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 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.

My work in materials science has been the subject of several articles:

- Our recent paper on the intrinsic dimension of hidden activations in Stable Diffusion was mentioned in the Gradient
- Machine Learning Accelerates Development of Advanced Manufacturing Techniques
- Artificial Intelligence Tools for Advanced Manufacturing Processes
- Using AI to Explore Possibilities in Advanced Manufacturing

Most of my work is available at preprints on arXiv.

**How many dimensions are required to find an adversarial example?**

Charles Godfrey, **Henry Kvinge**, Elise Bishoff, Myles Mckay, Davis Brown, Tim Doster, Eleanor Byler

* To appear at the The 3rd Workshop of Adversarial Machine Learning on Computer Vision: Art of Robustness*

Paper

**Fast computation of permutation equivariant layers with the partition algebra**

Charles Godfrey, Michael Rawson, Davis Brown, **Henry Kvinge**

* To appear at the ICLR 2023 Workshop on Physics for Machine Learning*

Paper

**Robustness of edited neural networks**

Davis Brown, Charles Godfrey, Cody Nizinski, Jonathan Tu, **Henry Kvinge**

* To appear at the ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models*

Paper

**Exploring the Representation Manifolds of Stable Diffusion Through the Lens of Intrinsic Dimension**

**Henry Kvinge**, Davis Brown, Charles Godfrey,

* To appear at the ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models*

Paper
### 2022

**Do neural networks trained with topological features learn different internal representations?**

Sarah McGuire, Shane Jackson, Tegan Emerson, **Henry Kvinge**

*NeurIPS Workshop on Symmetry and Geometry in Neural Representations, 122-136, 2023*

Paper

**Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Toward ML-Assisted Advanced Manufacturing**

Scott Howland, Lara Kassab, Keerti Kappagantula, **Henry Kvinge**, Tegan Emerson

*Integrating Materials and Manufacturing Innovation, 1-10, 2023*

Paper

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**Neural frames: A Tool for Studying the Tangent Bundles Underlying Image Datasets and How Deep Learning Models Process Them**

**Henry Kvinge**, Grayson Jorgenson, Davis Brown, Charles Godfrey, Tegan Emerson

Paper

**Convolutional networks inherit frequency sensitivity from image statistics**

Charles Godfrey, Elise Bishoff, Myles Mckay, Davis Brown, Grayson Jorgenson, **Henry Kvinge**, Eleanor Byler

Paper

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

**In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?**

**Henry Kvinge**, Tegan H. Emerson, Grayson Jorgenson, Scott Vasquez, Timothy Doster, Jesse D. Lew

*NeurIPS 2022*

Paper
### 2021

### 2020

### 2019

### 2018

**On the Symmetries of Deep Learning Models and their Internal Representations**

Charles Godfrey, Davis Brown, Tegan Emerson, **Henry Kvinge**

*NeurIPS 2022*

**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)*

**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**

**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*,

**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*,

**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.