I build recommendation systems. I’m a Sr. Tech-Lead Manager and AI Research Scientist at Meta, working on ranking and recommendation for Instagram. My recent work spans generative retrieval, LLM-based recommendation, and multimodal content understanding at billion-user scale.
Before Meta I led Shop Ads ranking at TikTok, ran a 30-person ads recommendation team at JD.com, built recruiter search at LinkedIn, and was a research scientist at Yahoo Labs. I hold a PhD from Northeastern University and a BS from Tsinghua University.
This site is where I keep long-form notes on papers and ideas at the intersection of recommender systems and large language models.
Turning viewers into creators, then growing creators into top creators: one flywheel, two engines, and three levers for thinking about where modeling fits in the creation problem on a short-video platform.
A close reading of OPRO, which uses meta-prompts to let LLMs iteratively optimize prompts, and GR2, which builds a recommendation re-ranker via semantic-ID mid-training, reasoning data generation, and DAPO RL.
Notes on five papers: CrossDistil’s cross-task distillation, DMT’s topology-aware training, an enhanced twin-tower retriever, Pinterest’s DHEN for CVR prediction, and Xiaohongshu’s UniNote multimodal embedding.
A close reading of DiffuRec, Flow Matching, and the learning dynamics of LLM finetuning, plus a survey of research directions, key papers, and open bottlenecks for diffusion in recommendation.
A categorized reading report on 41 papers at the intersection of recommender systems and LLMs, spanning Semantic ID generative retrieval, two-tower cold start, ranking scaling, and diffusion models.