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Welcome! I am Huiliang Zhang, a PhD candidate in Electrical and Computer Engineering at McGill University, supervised by Prof. Benoit Boulet.
I build reliable AI systems for decision-making in complex workflows. My work spans agentic AI products, applied reinforcement learning, spatial-temporal machine learning, graph learning, energy and control applications, and empirical model evaluation.
My research and product work asks one central question: how can AI systems help people and organizations make better decisions under uncertainty, while remaining measurable, auditable, and deployable? I am interested in academic research, industrial AI systems, and venture-building conversations around agentic AI, decision intelligence, and reliable machine learning.
Focus Areas
- Agentic AI products: building AI work platforms that help people manage long-running knowledge work across tools, documents, and decisions.
- Decision-making for finance: applying learning systems to repeated operational decisions where timing, cost, and accountability matter.
- Spatial-temporal representation learning: learning from long histories of real-world system behavior for forecasting and decision support.
- Graph learning for dynamic systems: modeling relationships across connected systems such as traffic, energy, buildings, and sensors.
- Applied machine learning for energy and control: connecting prediction with action in energy-management and control settings.
- LLM evaluation: measuring model behavior, bias, and reliability across languages and model families.
Education
- McGill University — Ph.D. in Electrical and Computer Engineering, supervised by Prof. Benoit Boulet, Aug 2020 – expected Oct 2026.
- Peking University — M.S. in Computer Science and Electronic Engineering; Outstanding Graduate (2020), Sep 2017 – Jul 2020.
- Xidian University — Bachelor of Engineering in Electronic Engineering (Valedictorian, with highest honor), GPA: 3.80/4.0 (rank 2nd), Sep 2013 – Jun 2017.
Explore
- Focus Areas - a more structured overview of the research and product directions.
- Professional Experience - research, product, academic service, and teaching experience.
- Publications - journal articles, conference papers, and preprints with official links.