EAS Doctoral Dissertation Defense by Guancheng Zhou

Wednesday, May 06, 2026 at 10:30am to 12:30pm

EAS Doctoral Dissertation Defense by

by Guancheng Zhou

Date: Wednesday May 6, 2026

Time: 10:30am

Topic: Towards Diagnosis, Fairness, and interpretation of Machine Learning Algorithms

Location: Library 314

Abstract:

A number of machine learning algorithms have delivered superior empirical performance. However, the understanding of their mechanisms has been hampered by the black-box nature of the algorithms. In this proposal, we approach the problem from two different lens. One is visualization, with a data-driven geometry following kernel—the rpf-kernel, which can extract complex and highly nonlinear patterns beyond the usual principal component analysis. The other is the diagnosis perspective. Specifically, we perform a diagnostic analysis to data points under a given algorithm and hope to use this as a proxy to understand the algorithm. Random Forests classification is used as an example algorithm for our study. We borrow two metrics, leverage and influence, from statistics regression to measure the importance of data points, while extending their definition to a small neighborhood of data points. Also studied is a related issue of fairness—whether the algorithm delivers a response that is fair in terms of some given metric, for example the gender of the associated subjects. K-means clustering is studied, and a computational efficient post-algorithm adjustment method is proposed. Experiments show that the proposed method is effective in improving the fairness while maintaining the clustering performance. Variable importance is of major significance in the practice of statistical analysis and model interpretation. However, current methods do not consider the correlation between variables, we proposed a method to solve this problem and obtained a more reasonable variable importance.

ADVISOR(S):  Dr. Donghui Yan, Department of Mathematics

(dyan@umassd.edu)

COMMITTEE MEMBERS:

• Dr. Haiping Xu, Department of Computer& Information Science
• Dr. Hongkang Xu, Department of Accounting & Finance
• Dr. Long Jiao, Department of Computer & Information Science

NOTE:  All EAS Students are ENCOURAGED to attend.

LIB 314
Dr. Donghui Yan
dyan@umassd.edu