PhD · University of Toronto · D3M Lab

Joe Liang Jiazhou

Researching how memory, language, and embodied AI come together — building conversational agents that remember, retrieve, and reason in immersive environments.

Currently PhD, Information Engineering
University UofT · D3M Lab
Location Toronto, ON
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Joe Liang
Joe Liang Toronto, ON · 2026
About

A brief introduction.

I'm a first-year PhD student at the University of Toronto, advised by Prof. Scott Sanner in the Data Driven & Decision Making (D3M) Lab.

My work sits at the intersection of natural language processing, recommender systems, and embodied AI. I build conversational agents that can hold structured memory, retrieve information from a user's lived experience, and reason about goals in the open world — with recent papers at ACL, ICLR, AAAI, EMNLP, and UMAP.

Before the PhD, I completed my MASc in Information Engineering at U of T and a BMath in Computational Mathematics at Waterloo. I'm a recipient of the Ontario Graduate Scholarship and the MIE Teaching Assistant Award.

NLP Conversational RecSys LLM Agents Knowledge Graphs XR / Embodied AI MIP / QPBO PyTorch
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10
peer-reviewed papers
6
at top venues, 2025–26
3
years as TA / head TA
100+
students mentored / term
Research

What I work on.

04 areas
01 / NLP

Conversational
recommendation

How can recommenders ground themselves in multimodal context — scenes, dialogues, items — and respond to natural-language preferences?

02 / AGENTS

Memory for
LLM agents

Structured, addressable memory layers — knowledge graphs and semantic paths — that let agents recall, reason, and stay consistent across long horizons.

03 / XR

Egocentric, situated
information retrieval

Retrieval inside immersive environments: in-situ item labels, scene grounding, and the user's first-person view as a query.

04 / OPT

Optimization
for ML

MIP and QPBO reductions for clustering and prediction problems, with a focus on near-optimal solutions in non-separable spaces.

Latest

Recent news.

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