Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Bio
About me
Page not in menu
This is a page not in th emain menu
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Exploration and Exploitation in Active Learning
I provide a visualization of and some thoughts about exploration and exploitation in active learning.
preprints
publications
Pre-processing and classification of hyperspectral imagery via selective inpainting
Published in IEEE ICASSP, 2017
Download here
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations
Published in ICML Workshop on Real World Experiment Design and Active Learning, 2020
Posterior Consistency of Semi-Supervised Regression on Graphs
Published in IOP Inverse Problems, 2021
Download here
Model Change Active Learning in Graph-Based Semi-Supervised Learning
Published in Submitted to SIAM Journal on Mathematics of Data Science (SIMODS), 2021
Submitted to SIAM Journal on Mathematics of Data Science (SIMODS). arXiv
Download here
Efficient and Reliable Overlay Networks for Decentralized Federated Learning
Published in Accepted to SIAM Journal on Applied Mathematics (SIAP), 2021
Accepted to SIAM Journal on Applied Mathematics (SIAP). arXiv. Yifan Hua and Kevin Miller co-first authors.
Download here
Graph-based Active Learning for Semi-supervised Classification of SAR Data
Published in SPIE Conference on Defense and Commercial Sensing 2022, 2022
Paper soon to appear at SPIE Conference on Defense and Commercial Sensing occurring April 4-7, 2022.
talks
Cyclic Community Detection in Directed Networks
Published:
Presented summer testing and research from my internship at LLNL, sorry poster not available.
Spectral Clustering in Directed Networks
Published:
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.
A Probabilistic Perspective on Link Prediction via Effective Resistances
Published:
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.
Bayesian Posterior Consistency in Graph Based Semi-Supervised Learning
Published:
SIAM PDE 2019, Minisymposium on PDEs in Machine Learning. Presented results on consistency of semi-supervised regression in graph learning, from our paper.
Active Learning in Graph Based Semi-Supervised Learning
Published:
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.
Multiclass Active Learning for Graph-Based Semi-Supervised Learning
Published:
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).
Active Learning in Graph Based Semi-Supervised Learning
Published:
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.
Active Learning Methods on Graphs for Image, Video and Multispectral Datasets
Published:
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.
Scalable and Sample-Efficient Active Learning in Graph-Based Semi-Supervised Classification
Published:
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.
Graph-based Active Learning for Semi-supervised Classification of SAR Data
Published:
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.
teaching
Consulting
Machine Learning Consulting, University of California, Los Angeles, 2020
I’m open to help with any ideas or questions you have relating to machine learning applications.
Tutoring
Undergraduate tutoring, University of California, Los Angeles, 2020
Looking for help with your undergraduate courses at UCLA (or other schools/universities)? I’m happy to help.