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.
I have spent time at Meta AI working with Yuandong Tian, Amy Zhang, and Simon S. Du. I also collaborate with Alyosha Efros and JunYan Zhu.
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.
torchreparam
developer (2019  2020). One of the earliest PyTorch toolkit for reparametrizing neural networks, e.g., for hypernets and metalearning.torchqmet
developer (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.
Selected Publications

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 
Denoised MDPs: Learning World Models Better Than The World
[ICML 2022] [Project Page] [arXiv] [code]
Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian 
On the Learning and Learnability of Quasimetrics
[ICLR 2022] [Project Page] [arXiv] [OpenReview] [code]
Tongzhou Wang, Phillip Isola 
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
[ICML 2020] [Project Page] [arXiv] [code]
Tongzhou Wang, Phillip Isola# bsz : batch size (number of positive pairs) # d : latent dim # x : Tensor, shape=[bsz, d] # latents for one side of positive pairs # y : Tensor, shape=[bsz, d] # latents for the other side of positive pairs def align_loss(x, y, alpha=2): return (x  y).norm(p=2, dim=1).pow(alpha).mean()
def uniform_loss(x, t=2): return torch.pdist(x, p=2).pow(2).mul(t).exp().mean().log()PyTorch implementation of the alignment and uniformity losses 
Dataset Distillation
[Project Page] [arXiv] [code]
Tongzhou Wang, JunYan Zhu, Antonio Torralba, Alexei A. Efros 
MetaLearning MCMC Proposals
[NeurIPS 2018] [PROBPROG 2018] [ICML 2017 AutoML Workshop Oral] [arXiv]
Tongzhou Wang, Yi Wu, David A. Moore, Stuart J. Russell 
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