Prof. David Yuen [University of Minnesota, USA]

Title: Potential Use of Big Data to learn about new physical properties | 利用大数据了解新物理特性

Time: 10:00 - 11:00 AM, Thursday, December 29, 2016

Place: Conference room 410, HPSTAR (Shanghai)

Host: Dr. Ho-Kwang Mao

Abstract

In this talk we will point out the use of big data techniques such as artificial intelligence, machine learning and deep learning to make predictions about events or physical properties. We discuss about the technique of deep learning, which can be used to new properties about a physical system. The example we will use comes from mantle convection and we will use the iron-spin transition as an example. Later on we will discuss about using existing data-base to extract physical properties of new materials, without doing the experiments.

Biography of the Speaker:





Prof. David Yuen graduated in 1969 from Caltech in chemistry. He then received a master in physical chemistry from UC Berkeley and a PHD in geophysics and geophysics in 1978 with a minor in applied mathematics from UCLA Following a two year NATO and NSF postdoctoral fellowships, he went to Arizona State University as assistant professor in geology. In 1985 he moved two times, first to Dept. of Geological Sciences at university of Colorado at Boulder in January as associate professor, then in September to University of Minnesota, as associate professor in geophysics and Fellow of Minnesota Supercomputer Institute. Since 2012, he has been spending 3 to 4 months each year at the School of Environmental Studies at 中国地质大学武汉 as a 长江 professor. He works mainly in computational geodynamics with interests also spanning to visualization and now Big Data大数据. He has published around 550 papers and edited six books in geophysics and high-performance computing. His h-index is 61 according to Thomas-Reuters.