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Developing Machine Learning Models to Tackle Hard Data Analytics Problems in Knowledge-Rich Domains
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.
News
- May 2026
Congratulations to Xumin Liu for receiving the GCCIS Outstanding Scholar award
- April 2026
Congratulations to Yuansheng Zhu for Successfully Defending His PhD Dissertation
“Toward a Unified Framework for Open World Visual Learning”. Yuansheng has worked as a Principal Data Scientist at Metrical Inc.
- May 2026
ICML 2026 Acceptance
We have two papers accepted at ICML 2026.
- February 2026
CVPR 2026 Acceptance
We have two CVPR papers accepted by CVPR 2026.
Research
Utilizing synergy between human and computer information processing for complex visual information organization and use
NSF IIS Award (~$500K, July 2018- June 2023)
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
Our People
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Qi Yu & Xumin Liu (Lab Directors)
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Ph.D Students
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MS Students
