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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
- 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.
- January 2023
Area Chair of ICML 2023.
Qi will serve as an Area Chair of ICML 2023.
- January 2023
ICLR 2023 Acceptance
We have one paper accepted by ICLR 2023.
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
