incremental update

Author: Anthony David Gruber

_pages/about.md

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 I am a mathematician interested in basic and applied research questions that arise in the contexts of data science, nonlinear partial differential equations (PDEs), and scientific machine learning.
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-Lately, my focus is primarily on developing structure-informed numerical methods for scientific applications.  Doing this generally involves identifying or reasoning about some abstract mathematical structure which is important for a particular phenomenon, formulating a smart discretization (based on e.g. a finite element or artificial neural network backbone) which takes this structure into account, and writing mathematical software in Python or C++ which implements a proposed approach.  Over time, I've found that my preferred flavor of research problem leaves ample opportunity for geometric ideas, satisfying visuals, and punchy algebraic arguments which are characteristic of my personal style.
+Lately, my focus is primarily on developing structure-informed numerical methods for scientific applications.  Doing this generally involves identifying or reasoning about some abstract mathematical structure which is important for a particular physical phenomenon, formulating a smart discretization (based on, e.g., a finite element or artificial neural network backbone) which takes this structure into account, and writing mathematical software in Python or C++ which implements a proposed approach.  Over time, I've found that my preferred flavor of research problem leaves ample opportunity for geometric ideas, satisfying visuals, and punchy algebraic arguments which are characteristic of my personal style.
 
 <details markdown="1"><summary><b>More about me</b></summary>
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@@ -45,7 +45,7 @@ Broad research keywords which tend to interest me include: scientific machine le
 ![image-right](/files/gifs/knotFandB.gif){: .align-right}{: width="200" } <br><br> Below are some coarse categories containing (some of) my work. Some have their own page for further reading.  (This part of the website is a work-in-progress.) <br><br>
 
 
-Structure-Informed Function Approximation and Model Reduction
+Structure-Informed Model Reduction and Function Approximation
 -----
 <img src="/images/GCNN_recon2.png" style="max-height: 275px; max-width: 325px; margin-right: 16px" align=left>  Due to their high computational cost, scientific studies based on large-scale simulation frequently operate at a data deficit which creates problems inverse to the issues with "big data".  Particularly, there is a need for efficient function approximation and model reduction strategies which can serve as cheap and reliable surrogates for the high-fidelity models used in practical applications.  These projects develop such technology using invariances and other structural considerations as a starting point, allowing for informed surrogates with beneficial behavior.
 

_pages/publications.md

@@ -92,7 +92,12 @@ Recorded here is a list of publications, current through 8/2024.
 1. [Preprint](https://arxiv.org/abs/2405.16305#){: .btn .btn--info .btn--small}{: .align-right}
 <sub> <ins>Anthony Gruber</ins>, Kookjin Lee, Haksoo Lim, Noseong Park, Nathaniel Trask.  
 *Efficiently Parameterized Neural Metriplectic Systems*  
-**Proceedings of the 18th NeurIPS** (2024). </sub>
+**Proceedings of the 13th ICML** (2025). </sub>
+
+1. [Preprint](https://arxiv.org/abs/2305.15616#){: .btn .btn--info .btn--small}{: .align-right}
+<sub> Eugenio Aulisa, <ins>Anthony Gruber</ins>, Magdalena Toda.  
+[Generalized Willmore Energies and Applications.](https://doi.org/10.7546/giq-29-2024-1-10)  
+**Geometry, Integrability and Quantization, vol. 29** (2024). </sub>
 
 1. [Preprint](https://arxiv.org/abs/2305.15616#){: .btn .btn--info .btn--small}{: .align-right}
 <sub> <ins>Anthony Gruber</ins>, Kookjin Lee, Nathaniel Trask.  
@@ -120,7 +125,12 @@ Recorded here is a list of publications, current through 8/2024.
 **Proceedings of the 36th ICML**, PMLR 97:764-772 (2019). </sub>
 
 ## Others
-1. [Here](to_be_included#){: .btn .btn--info .btn--small}{: .align-right}
+1. [Here](https://www.osti.gov/servlets/purl/2462988){: .btn .btn--info .btn--small}{: .align-right}
+<sub> <ins>Anthony Gruber</ins>.  
+*Learning Operators for Structure-Informed Surrogate Models*    
+**tech. rep., Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)** (2024). </sub>
+
+1. [Here](https://www.sandia.gov/app/uploads/sites/210/2024/12/CSRI-2024-proceedings_Final.pdf#page=180){: .btn .btn--info .btn--small}{: .align-right}
 <sub> Arjun Vijaywargia, Shane A. McQuarrie, <ins>Anthony Gruber</ins>.  
 *Tensor Parametric Hamiltonian Operator Inference.*  
 **CSRI Summer Proceedings** (2024). </sub>
@@ -136,6 +146,16 @@ Recorded here is a list of publications, current through 8/2024.
 **TTU Electronic Thesis and Dissertation Repository** (2019). </sub>
 
 ## Submitted Articles
+1. [Preprint](https://arxiv.org/abs/2501.06686#){: .btn .btn--info .btn--small}{: .align-right}
+<sub> Sanghyun Hong, Fan Wu, <ins>Anthony Gruber</ins>, Kookjin Lee.  
+*Understanding and Mitigating Membership Inference Risks of Neural Ordinary Differential Equations.*  
+(under review). </sub>
+
+1. [Preprint](https://arxiv.org/abs/2410.06219#){: .btn .btn--info .btn--small}{: .align-right}
+<sub> Jonas A. Actor, <ins>Anthony Gruber</ins>, Eric C. Cyr, Nathaniel Trask.  
+*Gaussian Variational Schemes on Bounded and Unbounded Domains.*  
+(under review). </sub>
+
 1. [Preprint](/files/preprints/QC_paper.pdf){: .btn .btn--info .btn--small}{: .align-right}
 <sub> <ins>Anthony Gruber</ins>, Eugenio Aulisa.  
 *Quasiconformal Mappings with Surface Domains.*