Presentation, SPIE Conference on Defense + Commercial Sensing 2022,
Presented at the SPIE Conference on Defense + Commercial Sensing on April 6, 2022. Presented our work on applying graph-based active learning to Synthetic Aperture Radar (SAR) data.
Talk, UMN Institute of Mathematics and its Applications (IMA) Data Science Seminar,
Presented work at University of Minnesota’s (UMN) IMA Data Science Seminar, per the invitation of Dr. Jeffrey Calder. Presented my recent work on active learning with application to Hyperspectral Imagery (HSI) and Synthetic Aperture Radar (SAR) data, including specific focus and discussion about exploration and exploitation in active learning.
Talk, 7th Annual Intelligence Community Academic Research Symposium 2021,
Presented work at the 7th Annual Intelligence Community Academic Research Symposium (ICARS) on September 29, 2021, in place of my advisor Dr. Bertozzi. Presented our recent work on active learning with application to Hyperspectral Imagery (HSI) and Synthetic Aperture Radar (SAR) data.
Talk, BYU Mathematics of Machine Learningm 2021,
Guest Lecturer for Dr. Jared Whitehead’s Mathematics of Machine Learning graduate level course at BYU. Presented my work on model change active learning for graph-based semi-supervised learning (SSL) as well as gave some advice on applying for Ph.D. programs in Applied Mathematics.
Poster, Naval Applications of Machine Learning (NAML) Conference 2021,
Presented poster at Naval Applications of Machine Learning (NAML) Conference on March 24, 2021. Presented my work on scalable and sample-efficient model change active learning for a family of graph-based semi-supervised learning models, with specific application to Hyperspectral Imagery (HSI).
Talk, SIAM CSE 2021,
SIAM CSE 2021, Minisymposium on Theory and Applications of Graph-Based Learning. Presented my work on model change active learning for graph-based semi-supervised learning (SSL), where we use the approximate change in the underlying SSL model as a measure of usefulness in the active learning process. This approximate change is efficiently done for a family of graph-based SSL models, using only a subset of the graph Laplacian’s eigenvalues and eigenvectors.
Talk, SIAM PDE 2019,
SIAM PDE 2019, Minisymposium on PDEs in Machine Learning. Presented results on consistency of semi-supervised regression in graph learning, from our paper.
Poster, BYU Spring Research Conference, 2017,
Won prize for best presentation of my session of the BYU Spring Research Conference. Presented research on using effective resistance for use in link prediction by viewing link prediction as a probabilistic problem wherein we view the current graph’s edge set as a realization of draws from an underlying probability distribution determined by a ground truth graph’s effective resistances.
Poster, BYU Spring Research Conference, 2016,
Won prize for best presentation of my session of the BYU Spring Research Conference. Presented research on estimating the latent number of clusters in directed networks for use in spectral clustering. The sequence of smallest eigenvalues of the associated graph Laplacian matrix that are real turned out to be a good estimator for the latent clustering structure.
Poster, Lawrence Livermore National Laboratories Student Poster Symposium 2015,
Presented summer testing and research from my internship at LLNL, sorry poster not available.