PNNL

Dr. Tegan Emerson is a chief data scientist and leader of the Mathematics, Statistics, and Data Science group in the National Security Division at Pacific Northwest National Laboratory (PNNL). She leverages her mathematics background to develop data science solutions in support of the Department of Energy’s mission. Specifically, Dr. Emerson focuses on artificial intelligence (AI) assurance in high-consequence settings—both developing models that are assured by design as well as improving, evaluating, and understanding assurances for frontier models. Her work and technical leadership have supported innovations in hyperspectral imaging, overhead image analysis, models of atmospheric turbulence, signal processing, neuroscience, medical imaging, materials science, and many other application domains.

Dr. Emerson’s research interests include geometric and topological data analysis, dimensionality reduction, image processing, generative AI, and optimization. Her work has resulted in over 30 peer-reviewed papers, invited talks, and the development of first-of-their-kind capabilities. She is a prolific member of the broader scientific community, holding joint appointments at two academic institutions, sitting on external advisory boards, leading and supporting organizational committees for academic and mission workshops, acting as an editor for the official journal of the Association of Women in Mathematics, and participating in strategy-defining activities. In 2022, Dr. Emerson, along with two of her PNNL colleagues, founded a collaborative research community in topology, algebra, and geometry in data science, which has grown to include over 1,000 researchers across the world. Through her engagement activities, she cultivates collaboration and influences research directions to increase the number and quality of tools and resources available to maintain a technological advantage for the nation.

• Topological data analysis
• Algebraic data analysis
• Geometric data analysis
• Representation learning
• AI for advanced manufacturing and materials science
• Explainable and robust AI
• Safety and security of AI systems
• Geospatial intelligence and remote sensing beyond the visible spectrum

• PhD in mathematics, Colorado State University
• MS in mathematics, Colorado State University
• BS in mathematics, Oregon State University

• Affiliate faculty, Department of Mathematics, Colorado State University
• Affiliate faculty, Data Science Research Institute, Colorado State University
• Research assistant professor, Department of Mathematical Sciences, University of Texas El Paso
• Workshop organizer, International Conference on Machine Learning
• Special session organizer, Joint Mathematics Meeting
• Lead editor, “Topology, Algebra, and Geometry in the Data Sciences” topical collection, La Matematica
• Lead editor, “Topological, Algebraic, and Geometric Learning” volume, Proceedings in Machine Learning Research
• Cofounder, Topology, Algebra, and Geometry in Data Science Research Community

• Plenary Talk Host (“Generating Realistic Material Microstructures Using Generative Networks for Advanced Manufacturing”), First World Congress on Artificial Intelligence in Materials and Manufacturing, 2022
• Best Paper Award (one of three awards presented, chosen from more than 175 papers), 8th Workshop on Hyperspectral Image and Signal Processing: Evolutions in Remote Sensing, 2016
• Mathematical Problems in Industry Fellowship (one of two awards presented), Mathematical Problems in Industry Workshop, 2015
• NSF-Mathematics Travel Grant, 2nd Heidelberg Laureate Forum (one of 20 members comprising the American delegation), 2014
• Culture of Writing Award (for outstanding writing in a writing intensive course in mathematics), Department of Mathematics at Oregon State University, 2010–2011

 Generative Invertible Network - Provisional patent

2025

• Emerson, T. H., J. A. Chaput, and R. Aster. 2025. “Deep clustering of ambient volcanic seismicity: An example at Erebus volcano, Antarctica.” Abstract submitted to International Geoscience and Remote Sensing Symposium.
• Kvinge, H. J., T. H. Emerson, T. J. Doster, S. A. Mahan, and S. L. McGuire. 2025. “Topological, Algebraic, and Geometric Methods for Safe, Robust, and Explainable Machine Learning.” Abstract submitted to 2025 Joint Mathematics Meetings.

