Statistics Seminar - Fall 2024

For questions about the seminar schedule, please contact Chong Jin.

September 12

Dr. Sheng Xu, Princeton University

Maximum likelihood for high-noise group orbit estimation and cryo-EM

Motivated by applications to single-particle cryo-electron microscopy (cryo-EM), we study a problem of group orbit estimation where samples of an unknown signal are observed under uniform random rotations from a rotational group. In high-noise regime, we describe a stratification of the Fisher information eigenvalues according to transcendence degrees in the algebra of group invariants. We relate the critical points of the log-likelihood optimization landscape to those of a sequence of moment matching problems. Some examples including a simplified model of cryo-EM will be discussed

Homepage: https://sx67.github.io/

November 14

Dr. Kangjie Zhou, Columbia University 

Dynamic Factor Analysis of High-dimensional Recurrent Events

Recurrent event time data arise in many studies, including biomedicine, public health, marketing, and social media analysis. High-dimensional recurrent event data involving large numbers of event types and observations become prevalent with the advances in information technology. This paper proposes a semiparametric dynamic factor model for the dimension reduction and prediction of high-dimensional recurrent event data. The proposed model imposes a low-dimensional structure on the mean intensity functions of the event types while allowing for dependencies. A nearly rate-optimal smoothing-based estimator is proposed. An information criterion that consistently selects the number of factors is also developed. Simulation studies demonstrate the effectiveness of these inference tools. The proposed method is applied to grocery shopping data, for which an interpretable factor structure is obtained. Based on joint work with Fangyi Chen, Yunxiao Chen and Zhiliang Ying.

Homepage: https://kangjie287.github.io/

December 5

Mr. Guanao Yan, University of California, Los Angeles [Department of Statistics and Data Science]

Advancing Statistical Rigor in Educational Assessment and Single-Cell Omics Using In Silico Control Data

In this talk, I will explore how in silico control data can be used to enhance statistical rigor in two distinct fields: educational assessment and single-cell omics data analysis.

First, I will discuss the application of in silico data in educational contexts to promote fairness in assessment. Specifically, I will highlight my work on developing statistical tools to detect patterns of collusion in online exams. By incorporating in silico data as negative controls, we can quantify errors—such as false positives—ensuring that educational assessments accurately reflect true student performance.

Next, I will address challenges in single-cell data analysis, particularly the complexity of selecting the right tool from over 1,700 available computational methods. One promising solution is the generation and use of synthetic data as positive controls. This approach establishes trustworthy evaluation standards, enabling more accurate method comparisons and providing rigorous evidence to advance our understanding of cellular biology.

Homepage: http://www.guanaoy.com/