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

All Publications

IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL

Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam

IJCAI - January 5, 2021

Asynchronous Gradient-Push

Mahmoud Assran, Michael Rabbat

IEEE Transactions on Automatic Control - January 1, 2021

Asynchronous Distributed Key Generation for ComputationallySecure Randomness, Consensus, and Threshold Signatures

Lefteris Kokoris Kogias, Dahlia Malkhi, Alexander Spiegelman

CCS - November 3, 2020

FastPay: High-Performance Byzantine Fault Tolerant Settlement

Mathieu Baudet, George Danezis, Alberto Sonnino

AFT - November 1, 2020

Weak-Attention Suppression For Transformer Based Speech Recognition

Yangyang Shi, Yongqiang Wang, Chunyang Wu, Christian Fuegen, Frank Zhang, Duc Le, Ching-Feng Yeh, Michael L. Seltzer

Interspeech - October 26, 2020

Winkle: Foiling Long-Range Attacks in Proof-of-Stake Systems

Sarah Azouvi, George Danezis, Valeria Nikolaenko

AFT - October 23, 2020

Taming the many EdDSAs

Kostas Chalkias, François Garillot, Valeria Nikolaenko

SSR - October 22, 2020

The Move Prover

Emma Zhong, Kevin Cheang, Shaz Qadeer, Wolfgang Grieskamp, Sam Blackshear, Junkil Park, Yoni Zohar, Clark Barrett, David Dill

CAV - October 21, 2020

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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