Biography. I am currently an Associate Professor in the School of Data Science and Society (SDSS) at the University of North Carolina at Chapel Hill. Prior to joining SDSS, I was a Senior Scientist in the Earth and Environmental Science Division at Los Alamos National Laboratory, where I served from 2010 to 2024. I received my Ph.D. in Applied and Computational Mathematics from Arizona State University in 2010.
Research. My current research focuses on scientific machine learning (AI for Science), deep learning, computational methods, and their applications in medical imaging, signal and image Analysis, and geoscience. Specifically, I have worked on ultrasound imaging and cancer detection, subsurface imaging for energy exploration,and time series classification for small earthquake detection.
Openings. Our group welcomes applications year-round for prospective Ph.D. students, summer research interns, and visiting researchers. If you are interested in contributing to cutting-edge research in this field, we encourage you to first complete the Application Form and Apply for the position that best aligns with your background and interests.
August 2025: New NSF project funded: Advancing Scientific Machine Learning for Wave Imaging. Thank you to NSF for the support.
July 2025: New NSF project with our collaborators funded: Quantum Cyberinfrastructure for Geoscience. Thanks to NSF!
July 2025: ICML 2025: Exploring Invariance in Images through One-way Wave Equations. Thanks to our collaborators!
April 2025: Our Kaggle Competition on Geophysical Waveform Inversion is live. Come and join the fun!
February 2025: Honored to join the Editorial Board of the IEEE Open Journal of Signal Processing.
January 2025: Excited to announce that one paper has been accepted by The Leading Edge and another by ICLR 2025.
December 2024: Thrilled that one paper has been accepted by AAAI 2025.
September 2024: Our proposal was awarded by the DOE-ASCR program office!
Here are some of my most recent projects. For a full list of research projects and descriptions, see my complete Publications and Research Page.
OpenFWI: A Collection of Large-scale, Multi-structural Benchmark Datasets for Data-driven Seismic Full-waveform Inversion.
WaveDiffusion: Exploring Full Waveform Inversion via Joint Diffusion in the Latent Space
Physics-Guided Data-Driven Seismic Inversion: Recent Progress and Future Opportunities in Full Waveform Inversion
Mask-enhanced Deep-learning for Prostate Ultrasound Tomography with Narrow Data Acquisition Aperture
EFWI: Multiparameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography
VelocityGAN: Subsurface Velocity Image Estimation Using Conditional Adversarial Networks
DeepDetect: A Cascaded Region-based Densely Connected Network for Seismic Event Detection
Adaptive Filtering for Event Recognition from Noisy Signal: an Application to Earthquake Detection
