Congratulations to Zhiling Gu! Her paper, "TSSS: A Novel Triangulated Spherical Spline Smoothing for Surface-based Imaging" has been awarded the runners-up (second place) in the Theory and Methods track of the 2023 Statistical Methods in Imaging Student Paper Competition. Along with other award winners, she will be giving an oral presentation on it at SMI 2023, on Monday, May 22, 1:10-2:40pm, in the Graduate Hotel, Minneapolis.
Zhiling is advised by Professors Lily Wang (currently at George Mason University) and Dan Nettleton. According to Professor Wang, this paper proposes a novel nonparametric method to efficiently discover the underlying signals on surface-based complex domains. The approach involves a penalized spline estimator defined on a triangulation of surface patches, which ensures both signal matching and smoothness. The proposed method offers several advantages over existing methods, including better handling of complex domains, computational efficiency, and potential applications in analyzing sparse and irregularly distributed data on the surface of complex objects. The superior performance of the method is demonstrated through simulation experiments and data applications on cortical surface functional magnetic resonance imaging data and oceanic near-surface atmospheric data.
Congratulations to Zhiling for a very well-deserved award!