Applied Scientist@Amazon

I am an applied scientist in Kiro Science. Post-train and orchestrate LLM agents for industrial-scale code repositories, with a focus on enabling models to continue learning from agent trajectory and support entereprise modernization workflows (e.g., VMware and .NET migration). I feel very fortunately working with Luka Huan. Keywords: post-train, LLM agent, code graph reasoning, software engineering automation, experiment-driven learning, small–large model collaboration, industrial code migration.

Awards

  • WSDM 2024 Best Paper Honor Mentioned Award (First Author) (3/615)
  • CIKM 2021 Best Short Paper Award (First Author) (1/626)
  • ICML 2024 Spotlight (First Author) (335/9473)
  • EMNLP 2024 best paper recommendation (Second Author)
  • NeurIPS 2024 Top Reviewer Award (Both Main track & DB Track) (0.64%)

Before transitioning to industry role, I spent five years researching the fundamental modeling capabilities on networks. During this journey, two moments stand out as sources of profound inner fulfillment.

  • Pioneering Graph Foundation Models (GFMs) [Research Statement] [Talk Slides]
    GFM is a naive yet intriguing research topic that intersects network science, geometry, and neural-symbolic reasoning, providing a complementary perspective to the existing foundation model paradigm. GFMs may hold future potential for applications in code graphs, scene graphs, and higher-level intelligence, such as analogical reasoning.

  • Serving as the organizer of the [Learning on Graph Conference 2024/25]
    LoG is an exciting new conference facilitating graph and geometry community engagement. Being part of this initiative is exciting.