Hanan Ather

I am a Member of Technical Staff at the Centre for AI Research and Excellence (CAIRE) at Statistics Canada. I build and deploy AI systems for federal stakeholders, with a focus on evaluation, safety, and security across large language models and agentic systems.

I also develop technical guidance for governing and securely deploying these systems in settings that handle confidential data.

As a member of the Office of Responsible AI, I lead technical reviews of AI systems across government and contribute to frameworks and governance guidance for UNECE, G7 GovAI, and the International Statistical Institute (ISI).

• First author of a paper on governing LLM services that handle confidential data, published in Journal of Official Statistics.

• Won first place in the 2025 IAOS Young Statisticians Prize for LLM-assisted record linkage, published in Statistical Journal of the IAOS.

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

• Received the 2026 Award of Excellence in Research from Statistics Canada’s Modern Statistical Methods and Data Science Branch.

• 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

• Governing Large Language Model Services for Confidential Microdata: A Decision Framework for National Statistical Offices Hanan Ather, Mohammed Haddou, and Saeid Molladavoudi. Journal of Official Statistics, 2026.
• LLM-assisted record linkage: A framework for official statistics Hanan Ather. Statistical Journal of the IAOS, 2026.
• Uncertainty Quantification in ML-Enhanced Statistical Inference — A Practitioner Handbook with Applications in Official Statistics UNECE · 2026 · Contributor
• Master Thesis: Deep Reinforcement Learning
• Law of the Iterated Logarithm
• MapReduce for Parallel Machine Learning