Zhihui Zhu

Zhihui Zhu works on the mathematical and computational foundations of machine learning and signal processing, including topics like low-rank and tensor data recovery, compressive sensing, optimization theory for deep networks (transformers, in-context learning), and quantum state reconstruction. Much of the work is theoretical, focusing on provable guarantees and efficient algorithms, though applications span wireless communications, remote sensing, structural health monitoring, and quantum information. Prospective students would likely engage with rigorous mathematical analysis of learning and optimization problems alongside applied case studies.

Publication output has remained fairly steady over the last decade, fluctuating between about 15 and 25 papers per year without a clear long-term increase or decline.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026