Mostafa Reisi gave a lecture titled “Modeling and Monitoring of Complex Systems with Heterogeneous and Network Data” at AMSS on 19, December 2020.

In this talk, three topics related to systems modeling and monitoring will be presented. First, a sequential approach for sampling high-accuracy (HA) data based on the information obtained from low-accuracy (LA) data is presented. Next, a dynamic network monitoring framework for detecting abrupt changes in a sequence of networks will be presented. The last topic focuses on the problem of estimating a process output, measured by a scalar, curve, image, or structured point cloud by a set of heterogeneous process variables. To create a unified modeling framework that effectively combines different forms of data points, while exploiting the correlation structure within an HD data point, a general multiple tensor-on-tensor regression (MTOT) approach will be discussed.

Mostafa Reisi received his master’s degree in computational science and engineering and his Ph.D. degree in industrial and systems engineering from Georgia Institute of Technology, and the M.Sc. degrees in transportation engineering and applied mathematics from the Southern Illinois University Edwardsville. He is currently an Assistant Professor with the Department of Industrial and Systems Engineering at the University of Florida. His research interests focus on developing efficient methodologies and algorithms for modeling and monitoring systems with high-dimensional or network data. Dr. Reisi is also interested in adaptive sampling and multi-accuracy data fusion. He is the co-director of Data Informatics for Systems Improvement and Design (DISIDE) lab. Dr. Reisi is a member of the Institute for Operations Research and the Management Sciences (INFORMS) and the Institute of Industrial and Systems Engineers (IISE).