Topological Data Analysis Seminar
Thursday, Mar 21, 2019, MC 105B, 3:30-4:30 - Michael Lesnick (SUNY/Albany), Computational aspects of 2-parameter persistent homology.
Abstract: In topological data analysis, one associates to the data a filtered topological space, whose structure we then examine using persistent homology. However, in many settings, a single filtered space is not a rich enough invariant to encode the interesting structure of the data. This motivates the study of multi-parameter persistence, which associates to the data a topological space simultaneously equipped with two or more filtrations. The homological invariants of these “multi-filtered spaces,” called persistence modules, are much richer than their 1-D counterparts, but also far more complicated. As such, adapting the usual 1-parameter persistent homology methodology for data analysis to the multi-parameter setting requires new ideas. For the past few years, I have been working with several collaborators on RIVET, a practical software tool for the visualization and analysis of 2-parameter persistent homology. One key new feature of RIVET is a fast algorithm for computing minimal presentations in the 2-parameter persistence setting; this is joint work with Matthew Wright. Perhaps surprisingly, the computational cost of the algorithm turns out to be similar to that of the standard algorithm for computing 1-parameter persistent homology. In this talk, I’ll introduce 2-parameter persistent homology, RIVET, and (time permitting) our algorithm for computing minimal presentations.
Data Security Seminar
Thursday, Mar 22, 2018 - Aleksander Essex (Electrical & Computer Engineering), Computing threshold functions under encryption
CBC news article about Aleks' work: http://www.cbc.ca/news/canada/london/london-ontario-online-voting-1.4598787
Thursday, Apr 5, 2018 - Chris Hall (Mathematics), An Overview of Blockchains
Spring 2018 Schedule
Zhou Lan: Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing
Gelfand, Alan E., Athanasios Kottas, and Steven N. MacEachern. "Bayesian nonparametric spatial modeling with Dirichlet process mixing." Journal of the American Statistical Association 100.471 (2005): 1021-1035.
Yawen Guan: A Multiresolution Gaussian Process Model
Nychka, Douglas, et al. "A multiresolution Gaussian process model for the analysis of large spatial datasets." Journal of Computational and Graphical Statistics 24.2 (2015): 579-599.
Arnab Hazra: Nonparametric Bayesian models for a spatial covariance
Reich, Brian J., and Montserrat Fuentes. "Nonparametric Bayesian models for a spatial covariance." Statistical methodology 9.1 (2012): 265-274.
Indranil Sahoo: Characterizing spatial processes on Spheres
Hitczenko, Marcin, and Michael L. Stein. "Some theory for anisotropic processes on the sphere." Statistical Methodology 9.1 (2012): 211-227.
Whitney Huang: Spatial Point Process
Gelfand, Alan E., et al., eds. Handbook of spatial statistics. CRC press, 2010.
Fall 2017 Schedule
Whitney Huang: Introduction to Spatial Statistics
Charpters 2-4 Handbook of Spatial Statistics
Maggie Johnson: Hierarchical Modeling with Spatial Data
Charpters 7 Handbook of Spatial Statistics
Yuan Tian: Introduction to Extreme Value Analysis
Charpters 3-4 An introduction to statistical modeling of extreme values
Zhou Lan: Low-Rank Representations for Spatial Processes
Charpters 8 Handbook of Spatial Statistics
Alexandra Larsen: Continuous Parameter Spatio-Temporal Processes
Charpters 23 Handbook of Spatial Statistics
Please visit the Westen Cryptography Research Group website for more information: https://sites.google.com/view/westerncrypto
Please visit the Homotopy Type Theory Electonic Seminar Talks website for more information: https://www.uwo.ca/math/faculty/kapulkin/seminars/hottest.html