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Implementations of a number of spatial scan statistic algorithms for different kinds of data sets.
This is the code used for the paper Practical Low-Dimensional Halfspace Range Space Sampling.
An implementation of the Fast Johnson Lindenstrauss Transform written in python. Uses the fast hadamard transform, numpy, and scipy libraries.
Does a combinatorial search to find Moore Graphs that satisfy the Hoffman–Singleton theorem.
Computes clusters of subreddits using various metrics and various clustering schemes
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
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
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
Published in European Symposium on Algorithms Track b, 2018
A fast and potentially practical method for computing close to optimal $\varepsilon$-samples for halfspaces.
Recommended citation: Michael Matheny and Jeff M. Phillips. Practical low-dimensional halfspace range space sampling. In European Symposium on Algorithms (arXiv:1804.11307), 2018. https://arxiv.org/abs/1804.11307
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
Published in In Submission, 2019
Methods and models for computing approximate statistical discrepancy over trajectory sets.
Recommended citation: Michael Matheny, Dong Xie, and Jeff M. Phillips. Scalable Spatial Scan Statistics for Trajectories. In Submission(arxiv:1906.01693), 2019. https://arxiv.org/abs/1906.01693
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
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field. files/ESA_Slides.pdf