WID researchers are engaging in fundamental mathematical and statistical research to support the development, testing, and fine-tuning of tools for the future, finding new ways to make sense of the mountains of data that are available in the 21st century and bringing into view important applications on the horizon.
A new tool developed at UW-Madison could save farmers time and money during the fall feed-corn harvest and make for more content, productive cows year-round.
The NRG focuses on signal processing, machine learning, optimization, and statistics. Areas of focus include sparsity and active learning, learning graphs and networks, and interactive machine learning with humans.
Research interests include signal processing, machine learning, and large-scale data science. In particular, methods to leverage low-dimensional models in a variety of contexts.