Hanan Ather

My Principles

I am a Member of Technical Staff at the Centre for AI Research and Excellence (CAIRE) at Statistics Canada. I build full-stack AI systems and the evals that make them safe to deploy in confidential-data settings, with a focus on LLM evals, AI safety, and AI security. My role involves solving high-impact problems and delivering business value through designing and shipping scalable AI/ML products for internal and external federal stakeholders. My past work involved building tools and infrastructure for uncertainty quantification (conformal prediction, prediction-powered inference) and quality assurance of AI systems at scale. Prior experience in SWE (full-stack), data engineering, and ML systems. I am also a member of the Office of Responsible AI, where I lead technical reviews of AI systems across government and contribute to AI frameworks and governance guidance for UNECE, G7 GovAI, and the International Statistical Institute (ISI).

• Won the 2025 International Association for Official Statistics Young Statisticians Prize for a Bayesian framework integrating LLMs with uncertainty quantification.

• Contributed to the development of a United Nations handbook, Uncertainty Quantification in ML-Enhanced Statistical Inference.

• Master of Science in Mathematics and Statistics at the University of Ottawa

  • Research topic: Deep Reinforcement Learning and Function Optimization

Projects

• I built Probability Lab, an interactive probability and statistics platform currently in use at the University of Ottawa’s second-year engineering program.
• I published a 6-part series of reinforcement learning labs (Jupyter notebooks): Reinforcement Learning Labs.

Graduate Course Notes

• Probability Theory II
• Real Analysis
• Statistical Machine Learning
• Graduate Probability Theory
• Mathematical Statistics II
• Mathematical Statistics I

Papers & Publications

• Master Thesis: Deep Reinforcement Learning
• Law of the Iterated Logarithm
• MapReduce for Parallel Machine Learning