Haolun Wu
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 and Fernando Diaz. I also collaborate with Laurent Charlin and Joelle Pineau, and have them on my supervisory committee.
My research is around human-centric AI and broadly touches personalization, evaluation, and responsibility in information access systems and recently LLMs. I am specifically interested in learning from human feedback for personalization as well as in exploring the social benefits of these technologies. Additionally, I love interdisciplinary research and am particularly interested in applying AI/ML techniques to Education and Psychology [CHI'24]. Below is a list of topics covered in some of my projects that I led or highly engaged:
- User modeling and preference elicitation [NeurIPS'24] [ICDE'24] [TOIS'23]
- Data-driven personalized recommendation [CIKM'24] [CIKM'22] [AAAI'21]
- Efficient knowledge discovery and distillation [EMNLP'24] [ICLR'24]
- Alignment to human value and social good (e.g., fairness, diversity, transparency, etc.) [NeurIPS'24] [TKDE'24] [SIGIR ICTIR'24] [SIGIR'22] [TOIS'22]
- LLMs for personalization, RLHF with personalization, ...
One of my recent engaged projects is on improving the scrutability and controllability in information access systems by using natual language as user profile for indicating preference [TEARS]. This approach builds the path to enhancing user control and autonomy in social media and information access platforms.
During my Ph.D. journey, I interned and collaborated closely with Google Research and Microsoft Research. My research is generously supported by McGill Graduate Excellence Award, Borealis AI Fellowship, and FRQNT PhD Scholarship (ranked 1st place).
news
My intern work at Google Research on density-based user representation for multiple interests has been accepted to NeurIPS 2024.
One paper on knowledge extraction using LLMs has been accepted to EMNLP 2024 main conference (Oral, top 7% of all accepted papers).
One survey paper on LLMs for telecommunications has been accepted to IEEE Communications Surveys and Tutorials 2024 (impact factor: 35.6).
One paper on diffusion-based contrastive learning for sequential recommendation has been accepted to CIKM 2024.
One theoretical paper on group-aware search success is accepted to SIGIR ICTIR 2024.
I am ranked in the 1st-place for the prestigious doctoral research scholarship by the Fonds de recherche du Québec – Nature et Technologies (FRQNT) 🎉.
One survey paper on diversity in search and recommendation is accepted to TKDE 2024.
One interdisciplinary collaboration on using linguistic features to reveal learners’ psychological patterns accepted to CHI 2024.
One paper on knowledge distillation accepted to ICLR 2024.
I have the honor of being elected as a Lab Representative at Mila, proudly representing the McGill PhD cohort.
I am selected as one of the 10 recipients of the Borealis AI Fellowship, which aims to advance world-leading AI research across Canada, guiding exceptional students and helping them achieve their research goals.
I am happy to be a student researcher at Google Research, New York. I am in Cicero's team and doing research on Mixture-of-Experts (MoE) and knowledge storage/modeling.
Our paper on multi-interest recommendation, Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation, has been accepted by ACM TOIS 2023.
I start my second MSR x Mila research collaboration with the Alexandria team at MSR Cambridge, UK.
I start my research intern at Google Research, Mountain view. I will join Craig Boutitlier's team and do research on multi-interest retrieval in online recommendation systems.
My first (in-person) conference ever! SIGIR 2023 at Madrid 🇪🇸.
I moved to Montreal.