The Department of Statistics congratulates professor Yuchen Zhou on receiving a Junior Researcher Award at the 2026 ICSA China Conference, recognizing outstanding research by emerging scholars in statistics and data science.
The award honors early-career researchers whose original research demonstrates exceptional promise and innovation in statistical methodology, novel application of statistical methods in interdisciplinary research, or other suitable contributions to statistics and data science. Recipients are selected through a competitive review process and present their work at the annual conference.
Zhou was recognized for the paper, Heteroskedastic Tensor Clustering, which introduces a new computational method for identifying patterns in complex, multi-dimensional data. The approach is designed to improve clustering performance in challenging real-world settings where data contain varying levels of noise, making it more reliable than existing methods across a wide range of applications.
Abstract: Tensor clustering, which seeks to extract underlying cluster structures from noisy tensor observations, has gained increasing attention. One extensively studied model for tensor clustering is the tensor block model, which postulates the existence of clustering structures along each mode and has found broad applications in areas like multi-tissue gene expression analysis and multilayer network analysis. However, currently available computationally feasible methods for tensor clustering either are limited to handling i.i.d. sub-Gaussian noise or suffer from suboptimal statistical performance, which restrains their utility in applications that have to deal with heteroskedastic data and/or low signal-to-noise-ratio (SNR).
To overcome these challenges, we propose a two-stage method, named High-order HeteroClustering (HHC), which starts by performing tensor subspace estimation via a novel spectral algorithm called Thresholded Deflated-HeteroPCA , followed by approximate $k$-means to obtain cluster nodes. Encouragingly, our algorithm provably achieves exact clustering as long as the SNR exceeds the computational limit (ignoring logarithmic factors); here, the SNR refers to the ratio of the pairwise disparity between nodes to the noise level, and the computational limit indicates the lowest SNR that enables exact clustering with polynomial runtime. Comprehensive simulation and real-data experiments suggest that our algorithm outperforms existing algorithms across various settings, delivering more reliable clustering performance.
The 2026 ICSA China Conference was held June 27–29 at Southern University of Science and Technology in Shenzhen, China. The Junior Researcher Award highlights Zhou's contributions to advancing statistical methodology and recognizes the promise of his continued research in the field.
Congratulations to Yuchen on this well-deserved recognition!