2024

• Emerson, T. H., S. A. Howland, K. S. Kappagantula, and H. J. Kvinge. 2024. “2024 TMS Abstract.” Abstract submitted to 2024 TMS Annual Meeting.
• Cedillo, L. R., J. V. Koch, T. J. Doster, and T. H. Emerson. 2024. “Joint estimation of bathymetry and water-column-corrected spectra with Neural ODEs.” Abstract submitted to SPIE - Defense and Commercial Sensing.
• Coda, E. D., J. Buckheit, D. R. Brown, B. T. Kennedy, L. T. Truong, B. Kay, T. H. Emerson, C. Joslyn, M. Henry, J. A. Emanuello, and H. Kvinge. 2024. “The Shape of 1+1: A Geometric Investigation of How Large Language Models Perform (or Fail to Perform) Arithmetic.” Abstract submitted to Deep Math: Conference on the Mathematical Theory of Deep Neural Networks.
• Kono, N., E. B. Byler, C. W. Godfrey, T. J. Doster, and T. H. Emerson. 2024. “Adapting self-supervised learning to the hyperspectral domain: methods, challenges, and lessons learned.” Abstract submitted to SPIE Defense + Commercial Sensing.
• Marrinan, T. P., T. J. Doster, T. H. Emerson, and J. V. Koch. 2024. “SPIE DC Abstract - Multiview hyperspectral image compression.” Abstract submitted to SPIE Defense + Commercial Sensing.
• Koch, J. V., B. M. Forland, B. E. Bernacki, T. J. Doster, and T. H. Emerson. 2024. “Data-Driven Invertible Neural Surrogates of Atmospheric Transmission.” In IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2024), Athens, Greece, July 7–12, 2024: 6943–6947.
• Cedillo, L. R., J. V. Koch, T. J. Doster, and T. H. Emerson. 2024. “Towards Joint Estimation of Bathymetry and Water-Column-Corrected Spectra with Neural Networks.” In SPIE Commercial and Defense Sensing.
• Howland, S., K. S. Kappagantula, H. J. Kvinge, and T. H. Emerson. 2024. “Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss.” In Proceedings of the ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling: 202–211. PMLR 251, 2024. 
• Emerson, T. H., H. J. Kvinge, K. S. Kappagantula, and S. Howland. 2024. “Geometry-grounded representation learning and generative modeling workshop at ICML 2024.” In Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss.
• Myers, A. D., T. J. Doster, C. C. Olson, and T. H. Emerson. 2024. “Topological and Dynamical Representations for Radio Frequency Signal Classification.” In ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling, Vienna, Austria, July 29, 2024: 1–10.

2023

• Howland, S. A., L. Kassab, K. S. Kappagantula, H. J. Kvinge, and T. H. Emerson. 2023. “Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing.” Integrating Materials and Manufacturing Innovation 12 (1): 1–10.
• DeVille, L., T. H. Emerson, S. Garibaldi, M. Reed, T. Washington, and S. Weeks. 2023. “Supporting Faculty in Mentoring Students for Careers Beyond Academia.” Notices of the American Mathematical Society 70 (9): 1442–1447.
• Koch, J. V., T. P. Marrinan, B. M. Forland, T. H. Emerson, and T. J. Doster. 2023. “A Neural Differential Equation Formulation for Modeling Atmospheric Effects in Hyperspectral Images.” Abstract submitted to 2023 SIAM PNW.
• Tipton, C. A., E. D. Coda, D. R. Brown, A. S. Bittner, J. H. Lee, G. S. Jorgenson, T. H. Emerson, and H. Kvinge. 2023. “Haldane Bundles: A Dataset for Learning to Predict the Chern Number of Line Bundles on the Torus.” In NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences.
• Tipton, C. A., E. D. Coda, D. R. Brown, A. S. Bittner, J. H. Lee, G. S. Jorgenson, T. H. Emerson, and H. Kvinge. 2023. “Haldane Bundles: A Dataset for Learning to Predict the Chern Number of Line Bundles on the Torus.” In AI for Accelerated Materials Design NeurIPS ‘23 Workshop.
• Koch, J. V., B. M. Forland, T. J. Doster, and T. H. Emerson. 2023. “A Neural Differential Equation Formulation for Modeling Atmospheric Effects in Hyperspectral Images.” In Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX.
• Kvinge, H. J., G. S. Jorgenson, D. R. Brown, C. W. Godfrey, and T. H. Emerson. 2023. “Internal Representations of Vision Models Through the Lens of Frames on Data Manifolds.” In the Thirty-seventh Conference on Neural Information Processing Systems.
• Kvinge, H. J., G. S. Jorgenson, D. R. Brown, C. W. Godfrey, and T. H. Emerson. 2023. “Exploring Neural Representations Using Frames on Data Manifolds.” In DeepMath 2023.
• Kvinge, H. J., G. S. Jorgenson, D. R. Brown, C. W. Godfrey, and T. H. Emerson. 2023. “Neural frames: A Tool for Studying the Tangent Bundles Underlying Image Datasets and How Deep Learning Models Process Them.” In the Fortieth International Conference on Machine Learning.
• Myers, A. D., H. J. Kvinge, and T. H. Emerson. 2023. “TopFusion: Using Topological Feature Space for Fusion and Imputation in Multi-Modal Data.” In the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. CVPRW, 2023.
• Kvinge, H. J., T. H. Emerson, G. S. Jorgenson, and T. J. Doster. 2023. “In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?” In DeepMath 2023.
• Kvinge, H. J., G. S. Jorgenson, D. R. Brown, C. W. Godfrey, and T. H. Emerson. 2023. “Internal Representations of Vision Models Through the Lens of Frames on Data Manifolds.” In Symmetry and Geometry in Neural Representations.
• Rawson, M. G., T. J. Doster, and T. H. Emerson. 2023. “Reproducing Kernel Hilbert Space Pruning for Sparse Hyperspectral Abundance Prediction.” In Proceedings of SPIE Defense + Commercial Sensing 2022.
• Tipton, C. A., E. D. Coda, D. R. Brown, A. S. Bittner, J. H. Lee, G. S. Jorgenson, and T. H. Emerson, and H. Kvinge. 2023. “Haldane Bundles: A Dataset for Learning to Predict the Chern Number of Line Bundles on the Torus.” In Symmetry and Geometry in Neural Representations.
• Truong, L. T., W. Choi, C. L. Wight, E. D. Coda, T. H. Emerson, K. S. Kappagantula, and H. J. Kvinge. 2023. “Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing.” In TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings.

