Anh Truong

Anh Truong is a graduate student in electrical engineering and computer science combining optimization, geometry, and machine learning in service of discovering and exploiting structure in geometric data. His work uncovers opportunities for greater computational efficiency by developing specialized algorithms that exploit the mathematical properties of representations such as neural fields and support functions. Anh discovered that low-rank structure naturally emerges in neural field weights during small edits, enabling 7-8x data compression through low-rank adaptation. In parallel work, he developed a regression-free method to approximate support functions of convex shapes using a variational characterization, establishing theoretical guarantees and enabling valid approximations from simple building blocks. As a MathWorks Fellow, he will expand geometry processing tools to bridge the gap between convex and non-convex geometry. By identifying and exploiting underlying structure in geometric data, Anh could help researchers develop more efficient algorithms with stronger theoretical guarantees.