Streaming Kernel PCA with applications to Kernel Analog Forecasting
Friday, April 9, 2021
2PM – 3PM
Kernel Principal Component Analysis (KPCA) plays a critical role in many modern pipelines for analyzing physical systems. However, naively computing kernel matrices and their subsequent principal components poses drastic issues in terms of scalability. In this presentation, we will discuss the benefits of streaming kernel principal component analysis––a field of research that adds critical flexibility to one of the most common tools in optimization. We will specifically demonstrate the considerable improvements that can be achieved by applying streaming KPCA to kernel analog forecasting (KAF) for dynamical systems.
Amelia Henriksen is a PhD candidate under Dr. Rachel Ward in the Oden Institute for Computational Engineering and Sciences. She received a Master of Science degree in Computational Science, Engineering and Mathematics from the University of Texas in 2019 and a Bachelor of Science degree in Applied and Computational Mathematics from Brigham Young University in 2015. Her dissertation work has focused primarily on improving theory and applications for streaming dimensionality reduction. In 2019, Amelia was a finalist in UT Austin’s three-minute thesis competition. She currently lives in Austin with her husband and son, where she enjoys volunteering with her local community and eating the spiciest queso she can get her hands on.
(The CSEM Student Forum is a seminar series given by current CSEM graduate students to their peers. The aim of the forum is to expose students to each other's research, encourage collaboration, and provide opportunities to practice presentation skills. First- and second-year CSEM students receive seminar credit for attending.)