Projects

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 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 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.

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.