About
I am a researcher working at the intersection of causal inference, machine learning, Bayesian modelling, and simulation.
My doctoral research at Queen’s University focuses on learning causal structure for robust and explainable artificial intelligence. Across my work, I am interested in a recurring problem: predictive models can tell us what is associated with an outcome, but many scientific and policy questions require understanding what may happen when we actively change a system.
That motivates my work on causal discovery, causal forecasting, intervention reasoning, uncertainty quantification, and agent-based modelling.
Research interests
- Causal discovery and structure learning
- Temporal causal inference
- Bayesian inference and uncertainty quantification
- Causal forecasting under non-stationarity
- Agentic causal reasoning
- Agent-based modelling and policy simulation
- Causal digital twins
Education
Ph.D., Computing
Queen’s University, Kingston, Ontario
M.Sc., Computer Science, AI Specialization
Lakehead University
Honours B.Sc., Computer Science
Lakehead University
B.Eng., Civil Engineering
Lakehead University