Research Areas

Giving people the power to share and connect requires constant innovation

At Facebook, research permeates everything we do. We believe the most interesting research questions are derived from real world problems. Working on cutting edge research with a practical focus, we push product boundaries every day. At the same time, we publish papers, give talks, and collaborate broadly with the academic community.

We solve real-world problems that impact billions of people in areas such as:

Latest Publications

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POPL - January 22, 2022

Profile Inference Revisited

Wenlei He, Julián Mestre, Sergey Pupyrev, Lei Wang, Hongtao Yu

POPL - January 16, 2022

Concurrent Incorrectness Separation Logic

Azalea Raad, Josh Berdine, Derek Dreyer, Peter O'Hearn

IEEE Spoken Language Technology Workshop (SLT) - January 9, 2022

Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR

Xiaohui Zhang, Frank Zhang, Chunxi Liu, Kjell Schubert, Julian Chan, Pradyot Prakash, Jun Liu, Ching-Feng Yeh, Fuchun Peng, Yatharth Saraf, Geoffrey Zweig

IEEE Transactions on Haptics (ToH) - January 1, 2022

Data-driven sparse skin stimulation can convey social touch information to humans

Mike Salvato, Sophia R. Williams, Cara M. Nunez, Xin Zhu, Ali Israr, Frances Lau, Keith Klumb, Freddy Abnousi, Allison M. Okamura, Heather Culbertson

ACM Transactions on Storage (TOS) - December 16, 2021

RocksDB: Evolution of Development Priorities in a Key-value Store Serving Large-scale Applications

Siying Dong, Andrew Kryczka, Yanqin Jin, Michael Stumm

SIGGRAPH ASIA - December 14, 2021

Modeling Clothing as a Separate Layer for an Animatable Human Avatar

Donglai Xiang, Fabián Prada, Timur Bagautdinov, Weipeng Xu, Yuan Dong, He Wen, Jessica Hodgins, Chenglei Wu

ICLR - December 13, 2021

DINO: A Conditional Energy-based GAN for Domain Translation

Konstantinos Vougioukas, Stavros Petridis, Maja Pantic

IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) - December 13, 2021

Kaizen: Continuously Improving Teacher Using Exponential Moving Average For Semi-supervised Speech Recognition

Vimal Manohar, Tatiana Likhomanenko, Qiantong Xu, Wei-Ning Hsu, Ronan Collobert, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed

Downloads & Projects

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PyTorch is a Python package that provides two high-level features: tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on a tape-based autograd system.

Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body.

Torch is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

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