Research

My Google Scholar page is up to date on publications.

I enjoy studying data with structures and geometry, including low-dimensional embeddings learned from generative ML models. Right now, I am mostly interested in:

• Graph-based learning, network science
• Manifold learning
• Sparse and low-rank data
• Optimal transport, diffusion and flow-based models
• Nonconvex optimization
• Federated learning, privacy-preserving machine learning
• Learning in low-label settings
• Self-supervised learning, representation learning
• Applications: Science of science, speech and audio, sleep (time series data, EHR), bioinformatics

I use tools in signal processing, machine learning, optimization, information theory, network science, and (high-dimensional) statistics.

Mentored students

Current

• Olawumi Olasunkanmi. UNC CS PhD. Co-advised by Stan Ahalt from SDSS and Chris Bizon from RENCI.
• Amartya Banerjee. UNC CS PhD. Co-advised by Caroline Moosmueller in Math.
• Saurav Pandey. UNC CS PhD.

Previous

• Lu Cheng. UCLA Applied Math and Statistics BS. Advised honor’s thesis. Now Data Scientist at Meta.