Haolun Wu 吳昊倫

I am a Ph.D. candidate in Computer Science at McGill University and Mila - Quebec AI Institute. I am delighted in working with Steve Liu, Joelle Pineau, and Laurent Charlin. I also collaborate closely with Fernando Diaz and have him on my supervisory committee. I am currently a visiting scholar working with Sanmi Koyejo at Stanford Trustworthy AI Research (STAIR) Lab. Happy to chat on any related topics.

My research centers on learning from human feedback using ML techniques to make AI systems trustworthy, responsible, and align with human needs. My work on human-AI alignment spans both micro-level aspects (such as personalization and data values) and macro-level aspects (such as social goods and norms). Additionally, I love interdisciplinary research and am particularly interested in applying AI/ML techniques to Education and Psychology. Additionally, I am one of the organizers of the OracleLLM community, where we dive into the frontier of using LLMs as oracles—trusted agents capable of providing reliable, high-level insights. I am also one of the significant contributors to the Test-Time Scaling (TTS) survey, a collaborative effort to understand and push the boundaries of how LLMs can adapt and improve at inference time. I co-founded and co-organize DEFirst, a reading group jointly run at Mila and the Vector Institute, providing an interdisciplinary forum for researchers from academia and industry working on diversity, explainability, and fairness in Information Retrieval, Recommendation, and Trustworthy AI. You can find past talks at our Youtube channel.

During my Ph.D. journey, I interned and collaborated closely with researchers at Microsoft Research, Google Research, and Google DeepMind. My research is generously supported by McGill Graduate Excellence Award, Borealis AI Fellowship, and FRQNT PhD Scholarship (ranked 1st place).

Below is a list of topics covered in part of my projects that I led or highly engaged:

• ▶ 👤 Align AI towards Human Preference
  • User modeling and preference elicitation [NeurIPS'24] [ICDE'24] [SIGIR ICTIR'24] [TOIS'23]
  • Efficient Personalized LLM Adaptation [ICML'25]
  • Data-Centric Learning for Robust Personalization [CIKM'24] [CIKM'22]
• ▶ 🌍 Align AI towards Social Goods
  • Fairness and Inclusivity [SIGIR'22] [TKDE'24] [TOIS'22]
  • Trustworthiness and Transparency [ICML'25] [WWW'25]
  • Knowledge and AI+Education [EMNLP'24] [CHI'24] [AIED'25] [AIED'25]

📑 Curriculum Vitae