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Computer Science

I chat with Shengyao Lu, one of the new faculty

October 07, 2026

Lu_Announcement_7Oct26

Can you tell me about your academic journey?

Prior to joining UVic, I received my BSc. and Ph.D. degree at the University of Alberta.

What led you to U of A? and do you find that there are differences between U of A and UVic?

U of A’s strong reputation in Computer Science and Engineering was a huge draw for me. I’d also heard a rumor that because Edmonton winters are so cold, students spend more time inside studying, which builds a great learning atmosphere! Coming from a hometown that freezes in the winter, I actually prefer colder climates over enduring heat most of the year, so Edmonton felt like a great fit. As for the programs, I haven't found major differences between U of A and UVic overall, as both follow the Canadian university system. However, UVic really stands out with its interdisciplinary combined programs, like Psychology + CS or Music + CS. That’s a unique strength of UVic, and it offers a wonderful opportunity for students looking to blend computer science with the arts or sciences.

Can you tell me about your research area?

My research interests primarily lie in human-centered trustworthy AI, graph neural networks (GNNs), reinforcement learning (RL), large language models (LLMs), and graph-native foundation models, with a focus on empowering AI systems with transparent epistemic reasoning and belief repair capabilities, as well as long-term, scalable working memory.

Why did you choose to pursue computer science?

My background was actually in Computer Engineering during my time at U of A, but my research centers on graph reasoning and GNN explainability. Because that work aligns much more closely with Computer Science, joining the CS department here at UVic felt like the natural fit for my research.

With respect to your research: What is (in your opinion) the current state of AI with respect to their ability to reason? I'm also curious about AI's ability to describe their reasoning, e.g. to a non-expert.

In my view, current AI systems still fundamentally rely on 'next-token prediction', predicting the probability of the next word based on a prompt. Because modern LLMs are so massive and trained on vast amounts of data, they convincingly mimic human communication, which leads people to believe they’re actually 'thinking'. However, they still lack true structured reasoning, which can make them feel a bit 'pseudohuman'. Bridging that gap is precisely what my research focuses on. By leveraging graph-structured reasoning and explainability, my goal is to help models move beyond surface-level pattern matching so they can perform structured reasoning—and explain that process clearly to non-experts.

Closing Question: Imagine if you went back in time and could give yourself one piece of advice. What would you say?

Spend more time with the people around me, learn from them, and take more opportunities to travel farther and see more of the world.

Author:  Yun Lu