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Faculty
Bill Basener
Professor of Data Science
School of Data Science
Email: wb8by@virginia.edu
Additional Contact Information
Website: billbasener.com/
Office: Elson, Room 198
Bill Basener is a Professor at the School of Data Science with a joint appointment in the Department of Systems and Information Engineering. He has authored research publications in machine learning, signal processing, image processing, dynamical systems, game theory, ecological economics, evolutionary genetics, and other applied mathematical fields, as well as a textbook on applied topology and multiple patents.
The methods and software he developed for processing images in hyperspectral imaging have become the gold-standard in the field, used for processing millions of images by dozens of organizations. The Basener-Ross model he developed for modeling ecological collapse has been used for studying ancient civilizations. His textbook, Topology and Its Applications, was one of the first textbooks in the field of applied topology, and covered diverse applications in cosmology, chaos theory, condensed matter physics, protein folding, computer graphics, and robot coordination. He invented the topological anomaly detection, gradient flow clustering, hierarchical material identification, and object-based identification algorithms in image processing. This technology has been used in disaster relief efforts across the world.
Prior to joining the School of Data Science in 2014, Basener was an Emeritus Professor at the Rochester Institute of Technology of Mathematical Sciences. He is also the founder and president of two data analytics software companies, Geospatial Technology Associates and Spectral Solutions. Basener holds a PH.D. in Mathematics from Boston University and a B.S. in Mathematics from Marist College.
Education
- PH.D., Mathematics, Boston University
- B.S., Mathematics, Marist College
Data Science Domains
Analytics, Systems
Areas of Practice
Lidar, Hyperspectral Analysis, Geospatial Information, Biological Modeling, Biomedicine, Environmental Science
Methodologies
Bayesian Methods, Machine Learning, Programming Network Science
Selected Publications
Basener, B., Ientilucci, E. J., & Messinger, D. W. (2007, April 27). Anomaly detection using topology. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII. doi:10.1117/12.745429
Canham, K., Schlamm, A., Ziemann, A., Basener, B., & Messinger, D. (2011). Spatially Adaptive Hyperspectral Unmixing. IEEE transactions on geoscience and remote sensing: a publication of the IEEE Geoscience and Remote Sensing Society, 49(11), 4248–4262. doi:10.1109/tgrs.2011.2169680
F. Basener, W., Parwani, K., & Wiandt, T. (2007a). Minimal flows. In Open Problems in Topology II (bll 453–462). doi:10.1016/b978-044452208-5/50045-4
Basener, B., & Ross, D. S. (2004). Booming and crashing populations and Easter island. SIAM Journal on Applied Mathematics, 65(2), 684–701. doi:10.1137/s0036139903426952
Basener, W. F., & Messinger, D. W. (2009, Mei 1). Enhanced detection and visualization of anomalies in spectral imagery. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. doi:10.1117/12.818672
Basener, W. F. (2010, April 23). Clutter and anomaly removal for enhanced target detection. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI. doi:10.1117/12.850303
Ziemann, A. K., Messinger, D. W., & Basener, W. F. (2010, April 23). Iterative convex hull volume estimation in hyperspectral imagery for change detection. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI. doi:10.1117/12.850122
Basener, W., Brooks, B., Radin, M., & Wiandt, T. (2008b). Rat instigated human population collapse on easter island. Nonlinear Dynamics, Psychology, and Life Sciences, 12(3), 227–240. Opgehaal van https://www.ncbi.nlm.nih.gov/pubmed/18510835
Messinger, D., Ziemann, A., Schlamm, A., & Basener, B. (2010, Junie). Spectral image complexity estimated through local convex hull volume. 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Presented at the 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland. doi:10.1109/whispers.2010.5594869
Basener, W., Brooks, B., Radin, M., & Wiandt, T. (2008a). Dynamics of a discrete population model for extinction and sustainability in ancient civilizations. Nonlinear Dynamics, Psychology, and Life Sciences, 12(1), 29–53. Opgehaal van https://www.ncbi.nlm.nih.gov/pubmed/18157926
Basener, W. F., Nance, E., & Kerekes, J. (2011, Mei 13). The target implant method for predicting target difficulty and detector performance in hyperspectral imagery. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII. doi:10.1117/12.885564
Schlamm, A. (2009). Geometric estimation of the inherent dimensionality of single and multi-material clusters in hyperspectral imagery. Journal of Applied Remote Sensing, 3(1), 033527. doi:10.1117/1.3133323
Basener, W. F., & Sanford, J. C. (2018). The fundamental theorem of natural selection with mutations. Journal of Mathematical Biology, 76(7), 1589–1622. doi:10.1007/s00285-017-1190-x
Schlamm, A., Messinger, D., & Basener, W. (2008, April 3). Geometric estimation of the inherent dimensionality of a single material cluster in multi- and hyperspectral imagery. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV. doi:10.1117/12.776903
Schlamm, A., Resmini, R. G., Messinger, D., & Basener, W. (2010, April 23). A comparison study of dimension estimation algorithms. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI. doi:10.1117/12.849125
Gartley, M. G., & Basener, W. (2009, Mei 1). Topological anomaly detection performance with multispectral polarimetric imagery. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. doi:10.1117/12.817843
Basener, W., Brooks, B. P., & Ross, D. (2006). The Brouwer Fixed Point Theorem applied to rumour transmission. Applied Mathematics Letters, 19(8), 841–842. doi:10.1016/j.aml.2006.02.007
