Kernel-based methods involve fundamental approximation theory (mathematics), stochastic analysis (statistics) and various applications from data science and machine learning. They are widely used in constructing mesh-free or high order methods for solving partial differential equations, high dimensional approximation, uncertainty quantification, density function estimation and machine (deep/kernel) learning. Recent advance in numerical analysis, statistical computing and learning theory brings fruitful results on kernel-based methods. The conference aims to provide a platform for experts and researchers on kernel methods with various backgrounds to work together in order to break research boundaries by exchanging ideas from various fields. It will provide a forum to announce research advancement, to discuss emerging research trends, to identify new research directions to promote applications of kernel methods on various fields and to deepen or stimulate fundamental research.

Themes of the conference themes include but not limited to
•Approximation theory, in particular, high dimensional approximation
•Uncertainty qualification, kernel-based meshless methods
•Kernel based learning theory, deep learning
•Kernel based methods for imaging processing, data science
•And other applications
Conference Chairs: 
•Prof. Gemai Chen, Xi’an Jiaotong-Liverpool University
•Prof .Zongmin Wu, Fudan University
•Prof. Yuesheng Xu, Old Dominion University
•Prof. Dingxuan Zhou, City University of Hong Kong
https://www.xjtlu.edu.cn/en/conference/ickbm/chairs-and-committee
Confirmed Invited Speakers 
https://www.xjtlu.edu.cn/en/conference/ickbm/invited-speakers
Registration 
https://www.xjtlu.edu.cn/en/conference/ickbm/registration
Talk title and abstract should be submitted to ickbm@xjtlu.edu.cn by August 25th, 2019
Notification: soon after registration. 
There are limited contributed talk slots left, first come first served.
Contact: ickbm@xjtlu.edu.cn
Conference website: https://www.xjtlu.edu.cn/en/conference/ickbm/