Computer Science and Engineering
"Dynamic Robust Sparse Modelling
Of High-Dimensional Data Streams For Online Monitoring"
Date: Wednesday, 13th Nov, 2019
Time: 3:00 pm
Location: Building 22, Room 130
Dr. Mohammad Nabhan
Systems Engineering Department
King Fahd University of Petroleum and Minerals
Current manufacturing and service
systems are capable of generating an overwhelmingly large amount of data in
real time throughout all stages of operation.
This is largely due to the incredible development in sensing technology
as well as the ubiquitous and abundance use of sensors. While the resulting plethora of information
likely hold key performance measurements and indicators of process efficiency
and capability, identifying and extracting these informative features is
extremely challenging at best with current benchmark techniques. This research focuses on addressing crucial
challenges that commonly arise when dealing with manufacturing and service
systems that generate data rich environments. stems. We develop a dimension
reduction method named “Robust Sparse Principal Component Analysis” (RS-PCA), that
is designed to robustly estimate a lower dimensional subspace by exploiting
sparse data structures.
Dr. Mohammad Nabhan is an assistant professor in the systems
engineering department at KFUPM. He received his PhD. degree (in 2019) and M.S.
degree in Industrial Engineering (in 2013) from Georgia Institute of
Technology. Before that, he obtained his B.S. degree in Industrial Engineering
(in 2011) from King Fahd University of Petroleum and Minerals. His research
interests include machine learning and data analytics for high-dimensional data
in different engineer and industrial applications, Image and profile data
modeling and monitoring for change detection and localization.
researchers and graduate students are invited to attend.
Department, College of Computer Sciences and Engineering
Telephone: +966 (13)
860 2988, Email: email@example.com, Website: www.kfupm.edu.sa/departments/se/
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Fahd University of Petroleum & Minerals