Xitong Zhang

Ph.D. candidate in Computational Mathematics

Michigan State University

Biography

I’m Xitong Zhang, currently pursuing my Ph.D. in Computational Mathematics at Michigan State University. I’m honored to be under the guidance of Dr. Wang, Rongrong and Dr. Hirn, Matthew. My academic journey has been deeply rooted in the study of machine learning and its wide-ranging applications. My research interests are broad, covering topics from the generalization theory to the graph neural networks. Moreover, I’ve also explored the realm of geophysics, with a particular emphasis on data-driven frameworks for seismic data analysis.

Interests

Learning on Graphs
AI for Science
Generalization

Education

Ph.D. candidate in Computational Mathematics

Michigan State University, United States
MSc in Computer Science

Michigan State University, United States, 2020
MSc in Computer Science

Worcester Polytechnic Institute, United States, 2018
BEng in Artificial Intelligence

Hunan University, China, 2016

Experience

Graduate Research/Teching Assistant

Michigan State University

August 2018 – Present Michigan, United States

Machine learning Theory and the applications on Directed Graph Neural Networks, Spatiotemporal Data Mining, Generalization and Regularization.

Machine Learning Engineer Intern

Qualcomm

May 2023 – August 2023 California, United States

Supervised and Unsupervised Image Inpainting and Outpainting by Diffusion Models.

Graduate Research Assistant

Los Alamos National Lab

March 2022 – August 2022 New Mexico, United States

Generated large-scale datasets and Uncertainty Quantification for end-to-end Data-driven Full Waveform Inversion (FWI).

Graduate Research Assistant

Los Alamos National Lab

June 2021 – August 2021 New Mexico, United States

Physics-informed Machine Learning for Full Waveform Inversion (FWI).

Graduate Research Assistant

Los Alamos National Lab

June 2020 – August 2020 New Mexico, United States

GNN-based Earthquake Characterization; Squential Seismic Images Generation.

Graduate Research Assistant

Worcester Polytechnic Institute

October 2017 – May 2018 Massachusetts, United States

Image Segmentation on Live cell Images.

Projects

Inpainting and Outpainting with Diffusion Models

During the Qualcomm 2023 summer internship, we initially delved into unsupervised inpainting and outpainting using the RePaint framework. We enhanced this by introducing a smoothing guidance for improved sampling. To speed up the diffusion process, we then investigated both unsupervised and supervised Wavelet Diffusion Models.

OpenFWI Datasets

OpenFWI is a collection of large-scale, multi-structural benchmark datasets for machine learning driven seismic FWI. We release twelve datasets synthesized from different priors, including one 3D dataset. We also provide baseline experimental results with four deep learning methods, InversionNet, VelocityGAN, UPFWI and InversionNet3D. OpenFWI is the first open-source platform to facilitate data-driven FWI research. It will be actively developed and the datasets are expected to evolve.

PyTorch Geometric Signed Directed

PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. It builds on open-source deep-learning and graph processing libraries. PyTorch Geometric Signed Directed consists of various signed and directed geometric deep learning, embedding, and clustering methods from a variety of published research papers and selected preprints.

FeedGrain Visualization

The system is designed to visualize the US historical feed grain data and gain insights into the supply and the demand of feed grains in domestic market and international trade of the US with import and export partners.

Featured Publications

Xitong Zhang, Avrajit Ghosh, Guangliang Liu, Rongrong Wang

October, 2023

Unlocking Tuning-free Generalization: Minimizing the PAC-Bayes Bound with Trainable Priors

Our proposed tuning-free PAC-Bayes training framework achieves test performance comparable to that of SGD/Adam, even when the latter are optimized through a complete grid search and supplemented with additional regularization terms.

Xitong Zhang, Will Reichard-Flynn, Miao Zhang, Matthew Hirn, Youzuo Lin

October, 2022 JGR: Solid Earth

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization

Earthquake characterization by graph neural networks.

Peng Jin, Xitong Zhang, Yinpeng Chen, Sharon Xiaolei Huang, Zicheng Liu, Youzuo Lin

April, 2022 ICLR

Unsupervised learning of full-waveform inversion: Connecting CNN and partial differential equation in a loop

This paper investigates unsupervised learning of Full-Waveform Inversion (FWI) by integrating PDE and CNN in a loop.

Xitong Zhang, Yixuan He, Nathan Brugnone, Michael Perlmutter, Matthew Hirn

December, 2021 NeurIPS 2021

Magnet: A neural network for directed graphs

We proposed a directed graph neural network based on a complex Hermitian matrix known as the Magnetic Laplacian.

Recent Publications

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Guangliang Liu, Zhiyu Xue, Xitong Zhang, Kristen Johnson, Rongrong Wang (2023). PAC-tuning: Fine-tuning Pre-trained Language Models with PAC-driven Perturbed Gradient Descent. EMNLP 2023.

Zhichao Hou, Xitong Zhang, Wei Wang, Charu C. Aggarwal, Xiaorui Liu (2023). Can Directed Graph Neural Networks be Adversarially Robust?.

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Avrajit Ghosh, He Lyu, Xitong Zhang, Rongrong Wang (2023). Implicit regularization in Heavy-ball momentum accelerated stochastic gradient descent. ICLR 2023 notable top 25%.

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Yanhua Liu, Xitong Zhang, Ilya Tsvankin, Youzuo Lin (2022). Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks.

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Chengyuan Deng, Shihang Feng, Hanchen Wang, Xitong Zhang, Peng Jin, Yinan Feng, Qili Zeng, Yinpeng Chen, Youzuo Lin (2022). OpenFWI: Large-scale Multi-structural Benchmark Datasets for Full Waveform Inversion. NeurIPS.

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