Curriculum Vitae

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PhD student in Mathematics and Statistics developing distribution-free methods for uncertainty quantification and risk control, with a current focus on the reliability, evaluation, and safety of LLMs and autonomous agents. First-author work at ICML 2026 and three manuscripts under review.

Education

2025 – 2029 (exp.)

Ph.D. in Mathematics and Statistics Direct-entry

McGill University, Montréal, QC

Supervisors: Prof. Masoud Asgharian, Prof. Vahid Partovi Nia

Research: distribution-free uncertainty quantification and risk control; reliability and safety of LLMs and autonomous agents

2016 – 2020

B.Sc. Honours in Statistics and Computer Science

McGill University, Montréal, QC

First-Class Honours Degree

Awards & Scholarships

ISM Graduate Scholarship Institut des sciences mathématiques 2026–2027
ISM Travel Grant Institut des sciences mathématiques 2026
ISM Graduate Scholarship Institut des sciences mathématiques 2025–2026
Graduate Excellence Award McGill University 2025–2029
First-Class Honours Degree McGill University 2020
Science Undergraduate Research Award (SURA) McGill University 2019
James McGill Scholarship McGill University 2016

Publications

^† Equal contribution.

Refereed

Pengqi Liu^†, Zijun Yu^†, Mouloud Belbahri, Arthur Charpentier, Masoud Asgharian, Jesse C. Cresswell. "Beyond Procedure: Substantive Fairness in Conformal Prediction." International Conference on Machine Learning (ICML), 2026. arXiv ↗

Under Review

Zijun Yu^†, Yu Gu^†, Vahid Partovi Nia, Masoud Asgharian. "G-CARB: Graph-Localized Conformal Agent Risk Budgets for Compositional Harm." Under review, 2026.
Yu Gu^†, Zijun Yu^†, Vahid Partovi Nia, Masoud Asgharian. "Pause and Reflect: Conformal Aggregation for Chain-of-Thought Reasoning." Under review, 2026. arXiv ↗
Yu Gu^†, Zijun Yu^†, Chi-Kuang Yeh, Xinyu Wang, Ziyang Song. "Severity-Controlled Prediction Sets for Medication Recommendation." Under review, 2026.

Presentations

"Beyond Procedure: Substantive Fairness in Conformal Prediction." Poster presentation. International Conference on Machine Learning (ICML), Seoul, South Korea, 2026.

Patents & Open-Source Software

Patent application filed (Layer 6 AI, 2026), related to methods in "Beyond Procedure: Substantive Fairness in Conformal Prediction."
llm-in-the-loop-conformal-fairness — open-source release of the evaluation and benchmarking framework for the ICML 2026 paper. github.com/layer6ai-labs/llm-in-the-loop-conformal-fairness ↗

Work Experience

2025 – 2026

Student Researcher Mitacs Internship

Layer 6 AI, Toronto

Conducted research on substantive fairness in conformal prediction, leading to "Beyond Procedure: Substantive Fairness in Conformal Prediction" (ICML 2026) and a filed patent application.
Designed and architected a modular codebase supporting large-scale experiment pipelines, enabling reproducible benchmarking across multiple fairness criteria, datasets, and model families; released open source.
Collaborated with research scientists on theoretical analysis and empirical validation.

2020 – 2024

Software Developer

OneDesk Inc., Montréal

Built a Retrieval-Augmented Generation chatbot pipeline (web crawler, vector database, LLM APIs) and added data analysis and visualization capabilities to the core product.
Diagnosed and resolved memory leaks, race conditions, and data-consistency failures across frontend and backend systems; applied time-series analysis to production logs to audit system performance and identify regressions.
Led and mentored a team of junior developers across multiple concurrent projects.

Technical Skills

ML PyTorch, Hugging Face Transformers, vLLM, llama.cpp

Languages Python, R, Java, TypeScript, SQL

Infrastructure Docker, PostgreSQL, Redis, Git, Proxmox VE