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Machine Learning and Data Intensive Computing (Mining)

The Mining Lab aims to build statistical models to tackle hard learning problems with limited labels 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.


  • May 2022


    KDD 2022 Acceptance

    We have one paper accepted by KDD (research track) 2022. 

  • April 2022


    IJCAI 2022 Acceptance

    We have one paper accepted by IJCAI 2022 as long oral. 

  • March 2022


    Area Chair of NeurIPS 2022.

    Qi will serve as an area chair of NeurIPS 2022.

  • March 2022


    CVPR 2022 Acceptance

    We have three papers accepted CVPR 2022.


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)


Using Novel Scientific Machine Learning to Revolutionize Computational Methods for High-Energy-Density Physics

DOE-Department of Energy / University of Rochester


Accurate and Efficient Understanding of Dynamic Materials under Extreme Conditions Through Novel Scientific Machine Learning

Center for Matter at Atomic Pressures (CMAP), University of Rochester

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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