Publications

---Current Writing---

(JLM and Jose Perea) "Geometric Data Analysis Across Scales via Laplacian Eigenvector Cascading" Submitted to Symposium on Discrete Algorithms 2019 ("Soda19").

(JLM and Jose Perea) "Nerve Learning: Semisupervised, Inductive Learning via Dataspace Geometry." Writing in progress. Plan to submit to Journal of Machine Learning.

(JLM with Greg Dreifus, S. Giles, J. Alling, M. Patel, and R. M. Foster.) "Descriptive Path Trajectories From Limited Data in Additive Manufacturing" In collaboration with Greg Dreifus and Oak Ridge National Lab. The other non-primary authors listed are undergraduates that we worked with. It was fun to explain all my algorithm ideas to them!

---Finished Publications---

Mike, Joshua Lee, and Vasileios Maroulas. "Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams." arXiv preprint arXiv:1803.02739 (2018).

Marchese, Andrew, Vasileios Maroulas, and Josh Mike. "Κ-means clustering on the space of persistence diagrams." Wavelets and Sparsity XVII. Vol. 10394. International Society for Optics and Photonics, 2017.

Joshua Mike, Colin D. Sumrall, Vasileios Maroulas, & Fernando Schwartz. Nonlandmark classification in paleobiology: computational geometry as a tool for species discrimination. Paleobiology, 1-11, 2016. (primary author) preprint

"Combinatorial Hodge Theory for Equitable Kidney Paired Donation" (JM with Vasileios Maroulas) Preprint