He research group presents at KDD2022

Jingrui He
Jingrui He, Associate Professor

Members of Associate Professor Jingrui He's research group, the iSAIL Lab, will present their research at the 28th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2022), which will be held from August 14-18 in Washington, D.C. The conference, hosted by the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share ideas, research, and experiences.

He will be a keynote speaker for the 6th International Workshop on Automation in Machine Learning, which will be held in conjunction with the conference. In her talk, "Towards Understanding Users' Behaviors in Multi-Armed Bandits," she will introduce her recent work on modeling users' behaviors in multi-armed bandits, a machine learning model for sequential decision making.

"My talk will focus on real-world applications, such as recommender systems, online advertising, and healthcare, where the ultimate goal is to satisfy the users' need from various aspects," said He.

Research paper presentations by the iSAIL Lab include:

  • "Comprehensive Fair Meta-Learned Recommender System," presented by PhD student Tianxin Wei
  • "Neural Bandit with Arm Group Graph," presented by PhD student Yunzhe Qi 
  • "Meta-Learned Metrics over Multi-Evolution Temporal Graphs," presented by Computer Science (CS) PhD student Dongqi Fu
  • "Contrastive Learning with Complex Heterogeneity," presented by CS PhD student Lecheng Zheng
  • "Domain Adaptation with Dynamic Open-Set Targets," presented by CS PhD student Jun Wu 

He's general research theme is to design, build, and test a suite of automated and semi-automated methods to explore, understand, characterize, and predict real-world data by means of statistical machine learning. She received her PhD in machine learning from Carnegie Mellon University.