2023 The Mathematics and Computer Science Prize Laureate
For fundamental contributions to artificial intelligence by introducing deep residual learning.
Deep neural networks have driven the revolution of artificial intelligence and its rapid development. Particularly, neural networks with increasing depths have led to groundbreaking progress in a wide range of artificial intelligence applications. The awardees as a team introduced deep residual learning as a framework for building deep neural networks. Deep residual learning has allowed neural networks to utilize unprecedented depths and unlock capabilities that previously deemed unachievable. Deep residual learning has been extensively adopted across many applications, paving the way for numerous breakthroughs such as AlphaGo, AlphaFold, and ChatGPT.
The research was undertaken by the awardees at Microsoft Research Asia in Beijing between 2012 and 2016.
Kaiming He: BS (2007) Tsinghua University, PhD (2011) Chinese University of Hong Kong.
Jian Sun: BS (1997) and PhD (2003), Xi'an Jiaotong University.
Shaoqing Ren:BS (2011) University of Science and Technology of China, PhD (2016) University of Science and Technology of China and Microsoft Research Asia.
Xiangyu Zhang:BS (2012) Xi'an Jiaotong University, and PhD (2017) Xi'an Jiaotong University and Microsoft Research Asia.