Henry Kvinge

Data Scientist and Mathematician
hjk3[at]uw.edu


I am a mathematician/machine learning researcher at Pacific Northwest National Lab and a Affiliate Assistant Professor in the University of Washington Mathematics Department. My research interests include:

  • Mathematically inspired approaches to representation learning,
  • Evaluating and building tools to improve the robustness of deep learning models,
  • Applications of topology, algebra, and geometry to machine learning and data science,
  • Applications of machine learning to materials science.
I used to study representation theory and still think about it when I can.

I organize the 'Pacific Northwest Seminar on Topology, Algebra, and Geometry in Data Science' at the University of Washington. We are hybrid, please join us!

I am also one of three founding organizers of the Topology, Algebra, and Geometry in Data Science series of Workshops, Conferences, and Journal Special Editions.

Curriculum Vitae

Publications + Preprints


Preprints

Most of my work is available at preprints on arXiv.

2022

On the Symmetries of Deep Learning Models and their Internal Representations
Charles Godfrey, Davis Brown, Tegan Emerson, Henry Kvinge

Making Corgis Important for Honeycomb Classification:
Adversarial Attacks on Concept-based Explainability Tools
Davis Brown, Henry J Kvinge
2022 ICML Workshop on New Frontiers in Adversarial Machine Learning

Random Filters for Enriching the Discriminatory Power of Topological Representations
Tegan Emerson, Grayson Jorgenson, Henry Kvinge, Colin Olson
2022 ICLR Workshop on Geometric and Topological Representation Learning

TopTemp: Parsing Precipitate Structure from Temper Topology
Tegan Emerson, Lara Kassab, Scott Howland, Henry Kvinge, Keerti Sahithi, Kappagantula
2022 ICLR Workshop on Geometric and Topological Representation Learning

Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps
Elizabeth Coda, Nico Courts, Colby Wight, Loc Truong, WoongJo Choi, Charles Godfrey, Tegan Emerson, Keerti Kappagantula, Henry Kvinge
2022 ICLR Workshop on Geometric and Topological Representation Learning

Bundle Networks
Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps
Nico Courts, Henry Kvinge
International Conference on Learning Representations. 2021

Differential Property Prediction
A Machine Learning Approach to Experimental Design in Advanced Manufacturing
Loc Truong, WoongJo Choi, Colby Wight, Lizzy Coda, Tegan Emerson, Keerti Kappagantula, Henry Kvinge
2021 AAAI Workshop on AI for Design and Manufacturing (ADAM)

2021

Hypergraph models of biological networks to identify genes critical to pathogenic viral response
Song Feng, Emily Heath, Brett Jefferson, Cliff Joslyn, Henry Kvinge, Hugh D Mitchell, Brenda Praggastis, Amie J Eisfeld, Amy C Sims, Larissa B Thackray, Shufang Fan, Kevin B Walters, Peter J Halfmann, Danielle Westhoff-Smith, Qing Tan, Vineet D Menachery, Timothy P Sheahan, Adam S Cockrell, Jacob F Kocher, Kelly G Stratton, Natalie C Heller, Lisa M Bramer, Michael S Diamond, Ralph S Baric, Katrina M Waters, Yoshihiro Kawaoka, Jason E McDermott, Emilie Purvine
BMC Bioinformatics

Sheaves as a Framework for Understanding and Interpreting Model Fit
Henry Kvinge, Brett Jefferson, Cliff Joslyn, Emilie Purvine
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops

Multi-dimensional scaling on groups
Mark Blumstein, Henry Kvinge
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops

A Topological-Framework to Improve Analysis of Machine Learning Model Performance
Henry Kvinge, Colby Wight, Sarah Akers, Scott Howland, Woongjo Choi, Xiaolong Ma, Luke Gosink, Elizabeth Jurrus, Keerti Kappagantula, Tegan H Emerson
ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning

Fuzzy Simplicial Networks
A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning
Henry Kvinge, Zachary New, Nico Courts, Jung H Lee, Lauren A Phillips, Courtney D Corley, Aaron Tuor, Andrew Avila, Nathan O Hodas
AAAI Workshop on Meta-Learning and MetaDL Challenge

One Representation to Rule Them All
Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations
Henry Kvinge, Scott Howland, Nico Courts, Lauren A. Phillips, John Buckheit, Zachary New, Elliott Skomski, Jung H. Lee, Sandeep Tiwari, Jessica Hibler, Courtney D. Corley, Nathan O. Hodas

