Aim & Scope:

We are in an era that experiences the explosion of data. The amount of data and available computing power has resulted in many exciting advancements of machine learning algorithms and applications. However, there are still many essential questions left unanswered. How do we harvest a large amount of available data to extract knowledge and insights? Particularly the cases in which one can generate the data by scientific computing algorithms that can simulate to great accuracy some systems that abide by known physical laws? How can mathematical theory and scientific computing insights help explain or design new ways to uncover information from data?

In this workshop, we have speakers from the fields of applied mathematics, scientific computing, and machine learning, presenting their latest research. The objective is two-fold: discover how state-of-the-art machine learning framework and algorithms can enable the advancement of scientific computing, and how scientific computing techniques can improve and generalize machine learning models and computation.

Invited Speakers:

Chih-Wei Chen: National Sun Yat-sen University

Ray-Bing Chen: National Cheng Kung University

Albert Chern: Technische Universität Berlin

Thomas Hou: California Institute of Technology

Yuh-Jye Lee: National Chiao Tung University

Guan-Ju Peng: National Chung Hsing University

Pei-Chiang Shao: Soochow University

Richard Tsai: The University of Texas at Austin

Program Dowoload (please click here)

Abstract & Title Download (please click here)