Dear Colleagues,
You are invited to attend Quality, Statistics and Reliability (QSR) Section Webinar entitled "Integrating Deep Learning with Statistical Methods for Complex Data Analysis", which will be given by Prof. Heeyoung Kim (KAIST) on Tue. Apr. 29th between 7PM and 8PM CST.
We look forward to your participation in this informative event. Please feel free to forward this invitation to other interested colleagues or students. The Webinar Registration link is as follows:
Zoom Registration: https://uiowa.zoom.us/meeting/register/__3Oh9cmT_6VbdhjZNPKDw
Abstract: Deep learning excels at uncovering patterns in complex data but often lacks interpretability and uncertainty quantification. In applications where these are as important as predictive accuracy, integrating deep learning with statistical models provides an effective solution. This talk presents two studies that demonstrate this integration. The first, on deep spatio-temporal forecasting, integrates recurrent neural networks with latent factor models to flexibly capture complex temporal patterns. The second, on deep emulation, integrates deep Gaussian processes with autoregressive models to capture nonstationary relationships between multiple sources of multifidelity data. These approaches highlight the potential of integrating deep learning with statistical models to improve both interpretability and predictive accuracy in complex data analysis.
Biographical Sketch: Heeyoung Kim is a KAIST Endowed Chair Professor and an Associate Professor in the Department of Industrial and Systems Engineering at KAIST. She received her BS and MS degrees in Industrial Engineering from KAIST, as well as an MS in Statistics and a PhD in Industrial Engineering from the Georgia Institute of Technology. Previously, she was a Senior Member of Technical Staff at AT&T Laboratories. Her research focuses on applied statistics, machine learning, and quality engineering. She currently serves as an Associate Editor for Technometrics and IISE Transactions, and previously served as an Associate Editor for IEEE Transactions on Automation Science and Engineering.
For more information about this webinar, please feel free to contact the event organizers:
Thank you.
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Chao Wang
Assistant Professor
University of Iowa
Iowa City IA
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