2022

• Emerson, T., G. Jorgenson, H. Kvinge, and C. Olson. 2022. “Random Filters for Enriching the Discriminatory Power of Topological Representations.” In ICLR 2022 Workshop on Geometrical and Topological Representation Learning.
• Emerson, T., L. Kassab, S. Howland, H. Kvinge, and K. S. Kappagantula. 2022. “TopTemp: Parsing Precipitate Structure from Temper Topology.” In ICLR 2022 Workshop on Geometrical and Topological Representation Learning.
• Godfrey, C., D. Brown, T. Emerson, and H. Kvinge. 2022. “On the Symmetries of Deep Learning Models and their Internal Representations.” In NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing Systems, preprint, arXiv, November 28–December 9, 2022. arXiv:2205.14258.
• Coda, E., N. Courts, C. Wight, L. Truong, W. Choi, C. Godfrey, T. Emerson, K. Kappagantula, and H. Kvinge. 2022. “Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps.” In Proceedings of Machine Learning Research 196, Topological, Algebraic and Geometric Learning Workshops 2022. Virtual, July 25–22, 2022.
• Dixon, S., T. Doster, and T. Emerson. 2022. “To fail or not to fail: an exploration of machine learning techniques for predictive maintenance.” In Proceedings of SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 590–599. SPIE, June 16, 2022.

2021

• Kvinge, H., C. Wight, S. Akers, S. Howland, W. Choi, X. Ma, L. Gosink, E. Jurrus, K. Kappagantula, and T. Emerson. 2021. “A Topological-Framework to Improve Analysis of Machine Learning Model Performance,” preprint, arXiv, 2021. arXiv:2107.04714. Accepted to 2021 ICML Workshop on Uncertainty & Robustness in Deep Learning.
• Tymochko, S., J. Chaput, T. Doster, E. Purvine, J. Warley, and T. H. Emerson. 2021. “Con Connections: Detecting Fraud from Abstracts using Topological Data Analysis.” 20th IEEE International Conference on Machine Learning and Applications: 403–408. IEEE, Pasadena, CA, December 13–15, 2021.
• Emerson, T. H., S. Tymochko, G. Stantchev, J. A. Edelberg, M. Wilson, and C. C. Olson. 2021. “A Topological Approach for Motion Track Discrimination.” Research in Computational Topology 2: 211–222. Springer, Cham.
• Stolz, B. J., T. Emerson, S. Nahkuri, M. A. Porter, and H. A. Harrington. 2021. “Topological data analysis of task-based fMRI data from experiments on schizophrenia.” Journal of Physics: Complexity 2 (3): 035006. Authorship: Joint First Author.