Basener, W., Brooks, B., Radin, M., & Wiandt, T. (2011). [Review of Spatial effects and turing instabilities in the invasive species model]. Nonlinear dynamics, psychology, and life sciences, 15(4), 455–464. Opgehaal van https://www.ncbi.nlm.nih.gov/pubmed/21933514
Albano, J. A., Messinger, D. W., Schlamm, A., & Basener, W. (2011, Mei 13). Graph theoretic metrics for spectral imagery with application to change detection. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII. doi:10.1117/12.883574
Canham, K., Schlamm, A., Basener, B., & Messinger, D. (2011, Mei 13). High spatial resolution hyperspectral spatially adaptive endmember selection and spectral unmixing. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII. doi:10.1117/12.884190
Messinger, D. W., van Aardt, J., McKeown, D., Casterline, M., Faulring, J., Raqueño, N., … Velez-Reyes, M. (2010, Augustus 19). High-resolution and LIDAR imaging support to the Haiti earthquake relief effort. In S. S. Shen & P. E. Lewis (Reds), Imaging Spectrometry XV. doi:10.1117/12.862090
Ziemann, A. K., Messinger, D. W., Albano, J. A., & Basener, W. F. (2012, Mei 1). Assessing the impact of background spectral graph construction techniques on the topological anomaly detection algorithm. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. doi:10.1117/12.918889
Doster, T. J., Ross, D. S., Messinger, D. W., & Basener, W. F. (2009, Mei 1). Anomaly clustering in hyperspectral images. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. doi:10.1117/12.818407
Schlamm, A., Messinger, D., & Basener, W. (2009a, Mei 1). Effect of manmade pixels on the inherent dimension of natural material distributions. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. doi:10.1117/12.816568
Basener, W. (2004a). Every nonsingular C1 flow on a closed manifold of dimension greater than two has a global transverse disk. Topology and Its Applications, 135(1–3), 131–148. doi:10.1016/s0166-8641(03)00160-3
Basener, W. (2002). Global cross sections and minimal flows. Topology and Its Applications, 121(3), 415–442. doi:10.1016/s0166-8641(01)00094-3
Schlamm, A., Messinger, D., & Basener, W. (2012). Interest segmentation of large area spectral imagery for analyst assistance. IEEE journal of selected topics in applied earth observations and remote sensing, 5(2), 409–420. doi:10.1109/jstars.2012.2195298
Basener, B., Schlamm, A., Messinger, D., & Ientilucci, E. (2011, Junie). A detection-identification process with geometric target detection and subpixel spectral visualization. 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Presented at the 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, Portugal. doi:10.1109/whispers.2011.6080948
Schlamm, A., Messinger, D., & Basener, W. (2010a, April 23). A novel method for change detection in spectral imagery. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI. doi:10.1117/12.849123
Basener, W., & Flynn, M. (2018, Oktober 23). Microscene evaluation using the Bhattacharyya distance. In A. M. Larar, M. Suzuki, & J. Wang (Reds), Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII. doi:10.1117/12.2327004
Basener, B. (2011, Mei 13). An automated method for identification and ranking of hyperspectral target detections. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII. doi:10.1117/12.885507
F. Basener, W., Parwani, K., & Wiandt, T. (2007b). Minimal flows. In Open Problems in Topology II (bll 453–462). doi:10.1016/b978-044452208-5/50045-4
Basener, W. F. (2013, Julie). Limits of chaos and progress in evolutionary dynamics. Biological Information. Presented at the Proceedings of the Symposium, Cornell University, USA. doi:10.1142/9789814508728_0004
Schlamm, A., Messinger, D., & Basener, W. (2010b, Junie). Interest segmentation of hyperspectral imagery. 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Presented at the 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland. doi:10.1109/whispers.2010.5594834
Basener, W. (2006a). Geometry of minimal flows. Topology and Its Applications, 153(18), 3627–3632. doi:10.1016/j.topol.2006.03.026
Basener, W., & Sullivan, M. C. (2006). Periodic prime knots and topologically transitive flows on 3-manifolds. Topology and Its Applications, 153(8), 1236–1240. doi:10.1016/j.topol.2005.03.009
Basener, W. (2004b). Knots and topologically transitive flows on 3-manifolds. Topology. An International Journal of Mathematics, 43(3), 697–703. doi:10.1016/j.top.2003.10.007
Basener, W. F., & Basener, W. J. (2019). Ecological collapse of Easter Island and the role of price fixing. European Journal of Mathematics, 5(3), 646–655. doi:10.1007/s40879-019-00352-5
Basener, W. F. (2017, Mei 5). Ensemble learning and model averaging for material identification in hyperspectral imagery. In M. Velez-Reyes & D. W. Messinger (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII. doi:10.1117/12.2263693
Basener, W. F., & Basener, A. (2017, Mei 5). Classification and identification of small objects in complex urban-forested LIDAR data using machine learning. In M. D. Turner & G. W. Kamerman (Reds), Laser Radar Technology and Applications XXII. doi:10.1117/12.2264641
Basener, W. (2006b). Transverse disks, symbolic dynamics, homology direction vectors, and Thurston–Nielson theory. Topology and Its Applications, 153(14), 2760–2764. doi:10.1016/j.topol.2006.03.025
Basener, W., Cordova, S., Hössjer, O., & Sanford, J. (2021). Dynamical systems and fitness maximization in evolutionary biology. In Handbook of the Mathematics of the Arts and Sciences (bll 1–72). doi:10.1007/978-3-319-70658-0_121-1
Schlamm, A., Messinger, D., & Basener, W. (2009b, Mei 1). Effect of manmade pixels on the inherent dimension of natural material distributions. In S. S. Shen & P. E. Lewis (Reds), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. doi:10.1117/12.816568
Radin, M., Basener, W., Brooks, B., & Wiandt, T. (2005). Population of Easter island, modeled by discrete dynamical systems [Data set]. PsycEXTRA Dataset. doi:10.1037/e402652008-001
Basener, W. F. (2006). Topology and Its Applications [PDF]. doi:10.1002/9780470067949