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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Distinctiveness Oriented Positional Equilibrium for Point Cloud Registration
Taewon Min, Chonghyuk Song, Eunseok Kim, Inwook Shim
ICCV, 2021

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

Improving Gradient Flow with Unrolled Highway Expectation Maximization
Chonghyuk Song, Eunseok Kim, Inwook Shim
AAAI, 2021

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.

Efficient Design Space Exploration of Multi-Mode, Two-Planetary-Gear, Power-Split Hybrid Electric Powertrains via Virtual Levers
Chonghyuk Song, Jaeho Hwang, Dongsuk Kum

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.

Credits to Jon Barron for this website's template.