Mark your calendars for the next ASA SPES Webinar on April 29!
The Executive Committee of the American Statistical Association (ASA) Section on Physical Sciences and Engineering (SPES) is proud to announce that Professor Kamran Paynabar (Georgia Institute of Technology) will give the next presentation in our webinar series. Additional information regarding his presentation and the registration for the webinar follow below.
Webinar and Registration Details
Date & Time: April 29, 2025, at 12:00 PM Eastern (USA & Canada)
Registration: Register in advance here: https://queensu.zoom.us/meeting/register/YI3Fi4aPQcegHzWbtnW0UQ
Upon registering, you will receive a confirmation e-mail with details on how to join the webinar.
Presentation Information
Title: Low-Dimensional Learning for System Monitoring and Control Using High-Dimensional Data Streams
Speaker: Professor Kamran Paynabar (Georgia Institute of Technology)
Time: 12:00 PM – 1:00 PM Eastern (USA & Canada), Tuesday, April 29
Abstract: Industry 4.0, along with advancements in sensing and communication, has enabled the large-scale collection of streaming data, creating unique opportunities for system modeling and monitoring. However, the complex nature of these datasets presents significant analytical challenges. Common characteristics include high variety, high dimensionality, high velocity, and intricate spatial and temporal structures. In this talk, I will present our research on developing efficient methods for system monitoring and control using high-dimensional data streams. The proposed frameworks leverage low-dimensional representations of high-dimensional data and can accommodate various data types, including profiles, images, videos, point clouds, and manifolds. These methods have been validated across multiple application domains, such as additive manufacturing, automotive, forging and rolling, and environmental monitoring.
Bio: Dr. Kamran Paynabar is the Fouts Family Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. His research focuses on methodological and applied aspects of statistical machine learning for engineering applications, supported by NSF, NIH, DOE, and industry leaders such as Samsung, Ford, and Boeing. He has received best paper awards from INFORMS, IISE, ASA, and POMS, along with multiple teaching honors. He is Editor-Elect of Technometrics and former Department Editor for IISE Transactions. A Fellow of ASQ and an Elected ISI Member, he also co-founded ProcessMiner, an AI-driven manufacturing analytics company.
We look forward to your participation!
SPES Executive Committee