I am a statistician and applied mathematician with broad interests in
statistical methodology and computing in the physical sciences. My primary
interest is modeling complicated dependence structure in real-life
temporal/spatial/spatio-temporal processes, and I'm particularly interested in
theoretical questions that are motivated by computationally scalable methods and
I have specific experience in the fields of maximum likelihood estimation for
Gaussian processes, nonparametric spectral density estimation in one and several
dimensions, and some applications of signal processing and multiple testing
strategies to the analysis of power systems.
I am currently a PhD student in the
Department of Statistics at Rutgers University,
Since August 2016, I have worked at
Argonne National Laboratory
under the supervision of
Statistical computing and algorithms
Spatio-temporal processes, especially spectral domain representations of
multidimensional and nonstationary processes
Nonstationarity, nonlinearity, and non-Gaussianity in time-dependent
Applications in the physical sciences, especially in envirometrics
Matrix computations, especially hierarchical matrices and other methods
for scalable dense linear algebra
Integral transforms and oscillatory integrals
Irregular/nonuniform Fourier-type methods
Inverse problems and data assimilation
a software companion to "Scalable Gaussian process computations using
hierarchical matrices" in the Julia programming language. It is reasonably
efficient and easy to use for workstation-level problems, up to perhaps a
few million points if you have time a few hundred thousand if you'd like
to just run something over your lunch break. It is not sufficiently tuned
to be a good choice for clusters, unfortunately. If you have any interest
and/or experience in bringing Julia software up to that level, though,
please do reach out, as I (and I hope other people) still actively develop
and use this package.
a simple software package that provides the obviously efficient matrix
methods for block diagonal matrices that are for some reason not provided
in Julia's LinearAlgebra module by default. 70 loc total and very simple.
a simple software package that provides a simple interface for simulating
ARMA processes in a convenient format with infinite iterators. Univariate,
vector-, and matrix-valued processes are supported, which a high degree of
extensibility if you define some of your own custom structs. For
univariate processes, some other simple things are implemented, like the
spectral density, autocovariance, and so on.
A short and incomplete list.
was the best boss I'll ever have. I took two of his classes as an
undergraduate that I wasn't entirely ready for and he was patient and
encouraging, and he took a huge chance on me when he hired me at ANL. He
has been a huge influence on my academic interests and on how I approach
problems in general. If he hadn't given me such a huge opportunity and
been so supportive throughout, I probably would not have become a
scientist at all. I really can't say enough about what he's done for me
and how grateful I am.
, my advisor, has been incredibly generous with his time and thoughts. I
am early in the program and haven't had too much of a chance to pester him
yet, but I really look forward to actively doing work with him and
learning from him.
taught me basically everything I know about spectral domain
representations of stochastic processes, and was incredibly supportive and
generous with her time when I started at ANL.
's introductory real analysis class was my first serious exposure to the subject, and the two
quarters that I had with him really cemented my love for analysis.
's introductory course (STAT 244) made me change my undergraduate plans
and try to take statistics seriously. Despite me being pretty annoying,
he's also been consistently patient, helpful, and supportive for over five
years now. He has been incredibly influential on my interests and
taught my favorite course that I took as an undergraduate---complex
analysis---and has been incredibly generous with her time as I've asked
her to write way too many recommendation letters for me.
The entire spatial statistics community has been incredibly warm and welcoming.
cgeoga $at anl $dot gov
christopher.geoga $at stat $dot rutgers $dot edu