Data Science for Dynamical Systems
Summer school, Lorentz Center, July 01, 2019
Description Summer School
Summer school, Lorentz Center, July 01, 2019
Description Summer School
Programming competition, SIAM FM21, sponsored by MathWorks, April 01, 2021
Danny Kurban and I participated and placed 4th place in the SIAM FM21 Student programming competition, and the specific problem details are here. The written report is here and below is a short summary of our solution.
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 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!
SIAM CSP, Lewis and Burke LLC, November 09, 2022
This time, we met with representatives from NSF-DMS, DOE-ASCR, DOD, and CDC.
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.
conference, RAI Congress Center, February 26, 2023
It was so nice to attend SIAM CSE again! I presented at MS46 Goal-Oriented and Context-Aware Scientific Machine Learning organized by Steffen Werner and Thomas O’Leary-Roseberry. This was the beginning of my 3-week travel, where I attended this conference and visited collaborators.
visit, DLR, March 06, 2023
After SIAM CSE, I headed over to near Munich to visit Veronika Eyring’s group at the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt).
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.
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:
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.
I am shamelessly plugging in my own poster here:
Short description of portfolio item number 1
Short description of portfolio item number 2
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
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
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
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This presentation discussed round off error analysis of linear algebra subroutines used for the Householder QR decomposition, as well as its implementation for use in graph analysis task. Further work on this topic resulted in this paper.
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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.
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I presented the last chapter of my dissertation to the CAOS seminar at Courant. It was a great chance to meet the CAOS faculty, postdocs, and students as a newcomer to the department!
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This presentation was a part of the Machine Learning for Data Assimilation portion of the ISDA 2021 events.
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I talked about some challenges of training machine learning emulators of an existing gravity wave parameterization scheme at the 2021 SPARC Gravity Wave symposium.
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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.
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, Amherst College, Music Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.