CUIDS Distinguished Speaker Series- Bayesian Poisson-Randomized Gamma Tensor Factorization with Application to International Trade Flows
The Carleton University Institute for Data Science (CUIDS) invites you to a Distinguished Lecture featuring Jie Jian, Postdoctoral Scholar at the Data Science Institute at the University of Chicago.
Date: Friday, October 2, 2026
Time: 10:00 – 11:00 AM
Location: Room 5345, Herzberg Laboratories, Carleton University
Format: Hybrid
Online: Zoom (Join the lecture via Zoom)
Bayesian Poisson-Randomized Gamma Tensor Factorization with Application to International Trade Flows
Abstract
We study sparse semi-continuous tensor data with excess zeros, heavy right tails, and slice-specific dispersion. Such features arise naturally in monetary-valued multi-way data, such as international trade, where most exporter–importer–product–year cells are zero while positive values are continuous and highly variable.
To model these data, we propose a Bayesian hierarchical tensor factorization model that places a low-rank CP structure on a latent Poisson rate tensor and couples it with a conditional Gamma model for positive outcomes, with rate parameters that can vary across slices within a mode. The model therefore separates the occurrence and magnitude of positive observations while borrowing strength across all tensor dimensions through a shared low-rank latent structure.
To scale posterior inference to large arrays, we develop a hybrid variational–Monte Carlo algorithm that combines efficient coordinate ascent updates with a partially collapsed augmented-data sampler.
Applied to approximately 60 million trade flows, the method surfaces multiway dependence across exporters, importers, products, and years that is difficult to recover from gravity-type or pairwise network analyses, which do not jointly model the product and temporal dimensions.
About the Speaker
Jie Jian is a postdoctoral scholar at the Data Science Institute at the University of Chicago. Her research develops interpretable probabilistic and machine-learning methods for network and tensor data.
These methods are motivated by applications in brain imaging, climate science, international trade and relations, and linguistics.
She completed her PhD in Statistics at the University of Waterloo. Before that, she earned an MSc in Mathematics from Simon Fraser University and a BSc in Statistics from Huazhong University of Science and Technology.
RSVP
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- Date: Friday, October 2, 2026
- Time: 10:00-11:00 AM
- Format: Hybrid, In-Person Location: 5345 Herzberg Laboratories and Zoom (Join the lecture via Zoom)
- Moderator: Prof. Dave Campbell