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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
- Januray 2025
ICLR 2025 Acceptance
We have two papers accepted by ICLR 2025.
- December 2024
AAAI 2025 Acceptance
We have two papers accepted by AAAI 2025.
- September 2024
NeurIPS 2024 Acceptance
We have FOUR papers accepted by NeurIPS 2024.
- July 2024
KDD 2024 Acceptance
We have a paper accepted by KDD 2024.
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
Qi Yu, PhD
Professor
Graduate Program Director
College of Computing and Information Sciences
Rochester Institute of Technology
