CISE Seminar: Xin Guo, UC Berkeley

Date: October 23, 2026
Time: 1:30pm – 2:30pm
Location: 665 Commonwealth Ave., CDS 1101

Xin Guo
Coleman Fung Chair Professor in Financial Modeling
UC Berkeley

Signature-Based Time Series Statistical Analysis
Signature transform has recently gained significant attention in the theory of stochastic analysis. In this talk, Xin will discuss how the signature transform can be exploited to address several long standing challenges in analyzing time series data, which are typically non-stationary, nonlinear, and often fragmented, and for which modern deep learning models are inappropriate due to limited interpretability and in principle require large volumes of training data. In particular, she proposes a simple signature-based adaptive Lasso approach that has been successfully developed and implemented in industry. This method addresses many of the challenges mentioned above while demonstrating strong potential for a wide range of applications.

The talk will begin with a brief introduction to the signature transform, which has its origins in topology and has been extensively developed within the rough path theory. She will then review the key properties of the signature transform that are most relevant to the statistical methodology. The talk is intended to be self-contained.

Xin Guo is the Coleman Fung Chair Professor in Financial Modeling in the Department of Industrial Engineering and Operations Research at UC Berkeley. Her research applies probability theory, control theory, and mean-field game theory to financial modeling, with additional work in machine learning for medical applications, supply chain management, and logistics. She holds a Ph.D. in mathematics from Rutgers University and previously held positions at IBM’s Thomas J. Watson Research Center and Cornell University before joining Berkeley.

Faculty Host: Jinglong Zhao
Student Host: Jiatong Guo