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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 2024


    ICML 2024 Acceptance

    We have three papers accepted by ICML 2024.

  • December 2023


    AAAI 2024 Acceptance

    We have one paper accepted by AAAI 2024.

  • April 2023


    ICML 2023 Acceptance

    We have FOUR papers accepted by ICML 2023. 

  • January 2023


    AISTATS 2023 Acceptance

    We have one paper accepted by AISTATS 2023. 


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 2023)

Machine Learning Data Model

A Multimodal Dynamic Bayesian Learning Framework for Complex Decision-making

DoD/ONR (~$1.6M, October 2018- September 2023)


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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