HanQin Cai
University of Central Florida, FL·Mathematics & Statistics
Modern science and artificial intelligence produce huge datasets with many dimensions at once. Think of a video, a hyperspectral image recording hundreds of colors per pixel, or traffic data across locations and times.
- Fields
Mathematics & Statistics
Computer Science
- Topics
tensor data analysis
tensors
low-rank tensor
- Grants
- 1 known grant, through Jun 2029
What they work on
- Develop scalable tensor algorithms based on mode-wise sampling and low-rank tensor representations to recover useful information from large, partially corrupted or incomplete multidimensional data.
- Prove mathematical guarantees, including convergence, perturbation bounds and sampling reliability, for the proposed randomized and non-convex optimization methods for tensor data.
- Validate the framework through computational studies of traffic anomaly detection and hyperspectral image analysis, assessing efficiency and robustness in high-dimensional settings.
More about the research
These datasets are often noisy, incomplete or partly corrupted, which makes pulling out reliable information hard. Tensors are multidimensional arrays that keep the links across all these dimensions intact. This collaborative project develops fast, robust mathematical and computational tools for analyzing very large tensor datasets.
Why it matters
Reliable, efficient tools for messy large-scale multidimensional data could help scientists and engineers extract trustworthy information from noisy or incomplete measurements. The grant names possible benefits for medical imaging and national security, with test cases in detecting traffic anomalies and analyzing hyperspectral images.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Longxiu Huang
Michigan State University, MI·Mathematics & Statistics
Develop mode-wise sampling methods that analyze large tensors (multidimensional data arrays) by working with small slices along each dimension, and prove that this sampling reliably captures the full data.
Anna Ma
University of California-Irvine, CA·Mathematics & Statistics
Analyze the sample complexity of low-rank tensor completion, meaning how many observed entries are needed to recover a multi-dimensional dataset with simple underlying structure.
Michal Derezinski
University of Michigan, MI·Computer Science·Early-career
Reformulate optimal matrix sketching, the construction of a small randomized approximation of input data, using black-box sampling methods, so that sketch and sub-sample outputs come with reliability guarantees suitable for software.
The school
University of Central Florida: 15 labs on PI Pages, $285M a year on research, 290 research doctorates a year.
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