Recordings of Invited Talks

Prof. Annie Qu                  De-Confounding causal inference using latent multiplemediator pathways

Prof. Huang Jian               Conditional stochastic interpolation for generative learning

Prof. Peter Bühlman         Causality-inspired Statistical Machine Learning

Prof. Aad van der Vaart    Gaussian processes in Bayesian statistics: review and recent results

Prof. Hongyu Zhao           Beyond global correlations


Recordings of Young Scholars Talks

Dr. CHEN Guanhua          Efficient transfer learning with pretrained models

Dr. WEI Hongxin              Natural robusness of machine learning in the open world

Dr. ZHANG Haoran          Identifiability and consistent estimation for Gaussian chain graph models

Please note that above recordings are for the purpose of academic exchange only and any unauthorized reproduction or distribution of these copyrighted work is illegal.


Time: December 1-3, 2023

Venue: Southern University of Science and Technology

Organizer: 

Dept. of Statistics and Data Science, Southern University of Science and Technology

Co-organizers: 

National Center for Applied Mathematics Shenzhen,

SUSTech International Center for Mathematics,

Chinese Associate for Applied Statistics-Transition of the Professional Committee on Multivariate Analysis Applications


About the Conference

The field of Statistics and Data Science play an increasingly important role in scientific research and the development of modern society. In order to strengthen the communication and cooperation in the related fields, as well as to explore the cutting-edge technologies and trends, the 4th International Frontier Forum on Statistics and Data Science is scheduled to be held on December 1-3, 2023 in Shenzhen, China.

The conference brings together world-known academicians, experts, and scholars in related fields to give talks on their latest research, it aims to provide a high-level platform for scholars in the field of Statistics and Data Science from all around the world to facilitate deeper communication and cooperation.


Invited Speakers

 

              Peter Buhlmann, Professor                      Huang Jian, Chair Professor                            Xihong Lin, Professor 

                        ETH Zürich                              Hong Kong Polytechnic University                                Harvard

 

             MA Zhiming, Academician                 Annie Qu, Chancellor’s Professor                  Kavita Ramanan, Professor     

    AMSS Chinese Academy of Sciences            University of California, Irvine                             Brown University                 

          Aad Van der Vaart, Professor                              Lan Wang, Professor                             XU Zongben, Academician    

                 Leiden University                                         University of Miami                              Xi’An Jiaotong University

 

 

  Hongyu Zhao, Ira V. Hiscock Professor 

                   Yale University


Organizing Committee 

(Department of Statistics and Data Science, SUSTech)


Chair 

SHAO Qi-Man   

Members

CHEN Guanhua

CHEN Xin         

HU Yanqing      

JIANG Xuejun   

JIAO Xiyun      

JING Bingyi     

LI Zeng          

MA Yifang      

SHI Jianqing     

TIAN Guoliang 

WANG Chao   

WEI Hongxin   

XU Cong           

YANG Lili         

YANG Peng

ZHANG Haoran

ZHANG Zhuosong


Secretariat

LI Lan

CHEN Minghui

DONG Ru

QIU Tong

QIU Yafang

SONG Anqi

TENG Yueran

WU Juan

ZHOU Lin

ZHOU Zhaodi

Contact Person

LI Lan at lil36@sustech.edu.cn

Telephone:0755-88015665


Agenda


Registration

Please Register online at https://www.wjx.top/vm/Q6fB8ab.aspx


Registration fees

Regular Rate: 1800 RMB

Early Bird discount until 31 Oct: 1500 RMB

Regular Student discount: 1000 RMB

Early Bird Student discount until 31 Oct: 800 RMB


Please note that the registration fees will be collected by the Chinese Associate for Applied Statistics-Transition of the Professional Committee on Multivariate Analysis Applications.