L. Minah Yang

L. Minah Yang

Postdoc at CIMS, NYU

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

One day I will make blog posts!

activities

Rising Stars in Computational and Data Sciences 2022

Workshop, UT Austin’s Oden Institute for Computational Engineering and Sciences, Sandia National Laboratories, and Lawrence Livermore National Laboratory, April 21, 2022

I participated in the 2022 Rising Stars in Computational and Data Sciences workshop. It was a great chance to meet other early career women in computational science, present my work, and discuss openly about academic and research careers with prominent women scientists. I highly recommend postdocs and PhD students graduating soon to apply to participate! Rising Stars

SIAM Committee on Science Policy Spring ‘22

SIAM CSP, Lewis and Burke LLC, April 29, 2022

I met SIAM leaders and other CSP fellows. We listened to representatives from NSF-DMS, DOE-ASCR, NASA, White House OSTP about their priorities and afterwards the committee discussed how those priorities align with SIAM’s needs and goals. We also visited congressional offices to advocate for funding for mathematics research!

AGU22 Session: Data-Driven Subgrid-Scale Parameterizations for Earth System Modeling

Conference, AGU, December 12, 2022

Laura Mansfield, Oliver Dunbar, Aakash Sane, and I are co-convening a session in the nonlinear Geophysics section of the American Geophysical Union.

MPI-M Visit 2023

visit, MPI-M, March 15, 2023

The last leg of the trip was at the Max-Planck Institute of Meterology at Hamburg. I presented my work on the Ides of March, and had the chance to meet face-to-face with our collaborators, Claudia Stephan and Laura Köhler. We learned about the NextGems simulation being done at the institute, which led to my current project.

DataWave Meeting in Boulder 2023

meeting, NWRA, October 26, 2023

The DataWave group had a short 2-day meeting hosted by North West Research Associates. We shared our progress and brainstormed ideas of how to move forward. Important Topics:

AGU23 Session: Data-Driven Subgrid-Scale Parameterizations for Earth System Modeling

Conference, AGU, December 12, 2023

Laura Mansfield, and I are co-convening a session in the nonlinear Geophysics section of the American Geophysical Union. This is the second rendition of this session, which we started in 2022.

portfolio

publications

Rounding Error Analysis of Mixed Precision Block Householder QR Algorithms.

Published in Society for Industrial and Applied Mathematics (SIAM) Journal on Scientific Computing, 2021

This paper is about mixed precision block Householder QR algorithms and their rounding error analyses.

Recommended citation: Yang, L. Minah, Fox, Alyson, Sanders, Geoffrey 2019. “Rounding Error Analysis of Mixed Precision Block Householder QR Algorithms.” Society for Industrial and Applied Mathematics (SIAM) Journal on Scientific Computing, vol. 43, no. 3, pp. A1723–A1753, 2021. https://doi.org/10.1137/19M1296367

The fidelity of exponential and IMEX integrators for wave turbulence: Introduction of a new near-minimax integrating factor scheme.

Published in Journal of Computational Physics, 2021

Highlights: Integrating Factor methods are the better exponential integrator for wave turbulence. A discrete-asymptotic analysis relates difference equations to asymptotic behavior. A new near-minimax rational approximation for the matrix exponential is proposed for use in IF methods. Implicit-Explicit integrators fundamentally treat wave resonances incorrectly.

Recommended citation: Yang, L. Minah, Ian Grooms, and Keith A. Julien. "The fidelity of exponential and IMEX integrators for wave turbulence: introduction of a new near-minimax integrating factor scheme." Journal of Computational Physics 434 (2021): 109992. https://doi.org/10.1016/j.jcp.2020.109992

Machine learning techniques to construct patched analog ensembles for data assimilation.

Published in Journal of Computational Physics, 2021

Highlights: Domains need to be partitioned when constructing analogs for geophysical models. Patches make the training of machine learning models more robust. The use of patches makes in data assimilation can be implemented in parallel. General autoencoders with an affine transformation in the latent space can be used. Patched constructed analogs can approximate ensemble members within DA methods.

Recommended citation: Yang, L. Minah, Ian Grooms, and Keith A. Julien. "The fidelity of exponential and IMEX integrators for wave turbulence: introduction of a new near-minimax integrating factor scheme." Journal of Computational Physics 434 (2021): 109992. https://doi.org/10.1016/j.jcp.2021.110532

talks

Time Integration of Wave Turbulence Problems

Published:

In this talk, Ian Grooms introduced background on wave turbulence, and then I presented my research on methods for time integration of wave turbulence problems using IMEX and exponential integrators. This work led to this paper.

Sampling Strategies for Training Machine Learning Emulators of Gravity Wave Parameterizations

Published:

This talk introduces a sampling strategy designed to overcome set imbalance in high dimensional datasets in regression tasks. In a case study of training emulators of a gravity wave parameterization scheme on a long-tail distributed dataset, we find that this strategy improves the errors at the tail of the distribution except at the extreme end, while maintaining minimal loss of accuracy at the peak of the distribution.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.