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Machine Learning and Data Intensive Computing (Mining)

The Mining Lab aims to build statistical learning models to tackle hard data analytics problems in knowledge-rich domain (e.g., medicine and bioinformatics).

Two central research themes: 
- Developing interpretable machine learning models that analyze large-scale multimodal dynamic data with limited supervised information 
- Keeping humans in the loop for interactive and continuous model improvement.


  • September 2021


    NeurIPS 2021 Acceptance

    One paper has been accepted by NeurIPS 2021. 

  • August 2021


    ICDM 2021 Acceptance.

    We have two papers accepted by ICDM 2021. 

  • 07/22/2021

    ICCV 2021

    ICCV 2021 Acceptance.

    We have two papers accepted by ICCV 2021, including one oral. 

  • 03/30/2021


    Area Chair of NeurIPS.

    Qi will serve as an Area Chair of NeurIPS 2021. 


Student watching eye movements on a computer screen

Utilizing synergy between human and computer information processing for complex visual information organization and use

NSF IIS Award (~$500K, July 2018- June 2022)

Machine Learning Data Model

A Multimodal Dynamic Bayesian Learning Framework for Complex Decision-making

DoD/ONR (~$1.6M, October 2018- September 2022)

Group photo of Qi Yu and students

The Mining lab has multiple PhD and Postdoc positions in the general areas of machine learning and data mining.

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