2020

• Tymochko, S., Z. New, L. Bynum, E. Purvine, T. Doster, J. Chaput, and T. Emerson. 2020. “Argumentative topology: Finding loop (holes) in logic,” preprint, arXiv, 2020. arXiv:2011.08952.
• Bynum, L., T. Doster, T. H. Emerson, and H. Kvinge. 2020. “Rotational Equivariance for Object Classification Using xView.” IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium: 3684–3687. IEEE.
• Emerson, T. H., J. M. Nichols. 2020. “Fitting local, low-dimensional parameterizations of optical turbulence modeled from optimal transport velocity vectors.” Pattern Recognition Letters 133: 123–128.

2019

• Emerson, T. H., J. A. Edelberg, T. Doster, N. Merrill, and C. C. Olson. 2020. “Generative and encoded anomaly detectors.” 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing: 1–5. IEEE.
• Emerson, T. H., C. C. Olson, and T. Doster. 2020. “Path-Based Dictionary Augmentation: A Framework for Improving k-Sparse Image Processing.” IEEE Transactions on Image Processing 29: 1259–1270.
• Emerson, T. H., C. C. Olson, and A. Lutz. 2020. “Image Recovery in the Infrared Domain via Path-Augmented Compressive Sampling Matching Pursuit.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle, WA, 2020.

• Nichols, J. M., T. H. Emerson, and G. K. Rohde. 2020. “A transport model for broadening of a linearly polarized, coherent beam due to inhomogeneities in a turbulent atmosphere.” Journal of Modern Optics 66 (8): 835–849.

  2018

• Nichols, J. M., T. H. Emerson, L. Cattell, S. Park, A. Kanaev, F. Bucholtz, A. Watnik, T. Doster, and G. K. Rohde. 2018. “Transport-based model for turbulence-corrupted imagery.” Applied Optics 57 (16): 4524–4536.
• Emerson, T. H., T. Doster, and C. C. Olson. 2018. “Path-based background model augmentation for hyperspectral anomaly detection.” 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing: 1–5. IEEE.
• Amenta, N., E. W. Chambers, T. Emerson, R. Glover, K. Turner, and S. Yap. 2018. “Density of Local Maxima of the Distance Function to a Set of Points in the Plane.” Research in Computational Topology: 115–123. Springer, Cham.
• Emerson, T. H., T. Doster, and C. Olson. September. 2018. “Path orthogonal matching pursuit for k-sparse image reconstruction.” 2018 26th European Signal Processing Conference: 1955–1959. IEEE.
• Doster, T., T. Emerson, and C. Olson. 2018. “Path orthogonal matching pursuit for sparse reconstruction and denoising of SWIR maritime imagery.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops: 1161–1168.
• Arn, R. T., P. Narayana, T. Emerson, B. A. Draper, M. Kirby, and C. Peterson. 2018. “Motion segmentation via generalized curvatures.” IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (12): 2919–2932.

2017

• Emerson, T. H., C. C. Olson, and T. Doster. 2017. “A study of the effect of alternative similarity measures on the performance of graph-based anomaly detection algorithms.” Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV 10644: 118–128. SPIE.
• Chambers, E., T. Emerson, C. Grimm, and K. Leonard. 2017. “Exploring 2D shape complexity.” Research in Shape Analysis: 61–83. Springer, Cham.
• Adams, H., T. Emerson, M. Kirby, R. Neville, C. Peterson, P. Shipman, S. Chepushtanova, E. Hanson, F. Motta, and L. Ziegelmeier. 2017. “Persistence images: A stable vector representation of persistent homology.” Journal of Machine Learning Research 18.

2016

• Emerson, T., M. Kirby, C. Peterson, and L. Scharf. 2016. “Reduced dimension estimators in matched subspace detection.” 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing: 1–5. IEEE.

2015

• Emerson, T., M. Kirby, K. Bethel, A. Kolatkar, M. Luttgen, S. O'Hara, P. Newton, and P. Kuhn. 2015. “Fourier-ring descriptor to characterize rare circulating cells from images generated using immunofluorescence microscopy.” Computerized Medical Imaging and Graphics 40: 70–87.