#
Pacific Northwest Seminar on Topology, Algebra, and Geometry in Data Science

Pacific Northwest Seminar on Topology, Algebra, and Geometry in Data Science

## Past Events

- Graph Metanetworks for Processing Diverse Neural Architectures (Derek Lim (MIT)) -
Tue, Feb 6 2024 - Generalization and universality in deep learning (Florentin Guth (NYU)) -
Tue, Jan 30 2024 - Machine learning, toric Fano varieties, and terminal singularities (Sara Veneziale (Imperial College London)) -
Tue, Jan 16 2024 - Global optimization of analytic functions over compact domain (Georgy Scholten, Sorbonne Université) -
Tue, Nov 7 2023 - Towards a deeper theoretical understanding of neural networks (Lechao Xiao, Google Brain) -
Thu, Feb 16 2023 - A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions (Swati Padmanabhan, UW) -
Thu, Feb 9 2023 - What can adversarial examples tell us about similarities between neural networks? (Jacob Springer, Carnegie Mellon University) -
Thu, Feb 2 2023 - Computing Representations for Lie Algebraic Networks (Noah Shutty, Google) -
Thu, Jan 26 2023 - projUNN: efficient method for training deep networks with unitary matrices (Bobak Kiani (MIT)) -
Thu, Oct 27 2022 - Neural Networks with Learned Coarsening for Simplicial Complexes (Sarah McGuire (Michigan State University)) -
Thu, Oct 13 2022 - The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning (Nick Konz (Duke)) -
Thu, Oct 6 2022 - Groups and Symmetries in Statistical Models (Anna Seigal (Harvard)) -
Thu, May 26 2022 - How to choose informative representations of topological features in persistent homology (Gregory Henselman-Petrusek (PNNL)) -
Thu, May 19 2022 - Approximate vector bundles and fiberwise dimensionality reduction (Luis Scoccola (Northeastern)) -
Thu, May 5 2022 - Modeling Many-to-Many Maps (Lizzy Coda (PNNL)) -
Thu, Apr 28 2022 - Riemannian Geometry in Machine Learning (Isay Katsman (Cornell)) -
Thu, Apr 21 2022 - Towards Mechanics of Learning in Neural Networks (Daniel Kunin (Stanford) and Hidenori Tanaka (NTT Physics & Informatics Laboratories)) -
Thu, Apr 14 2022 - Machine Learning Beyond Accuracy (Shibani Santurkar (Stanford)) -
Thu, Apr 7 2022 - Functional dimension of feedforward ReLU neural networks (Kathryn Lindsey, (Boston College)) -
Thu, Feb 10 2022 - Applications of Group Symmetry (Emily King, (Colorado State University)) -
Thu, Feb 3 2022 - DNA: Dynamic Network Augmentation (Scott Mahan (University of California, San Diego)) -
Thu, Jan 27 2022 - A tool for deep learning model interpretability (Davis Brown (Pacific Northwest National Laboratory)) -
Thu, Jan 20 2022 - From Zigzags to Networks: Topological Tools for Time Series Analysis (Sarah Tymochko (Michigan State University)) -
Thu, Jan 13 2022 - Private AI: Machine Learning on Encrypted Data (Kristin Lauter, Facebook AI Research (FAIR)) -
Thu, Dec 2 2021 - TBA (Mitchell Wortsman, UW CSE) -
Thu, Nov 18 2021 - Chirality in Vision (Rekha Thomas, UW Math) -
Fri, Nov 12 2021 - Detecting Short-lasting Topics Using Nonnegative Tensor Decomposition ( Lara Kassab, Colorado State University) -
Thu, Nov 4 2021 - Using the linear geometry of ReLU neural networks to detect out-of-distribution inputs (Grayson Jorgenson, Pacific Northwest National Lab) -
Thu, Oct 28 2021 - Studying relations geometrically and topologically (Michael Robinson, American University) -
Thu, Oct 21 2021 - A Polynomial Time Algorithm for Constructing Equivariant Neural Networks (Marc Finzi, NYU) -
Thu, Oct 14 2021 - Topic Models, Methods, and Medicine (Jamie Haddock, Harvey Mudd College) -
Thu, Oct 7 2021