Michael Matheny

Michael Matheny

PhD student at the University of Utah researching spatial scan statistics.

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

Posts

projects

pyscan

Implementations of a number of spatial scan statistic algorithms for different kinds of data sets.

pypartition

This is the code used for the paper Practical Low-Dimensional Halfspace Range Space Sampling.

FJLT

An implementation of the Fast Johnson Lindenstrauss Transform written in python. Uses the fast hadamard transform, numpy, and scipy libraries.

Moore

Does a combinatorial search to find Moore Graphs that satisfy the Hoffman–Singleton theorem.

publications

ExSciTecH: expanding volunteer computing to explore science, technology, and health

Published in IEEE 8th International Conference on E-Science, 2012

This paper presents ExSciTecH, an NSF-funded project deploying volunteer computing (VC) systems to Explore Science, Technology, and Health. ExSciTecH aims at radically transforming VC systems and the volunteer’s experience.

Recommended citation: M Matheny, Samuel Schlachter, LM Crouse, ET Kimmel, Trilce Estrada, Marcel Schumann, R Armen, G Zoppetti, M Taufer. ExSciTecH: expanding volunteer computing to explore science, technology, and health. In IEEE 8th International Conference on E-Science. 2012. https://ieeexplore.ieee.org/abstract/document/6404451

Performance impact of I/O on QMCPack simulations at the petascale and beyond

Published in IEEE 16th International Conference on Computational Science and Engineering, 2013

In this paper, we work to increase the granularity of the I/O in QMCPack simulations without increasing the I/O associated overhead or compromising the scalability of the simulations.

Recommended citation: M Matheny, S Herbein, M Wezowicz, J Krogel, J Logan, J Kim, S Klasky, M Taufer. Performance impact of I/O on QMCPack simulations at the petascale and beyond. In IEEE 16th International Conference on Computational Science and Engineering. 2013 https://ieeexplore.ieee.org/abstract/document/6755202

Using surrogate-based modeling to predict optimal I/O parameters of applications at the extreme scale

Published in 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS), 2014

We adopt and adapt an engineering method called surrogate-based modeling to efficiently search for the optimal I/O parameter values and accurately predict the associated I/O times at the extreme scale.

Recommended citation: M Matheny, S Herbein, N Podhorszki, S Klasky, M Taufer. Performance impact of I/O on QMCPack simulations at the petascale and beyond. In 20th IEEE International Conference on Parallel and Distributed Systems. 2014 https://ieeexplore.ieee.org/abstract/document/7097855

Scalable Spatial Scan Statistics through Sampling

Published in Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2016

What theoretical guarantees do we get when applying sampling to spatial scan statistics.

Recommended citation: Michael Matheny, Raghvendra Singh, Liang Zhang, Kaiqiang Wang, Jeff M. Phillips. Scalable Spatial Scan Statistics through Sampling. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2016 https://dl.acm.org/citation.cfm?id=2996939

Computing Approximate Statistical Discrepancy

Published in International Symposium on Algorithms and Computation, 2018

Methods for computing approximate statistical discrepancy for various range spaces and an almost matching lower and upper bound for rectangle scanning.

Recommended citation: Michael Matheny and Jeff M. Phillips. Computing Approximate Statistical Discrepancy. In 29th International Symposium on Algorithms and Computation (arXiv:1804.11307), 2018. https://arxiv.org/abs/1804.11287

The Kernel Scan Statistic

Published in In Submission, 2019

A new class of Scan Statistics based on Kernel functions are defined and then properties and methods of computing them are explored.

Recommended citation: Mingxuan Han, Michael Matheny, and Jeff M. Phillips. The Kernel Scan Statistic. In Submission(arxiv:1906.09381), 2019. https://arxiv.org/abs/1906.09381

talks

teaching