Chonghyuk (Andrew) Song

I'm a 2nd-year master's student at CMU's Robotics Institute, where I am extremely fortunate to be co-advised by Deva Ramanan and Jun-Yan Zhu. My research interests include 3D reconstruction of in-the-wild dynamic scenes from video, and its application to robotics.

Previously, I was at the Agency for Defense Development (ADD), where I developed autonomous mobile robots as part of my mandatory military service. I graduated from KAIST with a B.S. in Mechanical Engineering.

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A novel positional embedding module that yields unprecedented improvements in the accuracy of existing GNN-based rigid point cloud registration methods.

Replacing unrolled expectation maximization (EM) layers in neural networks with generalized EM layers based on the Newton-Rahpson method introduces highway connections, resulting in improved gradient flow during backpropagation.

A highly efficient design methodology that finds the optimal multi-mode, two-planetary-gear powertrain by leveraging the virtual lever, a modeling tool that eliminates the redundancy in the physical design space.

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