Date/Time
Date(s) - 01/03/2023
3:30 pm - 5:00 pm

Speaker:  KamranPaynabar, Georgia Tech

Date: WednesdayMarch 1, 3:30-5:00pm 

Title: Low Dimensional Learning from High Dimensional Data for System Modeling and Improvement

Abstract:  Industry4.0 has facilitated smart and large-scale communications through cyber-physicalsystems (CPS) and the Internet of Things. This generates data-rich environmentsand provides distinctive opportunities for system modeling and performance improvement.However, the complex characteristics of data pose significant analyticalchallenges yet to be addressed. Some common characteristics of these datasetsinclude high variety, high dimensionality, high velocity, and intricate spatialand temporal structure. In this talk, I present my research group’s efforts indeveloping efficient algorithms for learning the low-dimensional structure ofcomplex datasets to address some of the challenges in systems modeling,monitoring, control, prognostics, and optimization. The proposed methods havebeen validated in the variety of application domains including silicon wafermanufacturing, additive manufacturing, automotive, and microstructure analysisof stressed materials, which will also be discussed in this talk. 

Location: KTHB107 & Zoom 

Zoom Meeting Info:

  MeetingID: 993 2631 5722

  Passcode:913408

  https://mcmaster.zoom.us/j/99326315722?pwd=YVpYVW8yUHpyaU5RTmdjNTI4WGlPUT09