Joint research and resources
Paper collaboration, benchmark design, resource building, and student or lab exchange. Use dingjiandong2@huawei.com for university collaboration.
Research partnerships, industrial research discussions, invited talks, and focused academic exchange.
The best exchanges start from a concrete research problem, benchmark, system question, or publication direction.
Paper collaboration, benchmark design, resource building, and student or lab exchange. Use dingjiandong2@huawei.com for university collaboration.
Recommendation architecture, agent evaluation, retrieval systems, and data-intelligence problems at scale.
Focused talks on recommender systems, LLM agents, data mining, and applied AI systems.
I recruit student research interns into the research directions on this site. Logistics such as location, working mode, and duration are discussed individually during the first conversation.
Reliable recommendation under changing users, catalogs, and deployment constraints; auditable AI retrieval, including AI search, agent skill retrieval, and Semantic-ID interface diagnostics; and data-mining methods for weak and noisy evidence.
Solid machine-learning fundamentals and hands-on Python experience. Interest in recommendation, retrieval, or LLM systems matters more than a specific prior topic; published research or engineering work on large-scale systems is a plus.
Email dingjiandong2@huawei.com with the subject starting "Research Intern Application". Include your CV, the period you are available, one or two research or code samples (links are fine), and the direction you want to work on.
Current and past institutional collaborations.