He research group to present at artificial intelligence conference

Jingrui He
Jingrui He, Associate Professor

Members of Associate Professor Jingrui He's research group, the iSAIL Lab, will present a paper and tutorial at the thirty-fourth Association for the Advancement of Artificial Intelligence (AAAI) Conference, which will take place on February 7-12 in New York. The AAAI meeting is one of the world's leading conferences in the field of artificial intelligence (AI). The event promotes research in artificial intelligence and scientific exchange among researchers, practitioners, scientists, and engineers in affiliated disciplines.

He's lab has been working on a wide range of topics related to AI, machine learning, and data mining, especially in heterogeneous machine learning, rare category analysis, active learning, and semi-supervised learning.  At the AAAI conference, group members Dawei Zhou and Yao Zhou, PhD students in Computer Science, will present their recent work.

Dawei Zhou will give a spotlight presentation regarding a paper he coauthored with He, "Towards Fine-grained Temporal Network Representation via Time-Reinforced Random Walk." In the paper, the researchers propose a fine-grained temporal network embedding framework named FiGTNE, which aims to learn a comprehensive network representation that preserves the rich and complex network context in the temporal network.

Yao Zhou is selected to give a technical tutorial covering the recent advances in machine teaching from the machine perspective to the human perspective. The tutorial will introduce several applications under various teaching settings, including "machine teaches human," "machine teaches machine," and "human teaches machine." For each setting, he will provide a comprehensive review of existing techniques, and discuss the related applications.

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.

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