INFORMS Open Forum

Reminder of IISE/QCRE Webinar Series: Tensor Data Analysis for Modeling Systems with High-Dimensional Heterogeneous Data

  • 1.  Reminder of IISE/QCRE Webinar Series: Tensor Data Analysis for Modeling Systems with High-Dimensional Heterogeneous Data

    Posted 24 days ago

    Dear Colleagues,


    This is a kind reminder that IISE/QCRE division would like to invite you to attend the webinar on Friday April 16, 2021, 2-3 p.m. Eastern time. If interested, please register through the link below:



    This event is free but advance registration is mandatory. After registering, you will receive a confirmation email containing information about joining the meeting.


    Title: Tensor data analysis for modeling systems with high-dimensional heterogeneous data
    2 - 3 PM EST, Friday, April 16, 2021

    Presenter: Dr. Mostafa Reisi, Assistant Professor, Department of Industrial and Systems Engineering, University of Florida


    Abstract: This talk discusses tensor data analysis approaches for modeling high-dimensional and heterogeneous data. First, we will present the problem of estimating a process output, measured by a scalar, curve, image, or structured point cloud by a set of heterogeneous process variables. A general multiple tensor-on-tensor regression (MTOT) approach is presented to create a unified modeling framework that effectively combines different data forms while exploiting the correlation structure within HD data points. Next, MTOT is extended to the cases where the data contains missing values. Finally, the talk will focus on tensor-on-scalar regression models and extend them to the robust models that can handle the presence of outliers.


    Bio: Dr. 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 in 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 at UF. 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).





    Mingyang Li, Ph.D., University of South Florida

    Yisha Xiang, Ph.D., Texas Tech University

    Mingyang Li
    Assistant Professor
    University of South Florida
    Tampa FL