Principal Staff Software Engineer
I am a Senior Staff Software Engineer at LinkedIn. I am the tech lead of the Standardization team and in charge of building Machine Learning models to construct and refine the LinkedIn knowledge graph based on member profiles, company pages and job postings. I am interested in developing Machine Learning models that can extract insights from large scale graphs and text corpus, such as Graph Neural Networks, Pretrained NLP models and so on. Prior to LinkedIn, I obtained my Ph.D degree at Stanford Infolab under the supervision of Jure Leskovec. I also obtained a Master’s degree in Statistics from Stanford University and a Bachelor’s degree in Electrical Engineering from Seoul National University.
Talks and Events
2022 Talk: Graph Neural Networks for the LinkedIn Economic Graph
Graph Neural Networks (GNNs) are AI models that learn embeddings for the nodes in a graph and use the embeddings to perform prediction tasks. In this talk, we present how we developed GNNs for the LinkedIn economic graph. LinkedIn economic graph is a digital representation of the global economy with 1B nodes and 200B edges, consisting of social graphs about members’ connections, activity graphs between members and other economic entities, and knowledge graphs about members’, companies’, job postings’ attributes. By applying GNN to this graph, we can utilize the full potential of the economic graph in many search and recommendation products across LinkedIn. We will share lessons and findings from developing GNNs for various applications around the LinkedIn economic graph. We will explain how we combine graphs such as social graph, activity graph, knowledge graphs into one gigantic heterogeneous graph, and what algorithms we employed for this heterogenous graph. We will present a few case studies, such as how we identify job postings with vague titles and replace them with more specific titles using GNNs.