Rotating spiders and reflecting dogs
A class conditional approach to learning data augmentation distributions
Scott Mahan, Henry J Kvinge, Tim Doster

Adaptive Transfer Learning: a simple but effective transfer learning
Jung H Lee, Henry J Kvinge, Scott Howland, Zachary New, John Buckheit, Lauren A Phillips, Elliott Skomski, Jessica Hibler, Courtney D Corley, Nathan O Hodas

DNA: Dynamic Network Augmentation
Scott Mahan, Tim Doster, Henry Kvinge

2020

Prototypical Region Proposal Networks for Few-Shot Localization and Classification
Elliott Skomski, Aaron Tuor, Andrew Avila, Lauren Phillips, Zachary New, Henry Kvinge, Courtney D. Corley, Nathan Hodas
2020 NeurIPS Workshop on Meta-Learning

The center of the twisted Heisenberg category, factorial Schur Q-functions, and transition functions on the Schur graph
Henry Kvinge, Can Ozan Oğuz, Michael Reeks
Journal of Algebraic Combinatorics

Dimensionality Reduction
Sofya Chepushtanova, Elin Farnell, Eric Kehoe, Michael Kirby, Henry Kvinge
Chapter 7 from Data Science for Mathematicians
Tayor and Francis

LWIR compressive sensing hyperspectral sensor for chemical plume imaging
Julia R Dupuis, John P Dixon, Elizabeth Schundler, S Chase Buchanan, JD Rameau, David Mansur, Henry Kvinge, Elin Farnell, Chris Peterson, Michael Kirby
SPIE - Defense + Commercial Sensing

More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing
Henry Kvinge, Elin Farnell, Julia R Dupuis, Michael Kirby, Chris Peterson, Elizabeth C Schundler
SPIE - Defense + Commercial Sensing

A data-driven approach to sampling matrix selection for compressive sensing
Elin Farnell, Henry Kvinge, John P Dixon, Julia R Dupuis, Michael Kirby, Chris Peterson, Elizabeth C Schundler, Christian W Smith
SPIE - Defense + Commercial Sensing

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection
Elin Farnell, Henry Kvinge, Julia R Dupuis, Michael Kirby, Chris Peterson, Elizabeth C Schundler
SPIE - Defense + Commercial Sensing

Mathematical methods for visualization and anomaly detection in telemetry datasets
Manuchehr Aminian, Helene Andrews-Polymenis, Jyotsana Gupta, Michael Kirby, Henry Kvinge, Xiaofeng Ma, Patrick Rosse, Kristin Scoggin, David Threadgill
Interface Focus, The Royal Society

Mathematical methods for visualization and anomaly detection in telemetry datasets
Lucius Bynum, Timothy Doster, Tegan H Emerson, Henry Kvinge
IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium,

2019

Rare geometries: revealing rare categories via dimension-driven statistics
Henry Kvinge, Elin Farnell, Jingya Li, Yujia Chen
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA),

A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data
Henry Kvinge, Henry Kvinge, Michael Kirby, Chris Peterson, Chad Eitel, Tod Clapp
International Workshop on Self-Organizing Maps,

Khovanov’s Heisenberg category, moments in free probability, and shifted symmetric functions
Henry Kvinge, Anthony M Licata, Stuart Mitchell
Algebraic Combinatorics,

2018

Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large datasets
Henry Kvinge, Elin Farnell, Michael Kirby, Chris Peterson
2018 IEEE International Conference on Big Data (Big Data)2018 IEEE International Conference on Big Data (Big Data),

A Combinatorial Categorification of the Tensor Product of the Kirillov-Reshetikhin Crystal B1,1 and a Fundamental Crystal
Henry Kvinge, Monica Vazirani
Algebras and Representation Theory,

Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets
Henry Kvinge, Elin Farnell, Michael Kirby, Chris Peterson
2018 IEEE High Performance extreme Computing Conference (HPEC),

A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction
Henry Kvinge, Elin Farnell, Michael Kirby, Chris Peterson
2018 17th International Symposium on Parallel and Distributed Computing (ISPDC),

Endmember Extraction on the Grassmannian
Elin Farnell, Henry Kvinge, Michael Kirby, Chris Peterson
2018 IEEE Data Science Workshop (DSW 2018),

A Frobenius-Schreier-Sims Algorithm to tensor decompose algebras
Ian Holm Kessler, Henry Kvinge, James B Wilson

Coherent systems of probability measures on graphs for representations of free Frobenius towers
Henry Kvinge


Any opinions expressed here are personal, and do not necessarily reflect those of my employer.

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