Dear All,

Just a friendly reminder for the Statistics and Data Science (SDS), jointly with the CAM group, talk (virtual) today [Tuesday October 4] at 2:30PM. 

Speaker is Dr. Boyuan Chen, Assistant Professor of Mechanical Engineering and Materials Science at Duke University.  

Boyuan graduated from Columbia University, and his work has received several accolades at the interface of materials, robotics, and AI. In fact, his latest work (with Qiang Du among others) was featured as a cover article in Nature Computational Science.

Today he will speak on "Automated Discovery of Fundamental Variables Hidden in Experimental Data”-abstract is below.

Thank you and I am looking forward to seeing you in about an hour or so. 

Cheers,
Vasileios 

Zoom LINK: https://tennessee.zoom.us/j/99434113279 


 
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Vasileios Maroulas, PhD
https://sites.google.com/utk.edu/mrg

Professor @ University of Tennessee
Department of Mathematics---Department of Business Analytics and Statistics---Bredesen Center, Data Science and Engineering

Director of Data Science and AI
National Institute for Mathematical and Biological Synthesis 

Editor-In-Chief @ Foundations of Data Science
https://aimsciences.org/journal/A0000-0002

Associate Editor @ Statistics and Computing, Springer Nature
https://www.springer.com/journal/11222  

> Speaker: Boyuan Chen
> Affiliation: Duke University
> 
> Title: : Automated Discovery of Fundamental Variables Hidden in Experimental Data
> 
> 
> Abstract: All physical laws are described as mathematical relationships between state variables. These variables give a complete and non-redundant description of the relevant system. However, despite the prevalence of computing power and artificial intelligence, the process of identifying the hidden state variables themselves has resisted automation. Most data-driven methods for modelling physical phenomena still rely on the assumption that the relevant state variables are already known. A longstanding question is whether it is possible to identify state variables from only high-dimensional observational data. In this talk, I will present our recent work on determining how many state variables an observed system is likely to have, and what these variables might be. We demonstrate the effectiveness of this approach using video recordings of a variety of physical dynamical systems, ranging from elastic double pendulums to fire flames. Without any prior knowledge of the underlying physics, our algorithm discovers the intrinsic dimension of the observed dynamics and identifies candidate sets of state variables. In the end, I will also discuss how such efforts can be useful for robotics and computer vision applications.
> 
> Dial-In Information
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> 
> https://tennessee.zoom.us/j/99434113279
> 
> 
> 
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