By combining information from many farms, predictive models and analytic tools can be developed to help producers and consultants navigate, visualize. and analyze the data they are getting from an increasing number of sources to support better management decisions.
A new approach to climate data analysis hopes to improve regional forecasts.
Investigators from WID are among the recipients of the latest round of UW2020 awards.
Laurent Lessard is working to improve the algorithms and computer software that keep the modern world running smoothly.
Karen Schloss and Laurent Lessard are working on a method for matching colors to people’s expectations to send the right message — starting with the best colors for waste and recycling bins.
The new institute, housed at UW–Madison’s Wisconsin Institute for Discovery (WID), will play a key role in the future of data science, developing fundamental techniques for handling increasingly massive data sets in shorter times.
Numerical optimization, especially problems involving real (as opposed to integer or discrete) variables. Includes theory, algorithms, implementation and application.
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.
Interests include energy markets, Climate Policy, international trade, technical change and computational economics.
Theoretical and algorithmic aspects of mixed-integer optimization, with a special emphasis in linear and polynomial functions. Other interests include polyhedral combinatorics and combinatorial optimization.
Algorithmic and interface development for large scale problems in mathematical programming, including links to the GAMS and AMPL modeling languages, and general purpose software such as PATH, NLPEC and FATCOP.