Paul L Bendich

Paul L Bendich
  • Associate Research Professor of Mathematics
  • Assistant Director of Curricular Engagement of the Information Initiative at Duke
External address: 121 Physcis Bldg, Durham, NC 27708
Internal office address: Box 90320, Durham, NC 27708-0320
Phone: (919) 660-2811

Research Areas and Keywords

Computational Mathematics

topological data analysis, data science

Signals, Images & Data

topological data analysis, machine learning, applied topology, data science


topological data analysis, applied topology

I work in computational topology, which for me means adapting and using tools from algebraic topology in order to study noisy and high-dimensional datasets arising from a variety of scientific applications. My thesis research involved the analysis of datasets for which the number of degrees of freedom varies across the parameter space. The main tools are local homology and intersection homology, suitably redefined in this fuzzy multi-scale context. I am also working on building connections between computational topology and various statistical data analysis algorithms, such as clustering or manifold learning, as well as building connections between computational topology and diffusion geometry.

Education & Training
  • Ph.D., Duke University 2008

Selected Grants

Geometric and Topological Methods for Multi-Modal Data Analysis and Fusion awarded by Air Force Office of Scientific Research (Co-Principal Investigator). 2018 to 2021

BIGDATA: F: DKA: CSD: Topological Data Analysis and Machine-Learning with Community-Accepted Features awarded by National Science Foundation (Co-Principal Investigator). 2014 to 2019

Topological Signal Analysis for Multi-Modal Data Analysis awarded by Geometric Data Analytics, Inc. (Principal Investigator). 2016 to 2017

Bendich, Paul, et al. “Stabilizing the unstable output of persistent homology computations.” Journal of Applied and Computational Topology, Springer, Nov. 2019, pp. 1–30.

Bendich, P., et al. Scaffoldings and Spines: Organizing High-Dimensional Data Using Cover Trees, Local Principal Component Analysis, and Persistent Homology. Vol. 13, Jan. 2018, pp. 93–114. Scopus, doi:10.1007/978-3-319-89593-2_6. Full Text

Bendich, P., et al. “Topological and statistical behavior classifiers for tracking applications.” Ieee Transactions on Aerospace and Electronic Systems, vol. 52, no. 6, Dec. 2016, pp. 2644–61. Scopus, doi:10.1109/TAES.2016.160405. Full Text

Bendich, P., et al. “Persistent homology analysis of brain artery trees.” Annals of Applied Statistics, vol. 10, no. 1, 2016, pp. 198–218. Open Access Copy

Munch, E., et al. “Probabilistic Fréchet means for time varying persistence diagrams.” Electronic Journal of Statistics, vol. 9, Jan. 2015, pp. 1173–204. Scopus, doi:10.1214/15-EJS1030. Full Text Open Access Copy

Bendich, P., et al. “Homology and robustness of level and interlevel sets.” Homology, Homotopy and Applications, vol. 15, no. 1, Apr. 2013, pp. 51–72. Scopus, doi:10.4310/HHA.2013.v15.n1.a3. Full Text

Bendich, P., et al. “A point calculus for interlevel set homology.” Pattern Recognition Letters, vol. 33, no. 11, Aug. 2012, pp. 1436–44. Scopus, doi:10.1016/j.patrec.2011.10.007. Full Text

Bendich, P., et al. “Improving homology estimates with random walks.” Inverse Problems, vol. 27, no. 12, Dec. 2011. Scopus, doi:10.1088/0266-5611/27/12/124002. Full Text

Bendich, P., and J. Harer. “Persistent Intersection Homology.” Foundations of Computational Mathematics, vol. 11, no. 3, June 2011, pp. 305–36. Scopus, doi:10.1007/s10208-010-9081-1. Full Text

Bendich, P., et al. “The robustness of level sets.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6346 LNCS, no. PART 1, Nov. 2010, pp. 1–10. Scopus, doi:10.1007/978-3-642-15775-2_1. Full Text


Blasch, E., et al. “Machine learning in/with information fusion for infrastructure understanding, panel summary.” Proceedings of Spie  the International Society for Optical Engineering, vol. 11423, 2020. Scopus, doi:10.1117/12.2559416. Full Text

Tralie, C. J., et al. “Multi-Scale Geometric Summaries for Similarity-Based Sensor Fusion.” Ieee Aerospace Conference Proceedings, vol. 2019-March, 2019. Scopus, doi:10.1109/AERO.2019.8741399. Full Text

Bendich, P. “Topology, geometry, and machine-learning for tracking and sensor fusion.” Proceedings of Spie  the International Society for Optical Engineering, vol. 11017, 2019, pp. lxxxiii–cii.

Garagić, D., et al. “Upstream fusion of multiple sensing modalities using machine learning and topological analysis: An initial exploration.” Ieee Aerospace Conference Proceedings, vol. 2018-March, 2018, pp. 1–8. Scopus, doi:10.1109/AERO.2018.8396737. Full Text

Tralie, C. J., et al. “Geometric cross-modal comparison of heterogeneous sensor data.” Ieee Aerospace Conference Proceedings, vol. 2018-March, 2018, pp. 1–10. Scopus, doi:10.1109/AERO.2018.8396789. Full Text Open Access Copy

Bendich, P., et al. “Geometric models for musical audio data.” Leibniz International Proceedings in Informatics, Lipics, vol. 51, 2016, pp. 65.1-65.5. Scopus, doi:10.4230/LIPIcs.SoCG.2016.65. Full Text Open Access Copy

Bendich, P., et al. “Geometric Models for Musical Audio Data.” Proceedings of the 32st International Symposium on Computational Geometry (Socg), 2016.

Tralie, C. J., and P. Bendich. “Cover Song Identification with Timbral Shape Sequences.” 16th International Society for Music Information Retrieval (Ismir), 2015, pp. 38–44. Open Access Copy

Bendich, P., et al. “Multi-scale local shape analysis and feature selection in machine learning applications.” Proceedings of the International Joint Conference on Neural Networks, vol. 2015-September, 2015. Scopus, doi:10.1109/IJCNN.2015.7280428. Full Text Open Access Copy

Rouse, D., et al. “Feature-aided multiple hypothesis tracking using topological and statistical behavior classifiers.” Proceedings of Spie  the International Society for Optical Engineering, vol. 9474, 2015. Scopus, doi:10.1117/12.2179555. Full Text


Bendich, P., et al. Stratification learning through homology inference. 1 Dec. 2010, pp. 10–17.

A key goal of Together Duke is to invest in faculty as scholars and leaders of the university’s intellectual communities. To foster collaboration around new and emerging areas of interest... read more »

Paul Bendich

For his excellent work in developing Data+ into a university model for undergraduate mentoring and research, Paul Bendich has been granted the Dean's Leadership Award.  This award is given to a faculty or staff member who has made a distinctive... read more »