PNNL

Tegan Emerson is a senior data scientist and leader of the Mathematics, Statistics, and Data Science group in the National Security Directorate at Pacific Northwest National Laboratory (PNNL). With her background in geometric and topological data analysis, she spent time on PNNL's Mathematics of Data Science Dragontail team, where she researched the intersection of topology, algebra, and geometry in data science with deep learning and artificial intelligence. Additionally, she has explored hyperspectral imaging, overhead image analysis, models of atmospheric turbulence, and materials science. With her knowledge and experience, she is also a lead researcher on the Artificial Intelligence Tools for Advanced Manufacturing Processes project.

Emerson’s research interests include geometric and topological data analysis, dimensionality reduction, image processing, deep learning, and optimization. Application domains have included geospatial intelligence, remote sensing, materials science, biomedical image processing, and natural language processing. Her work has resulted in over 30 peer-reviewed papers and several invited talks. One of Emerson’s passions is growing a collaborative research community in topology, algebra, and geometry in data science. To that end, she has worked closely with two of her PNNL colleagues, Henry Kvinge and Timothy Doster, to organize numerous conference workshops and special sessions, as well as founding new publishing venues for these avenues of exploration in mathematical data science.

• 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

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

• Best Paper Award - Received one of three awards, chosen from more than 175 papers, at the 8th Workshop on Hyperspectral Image and Signal Processing: Evolutions in Remote Sensing, 2016
• Mathematical Problems in Industry Fellowship - Received one of two awards given at the Mathematical Problems in Industry Workshop to continue work on a problem begun during this program, 2015
• NSF-Mathematics Travel Grant - Grant to attend the 2nd annual Heidelberg Laureate Forum as one of twenty members comprising the American delegation, 2014
• Culture of Writing Award - Given by the Department of Mathematics at Oregon State University for outstanding writing in a writing intensive course in mathematics during the 2010-2011 academic year
• Plenary Talk - “Generating Realistic Material Microstructures Using Generative Networks for Advanced Manufacturing.” 1st World Congress on Artificial Intelligence in Materials and Manufacturing, Pittsburgh, Pennsylvania

Generative Invertible Network - Provisional patent

2022

• Emerson, T., G. Jorgenson, H. Kvinge, and C. Olson. 2022. “Random Filters for Enriching the Discriminatory Power of Topological Representations.” 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.” 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.” arXiv preprint 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.” arXiv preprint arXiv:2203.08189. To appear in PMLR Volume Topological, Algebraic, and Geometric Learning.
• Dixon, S., T. Doster, and T. Emerson. 2022. “To fail or not to fail: an exploration of machine learning techniques for predictive maintenance.” Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV (Vol. 12113, pp. 590-599). SPIE.

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.” arXiv preprint 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 (pp. 403-408). IEEE.
• 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 (pp. 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.” arXiv preprint 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 (pp. 3684-3687). IEEE.
• Emerson, T.H. and 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 (pp. 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, pp. 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.
• 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 (pp. 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 (pp. 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 (pp. 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 (pp. 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), pp.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 (Vol. 10644, pp. 118-128). SPIE.
• Chambers, E., T. Emerson, C. Grimm, and K. Leonard. 2017. “Exploring 2D shape complexity.” Research in Shape Analysis (pp. 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 (2017).

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 (pp. 1-5). IEEE.

2015

• Emerson, T., M. Kirby, K. Bethel, A. Kolatkar, M. Luttgen, S. OâĂŹHara, 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.