Current and Graduated Students
- Nick Grunloh
is working on nonparametric modeling of fish populations.
- Sampson Mao,
MS 2021, co-advised by Robert Lund, worked on methods for transforming Gaussian random fields to get
fields with other distributions, such as correlated counts.
- Amanda
Coleman, MS 2020, worked on a remote sensing project with the Naval
Postgraduate School.
- Rui Meng,
PhD 2020,
worked on temporal stochastic process model projects with Lawrence Livermore National Laboratory.
- Travis
Linscome-Hatfield, MS 2019,
worked on surrogate modeling for variable selection in computer simulation experiments.
- Matt Simms,
PhD 2019, co-advised by Hongyun Wang,
worked on statistical inference for nanopore experiments.
- Tony Pourmohamad, PhD
2016, worked on constrained optimization using a filter approach.
- Kenna Nelson, MS 2015,
worked on optimizing locus of control for aircraft flights.
- Brenton Blair, MS 2015,
worked on classification for damaged aircraft trajectories.
- Nick Grunloh, MS 2014,
worked on assessing convergence for statistical optimization.
- John Guenther, PhD 2013,
worked on finding, characterizing, and choosing among multiple optima, as well
as simultaneous optimization and sensitivity analysis.
- Morgan Mendoza, MS 2012, developed algorithms for generating geologically realistic permeability grids as simulator inputs
- Samantha Crane, MS 2012, explored combining statistical modeling with a filter approach for constrained optimization
- Yuning He, PhD 2012,
worked on computer model emulation for complex simulators
with multivariate functional output.
- Waley Liang, PhD 2012, worked on partitioning
for the convolution approach to Gaussian processes, to allow
nonstationary modeling of large spatial datasets.
- Jing Chang, PhD 2011,
worked on model selection for neural networks and computer models.
- Tracy Holsclaw,
PhD 2011, co-advised by Bruno Sansó,
worked on modeling of unknowns in the equation of state
in cosmology, to help our understanding of dark energy.
- Xin Zhang, MS 2010, explored scaling issues for mixed-integer surrogate modeling
- Angela Pignotti,
PhD 2009, worked on
methodology for comparing spatially correlated fields, with an
application to global and regional climate modeling.
- Matt
Taddy, PhD 2008, did his thesis work primarily with Thanasis
Kottas on Bayes nonparametrics, but also worked with me on computer
modeling and optimization using treed Gaussian processes.
- Weining Zhou, PhD
2006, co-advised by Bruno Sansó, worked on spatial
problems, in particular an inverse problem for a proton accelerator
and an environmetric application with Bruno Sansó.
- Bobby
Gramacy, PhD 2005, won the Savage Award for
his thesis on treed Gaussian processes, a flexible yet computationally
efficient approach for nonstationary spatial modeling and
heteroscedastic semiparametric regression.
- Chris
Holloman, PhD 2002 from Duke University, co-supervised with Dave
Higdon, worked on multiscale methods for spatial inverse problems,
including multiscale genetic algorithms.