I am a current PhD student at MIT CSAIL working with Antonio Torralba and Phillip Isola on machine learning. My current focuses are geometric structures of learned representations, and enabling efficient, adaptive and general agents via such representations. I am broadly interested in representation learning, reinforcement learning, dataset distillation, and machine learning in general.
Before joining MIT, I worked at Facebook AI Research (now Meta AI) on PyTorch, and studied computer science and statistics at UC Berkeley, where I was fortunate to work with Stuart J. Russell, Ren Ng, and Alyosha Efros.
Click here for my CV.
Open Source Projects
- PyTorch core developer (2017 - 2019, initial team size <10). Data loading, CUDA/CPU kernels, autograd optimization, ML ops, Python binding, etc.
torchreparamdeveloper (2019 - 2020). One of the earliest PyTorch toolkit for re-parametrizing neural networks, e.g., for hyper-nets and meta-learning.
torchqmetdeveloper (2022 - now). PyTorch toolkit for SOTA quasimetric learning.
- CycleGAN and pix2pix in PyTorch maintainer (2018 - now). 18.9k stars.
See below for open source code for my researches.
Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings
[NeurIPS 2022 NeurReps Workshop] [Project Page] [arXiv] [PyTorch Package for Quasimetric Learning]
Tongzhou Wang, Phillip Isola
Learning to Synthesize a 4D RGBD Light Field from a Single Image
[ICCV 2017] [arXiv]
Pratul Srinivasan, Tongzhou Wang, Ashwin Sreelal, Ravi Ramamoorthi, Ren Ng