning, zero-shot learning, and few-shot learning. Previously, I worked on adversarial machine learning for 3D point cloud processing and streaming algorithms for time-varying volume data.

Publications and Manuscripts (By Years / Selected)

Curvature Diversity-Driven Nuclear-Norm Wasserstein Domain Alignment for Point Cloud

Mengxi Wu, Hao Huang, Yi Fang, Mohammad Rostami

arXiv, 2024

[Preprint]

Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data

Mengxi Wu, Mohammad Rostami

Transactions on Machine Learning Research, 2024

[Paper] [Code]

Streaming Approach to In Situ Selection of Key Time Steps for Time-Varying Volume Data

Mengxi Wu, Yi-Jen Chiang, Christopher Musco

Eurographics/IEEE Conference on Visualization, 2022

[Paper] [Code]

3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation

Mengxi Wu, Hao Huang, Yi Fang

International Conference on Pattern Recognition, 2022

[Paper] [Code]

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