Chonghyuk (Andrew) Song

I'm a 1st-year Ph.D. student at MIT's Computer Science and Artificial Intelligence Laboratory, where I am extremely fortunate to be advised by Vincent Sitzmann.

Prevously, I completed my M.S. degree at CMU's Robotics Institute, where I had the distinct pleasure of being co-advised by Deva Ramanan and Jun-Yan Zhu. I graduated from KAIST with a B.S. in Mechanical Engineering. I also interned at Snap.

My research interests lie at the intersection of machine learning and computer vision. I am especially interested in learning structure, such as 3D scenes and dynamics, from videos.

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Given a RGBD video containing deformable actors (top row), Total-Recon reconstructs the scene as a compositional 4D neural field, enabling extreme view synthesis from embodied views and bird's-eye views (bottom row).

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.

Credits to Jon Barron for this website's template.