James Theiler
| James Theiler | |
|---|---|
 James Theiler.jpg Theiler in 2024 | |
| Born | |
| 🎓 Alma mater | Massachusetts Institute of Technology (SB) California Institute of Technology (PhD) |
| 💼 Occupation | |
| Known for | Chaos theory, Theiler window, anomaly detection, remote sensing |
James Theiler is an American physicist and Laboratory Fellow at Los Alamos National Laboratory (LANL). His research interests include remote sensing, statistical modeling, machine learning, and image processing.
Education and early career
Theiler received S.B. degrees in physics and mathematics from the Massachusetts Institute of Technology in 1981. He completed his Ph.D. in physics at the California Institute of Technology in 1987, where his research focused on algorithms for identifying chaos in time series.[1]
Career
In 1994, Theiler joined the Space and Remote Sensing Sciences Group at Los Alamos National Laboratory. He has authored more than 250 articles, book chapters, and conference proceedings.[2] He currently serves on the program committee for a conference of the SPIE, and is a Senior Area Editor for the journal IEEE Transactions on Computational Imaging.
Scientific contributions
In his early work, Theiler is known for his foundational research in chaos theory and nonlinear time series analysis. He introduced the concept of the Theiler window, which refers to a region around the diagonal of a recurrence plot excluded in the calculation of recurrence quantification analysis (RQA) parameters. This helps to avoid artifacts from self-recurrences and high-resolution data.[3] At LANL, he collaborated with physicist J. Doyne Farmer, contributing to the development of techniques for detecting determinism and structure in complex dynamical systems.[4]
In the field of remote sensing, Theiler is internationally recognized for his research in anomaly detection and change detection applied to multispectral and hyperspectral imagery. His work has contributed to algorithms and methodologies that enable the automated identification of unusual patterns or changes in high-dimensional satellite data. This research supports a variety of applications including environmental monitoring, surveillance, and chemical detection.[5] He is the co-inventor on a United States patent for techniques in anomaly detection for hyperspectral image analysis.[6]
Theiler also collaborated with Mark Galassi, a fellow researcher at LANL, particularly in the development of scientific software and open-source tools. Both were authors of the GNU Scientific Library, an open-source numerical library for C and C++ programmers widely used in scientific computing.
Selected publications
- Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., & Farmer, J. D. (1992). Testing for nonlinearity in time series: the method of surrogate data. Physica D: Nonlinear Phenomena, 58(1–4), 77–94. [1](https://doi.org/10.1016/0167-2789(92)90102-S)
- Theiler, J. (1990). Estimating fractal dimension. Journal of the Optical Society of America A, 7(6), 1055–1073. [2](https://doi.org/10.1364/JOSAA.7.001055)
- Theiler, J., & Prichard, D. (1996). Constrained-realization Monte-Carlo method for hypothesis testing. Physica D: Nonlinear Phenomena, 94(4), 221–235. [3](https://doi.org/10.1016/0167-2789(96)00067-4)
- Theiler, J. (1986). Spurious dimension from correlation algorithms applied to limited time-series data. Physical Review A, 34(3), 2427–2432. [4](https://doi.org/10.1103/PhysRevA.34.2427)
- Theiler, J., & Prichard, D. (1994). Generating surrogate data for time series with several simultaneously measured variables. Physical Review Letters, 73(7), 951–954. [5](https://doi.org/10.1103/PhysRevLett.73.951)
- Theiler, J. (1991). Some comments on the correlation dimension of 1/fα noise. Physics Letters A, 155(8–9), 480–493. [6](https://doi.org/10.1016/0375-9601(91)91094-3)
- Theiler, J., & Rapp, P. E. (1996). Re-examination of the evidence for low-dimensional, nonlinear structure in the human electroencephalogram. Electroencephalography and Clinical Neurophysiology, 98(3), 213–222. [7](https://doi.org/10.1016/0013-4694(95)00232-9)
- Theiler, J. (1987). Efficient algorithm for estimating the correlation dimension from a set of discrete points. Physical Review A, 36(9), 4456–4462. [8](https://doi.org/10.1103/PhysRevA.36.4456)
- Theiler, J. (2000). Anomaly detection in hyperspectral imagery. Proceedings of SPIE, 4049, 61–72. [9](https://doi.org/10.1117/12.410358)
- Theiler, J., Cao, Y., & Bouman, C. A. (2010). Multi-sensor anomalous change detection in remote sensing imagery. Journal of Applied Remote Sensing, 15(4), 042411. [10](https://doi.org/10.1117/1.JRS.15.042411)
Personal life
Theiler lives in New Mexico with his wife, Bette Korber, a computational biologist internationally renowned for her work in HIV/AIDS and COVID-19 vaccine research. He has contributed to computational analysis in support of her biomedical research.
Recognition
As of 2025, Theiler's work has received over 30,000 citations according to Google Scholar.[2]
See also
- Chaos theory
- Recurrence plot
- Remote sensing
- Hyperspectral imaging
- Bette Korber
- Mark Galassi
- J. Doyne Farmer
External links
References
- ↑ Theiler (1987). "Identifying Chaos in Time Series". PhD Dissertation. California Institute of Technology.
- ↑ 2.0 2.1 "James Theiler – Google Scholar".
- ↑ Theiler (1990). "Some Comments on Recurrence Plots". Physica D. 58: 77–93.
- ↑ Theiler, J.; Eubank, S.; Longtin, A.; Galdrikian, B.; Farmer, J. Doyne (1992). "Testing for nonlinearity in time series: the method of surrogate data". Physica D. 58 (1–4): 77–94. doi:10.1016/0167-2789(92)90102-S.
- ↑ Theiler (2000). "Anomaly Detection in Hyperspectral Imagery". Proceedings of SPIE.
- ↑ US 7953280, "Methods and systems for detecting anomalous regions in hyperspectral images", assigned to Los Alamos National Security, LLC
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