Zhanxin Wu

I am a second-year master student majoring in Computer Science at National University of Singapore (NUS) advised by Prof David Hsu. I work at the intersection of robotics and machine learning, with the goal of enabling robots to work intellectually and seamlessly in the real world.

Previously, I worked as an Undergraduate Research Assistant with Prof Junhua Zhao in the Lab of Energy Internet. After that, I was a Robotics Institute Summer Scholar (RISS) at Carnegie Mellon University advised by Prof Jean Oh. Currently, I am working on open scene graphs for open-world object goal navigation at the NUS Adacomp. I received my B.E. in Computer Science and Engineering from the Chinese University of Hongkong, Shenzhen (CUHKSZ) in 2022.

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This work examines, experimentally and theoretically, one representation that enables visual navigation policies solely trained in the Habitat simulator to generalize to real-world scenes, both indoor and outdoors.

A framework that enables an agent to put misplaced objects back in place with partial map information by exploiting commonsense knowledge in large language models (LLMs).

A lightweight framework for semantic segmentation to recover information from uninformative pixels and improve 3D semantic understanding of scenes.

Formulated corporate carbon footprint estimation problem and proposed the first Methodology to estimate the direct and indirect carbon emissions of factories in real time

A temporal convolutional neural network with attention mechanism based method is proposed for industrial non-intrusive load monitoring.

Developed a deep end-to-end super resolution perception, recovered nearly 89% of high-frequency information lost in low-frequency information for equipment load identification at 1/10th of the original data volume, communication, and storage requirements.

Developed a self-driving agent in the Duckietown simulation with classical planning, computer vision, and imitation learning techniques. Placed among the top-scored project in the module.

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