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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.


  • 1/30/2021

    aistats 2021

    AISTATS 2021 Acceptance

    Two papers have been accepted by the 24th International Conference on
    Artificial Intelligence and Statistics (AISTATS 2021). 

  • December 2020

    IJCAI 2017

    AAAI 2021 Acceptance

    The paper "A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation" has been accepted by AAAI 2021. 

  • September 2020

    Logo Neural Information Processing Systems

    NeurIPS 2020 Acceptance

    Two papers have been accepted by the Thirty-forth Conference on Neural Information Processing Systems (NeurIPS 2020).

  • August 2020

    Group photo of Qi Yu and students

    Mining Lab Welcomes New Members

    The Mining Lab welcomes three new PhD students joining the lab starting from fall 2020.


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 2021)

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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