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This talk gives an overview of stochastic modeling and analysis of a large 3-dimensional array of geospatial porosity data. The analysis is based on empirical methods for covariance estimation on horizontal cross sections of the data using singular value decomposition (SVD) for principal component analysis (PCA) together with a kernel (KPCA) method yielding dimension reduction. The results can be used to produce simulated data with characteristics mimicking those of the original porosity observations.
In this talk I present a framework to address participation in the mathematics classroom. This framework represents my journey through mathematics education research from a largely cognitive approach (with a focus on understanding and beliefs) to a sociocultural perspective (with a focus on context and valorization of knowledge). Some of the questions I discuss are: what does it mean to be good at math? Whose knowledge and experiences are represented? Whose and what approaches are valued? Which language(s) and forms of communication get privileged?
Former OSU Mathematics graduate student Li Chen received her PhD from the Department of Applied Mathematics and Statistics at Johns Hopkins University May, 2015, with a Phd thesis entitled Pattern Recognition on Random Graphs. She has accepted a position at Intel in Hillsboro, Oregon. Li completed her Master's degree in Mathematics at OSU in 2010 with her MS thesis: Risk Management for NonProfit Organizations. Congratulations Li !