Youzuo Lin

Scientist & Team Lead for Sensors & Signatures Team @ Los Alamos National Lab, working on Scientific AI, Computational Imaging, Geophysics, and Seismology.

[Kaggle @kaggle]

📣 Competition Launch Alert! Geophysical Waveform Inversion hosted by @Yale & @UNC

🎯 Develop physics-guided ML models to solve full-waveform inversion problems
💰 $50,000 Prize Pool
⏰ Entry Deadline: June 23, 2025

https://t.co/CqHlrCnE32

[Sun Devil MBB @SunDevilHoops]

The Reaction Says it All 🙌

[S. Mostafa Mousavi @smousavi05]

It is interesting to see how the focus of applications of neural networks in seismology has changed over time. #DeepLearningSeismology #AIforEarth #Seismology #Earthquakes #ExplorationSeismology #MachineLearning

[Miao Zhang @SeisMiaoZhang]

Please check out our recent collaborative work in JGR (co-supervised with Dr. Yuozuo Lin @YouzuoLin1). We develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes.

https://t.co/BLM8d4ZVO6

[Youzuo Lin @YouzuoLin1]

Very excited to share our OpenFWI was accepted by #NeurIPS. Great job Chengyuan, Shihang, and everyone contributing to this effort!
OpenFWI: https://t.co/5KLCXyE7bQ

[Youzuo Lin @YouzuoLin1]

I am extremely happy to share that the #ICML2022 submission by us has been accepted for a short presentation.

We show an interesting near-linear relationship after applying the integral transform.

Great job, Yinan & the team!

Here is our work: https://t.co/1cdMngHawJ

[Los Alamos National Laboratory @LosAlamosNatLab]

It wasn't dinosaurs' best day ever. Watch LANL scientist Cathy Plesko talk about #asteroid impacts and how to deflect them on the "Dinosaur Apocalypse" episode of NOVA on @PBS on May 11. https://t.co/7r6uxTXWE7

[Youzuo Lin @YouzuoLin1]

Excited to share our newest review paper on the topic of Physics-Informed Data-Driven Seismic Inversion.

Here is the work: https://t.co/4BupPH5DN9

[Youzuo Lin @YouzuoLin1]

@Tieyuan_Zhu Thanks for the reply! 😀 Like what you said, the quantity and quality of the training set are equally important. Our method learns the physics knowledge (i.e. physics perception) and uses it to make sure the synthetic velocity maps generated would be physically meaningful.

[Youzuo Lin @YouzuoLin1]

You don't have sufficient seismic velocity maps to train your data-driven inversion models?

Hey, our method turns natural images into velocity maps. Check it out below:

Paper: https://t.co/14sgmgKEtd

[Youzuo Lin @YouzuoLin1]

Look forward to the upcoming SEG Workshop on Data Analytics and Machine Learning for Exploration and Production! Here is the agenda to download: https://t.co/xBRwUFebJW

[Youzuo Lin @YouzuoLin1]

@meta_Innovation This seems like very nice functionalities and also a user-friendly GUI. 👍

[Youzuo Lin @YouzuoLin1]

Excited to share our ICLR work "Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential in a Loop", as a poster presentation this year! Nice job, Peng and Xitong! 👍

Paper: https://t.co/tq0LIiz7fn

[Youzuo Lin @YouzuoLin1]

My first Twitter Message!!