Chris Geoga

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 approximations.

Education:

Ph.D. Statistics, Rutgers University (2018 – 2023)

Dissertation: Models and Computation for Nonstationary Processes at Scale
Adv. Michael L. Stein

B.S. Mathematics / B.A. Statistics, University of Chicago (2012 – 2016)

Employment:

University of Wisconsin-Madison, Dept. of Statistics

Assistant Professor (2023 – )

Argonne National Laboratory, Division of Mathematics and Computer Science

Visiting Student (2022 – 2023)
Argonne Associate (2018 – 2022)
Assistant Computational Mathematician (RD1) (2016 – 2018)

Publications:

Clicking on the title of the paper will take you to ArXiv or some other direct PDF source, and the DOI link will take you to the official journal version. Direct PDF links marked (ArXiv vXXX) mean that the ArXiv version differs from the journal version somehow. I try to do a good job about uploading substantial revisions to arXiv, so the differences are in general limited to small revisions that don't really change the substance of the paper very much. But if you want a journal version and can't get access, please shoot me a note and I'll send one over.

You can also see my publications on My google scholar profile.

P.G. Beckman, C.J. Geoga. Fast Adaptive Fourier Integration for Spectral Densities of Gaussian Processes. To appear, Statistics and Computing.
C.J. Geoga. Fast Machine-Precision Spectral Likelihoods for Stationary Time Series. To appear, SISC.
C.J. Geoga, M.L. Stein. A Scalable Method to Exploit Screening in Gaussian Process Models with Noise (Arxiv v2). Journal of Computational and Graphical Statistics. DOI link.
P.G. Beckman, C.J. Geoga, M.L. Stein, and M. Anitescu. Scalable Computations for Nonstationary Gaussian Processes (Arxiv v1). Statistics and Computing. DOI link.
C.J. Geoga, O. Marin, M. Schanen, and M.L. Stein. Fitting Matern smoothness parameters using automatic differentation . Statistics and Computing. DOI link.
C.J. Geoga, M. Anitescu, and M.L. Stein. Flexible nonstationary spatio-temporal modeling of high-frequency monitoring data (ArXiv v1). Environmetrics. DOI link.
C.J. Geoga, M. Anitescu, and M.L. Stein. Scalable Gaussian process computations using hierarchical matrices. Journal of Computational and Graphical Statistics. DOI link.
C.J. Geoga, C.L. Haley, A. Siegel, and M. Anitescu. Frequency-wavenumber spectral analysis of spatiotemporal flows. Journal of Fluid Mechanics. DOI link.

Software:

All code is written in the Julia language. If you are an R user but want to use any of this software, feel free to reach out---I have provided or helped other people develop bindings for use in R before. It isn't too bad to do.

SMultitaper.jl: a bare-bones adaptively-weighted multitaper spectral density estimator. Makes aggressive use of caching for fast and non-allocating repeated calls.
GPMaxlik.jl: A backend library for plugging in your exotic implicit matrix operator and getting a careful and efficient Gaussian negative log-likelihood, score, and expected Fisher information matrix. I actually wrote docs for this one, so bring your FMM code and skip coding the GP parts by using this library.
Vecchia.jl, A chunked implementation of Vecchia's approximation. Carefully tuned for great thread-parallelism and automatic differentiation. This software is a testbed for a lot of what I do and offers a lot of functionality that no other scalable GP library provides, so please do give it a shot.
BesselK.jl, An implementation of the modified second-kind Bessel function K_v that is `ForwardDiff.jl`-compatible with respect to the order. See the paper "Fitting Matern..." for more details on this.
libmatern, An improved version of the besselk routines in BesselK.jl, and now written in C for portability. Hook into it with your favorite programming language and existing software stack!
SpectralKernels.jl, a library for evaluating covariance functions in one dimension via their spectral density. It is the software companion to "Fast Adaptive Fourier...", and has a lot of neat tricks for scaling up well and handling difficult spectral densities, like with singularities or slow tail decay. It is also designed with autodiff in mind, so you can automatically get gradients of your spectrally-specified models as well.
SpectralEstimators.jl, The software companion to "Fast Machine-Precision...", gives exact log-likelihoods for time series models with piecewise smooth spectral densities in quasilinear time. I haven't gotten around to writing much documentation for this, but if you are interested in hooking in, shoot me a note and I'll develop whatever docs you need.

Contact:

Besides for a very new Mastodon account that I will probably not actually get into the habit of using, I do not use any social media or internet networking websites. If you see any account with my name on it, please assume that it is not really me. Please contact me by email at the following address:

geoga $at wisc $dot edu

I also use XMPP, so if you'd prefer to communicate by instant messaging please first reach out by email and I will share that contact information with you.

                    
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