Machine learning uses algorithms to build analytical models, helping computers “learn” from data. It can now be applied to huge quantities of data to create exciting new applications. Researchers at WID are developing new methodology and theory for extracting useful information from data which may be noisy or high-dimensional, contain missing elements, or come from a variety of different sensors or streaming input.
Machine learning at WID plays a key role in problems as diverse as understanding the immune system, finding the funniest cartoon captions, and developing driverless cars.
Machine learning is an important component of WID’s Data Science Hub.
Steven Wright, a Wisconsin Institute for Discovery Fellow and Computer Science professor, has been nominated for a Hilldale Professorship for his pioneering work in continuous optimization and machine learning.
Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE) led by Robert Nowak, UW–Madison professor of electrical and computer engineering and researchers at Ohio State.
Professor of Computer Sciences at WID Stephen Wright and three colleagues were announced winners of the prestigious Test of Time Award at the 2020 Conference on Neural Information Processing Systems.
The Bazaar, happening throughout February, 2021, has the theme Data Science for the Social Good.
The Wisconsin Institute for Discovery is home to the Institute for the Foundations of Data Science, which has received Phase II funding from the National Science Foundation.
Jon Eckhardt, Robert Nowak, and Kevin Ponto were among the recipients of nine mini grants from the American Family Insurance Data Science Institute to advance data science.
Claudia Solís-Lemus and Daniel Pimentel-Alarcón are experts in statistics and machine learning, augmenting WID’s data science expertise.
WID graduate student Arezoo Movaghar was a collaborator in a study that employed machine learning to mine decades of electronic health records of nearly 20,000 individuals.
Researchers at the Wisconsin Institute for Discovery are co-Principal Investigators and co-Investigators on four UW2020: WARF Discovery Initiative projects.
WID’s new hubs—Data Science, Multi-Omics, and Illuminating Discovery—represent a new path forward for collaborative research projects and fields.
A paper published in eLife this week by an interdisciplinary team at WID describes new methods for reproducibly manufacturing brain and spinal cord organoids with strict control over morphogenic and developmental processes.
A new approach to climate data analysis hopes to improve regional forecasts.
Much remains mysterious in the realm of machine learning. The next generation of machine learning algorithms is expected to not only bolster national defense capabilities, but also benefit civilians.
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.
WID researcher Sushmita Roy and collaborators at UW–Madison and the University of Florida will use a $7 million grant from the U.S. Department of Energy to study how some plants partner with bacteria to create usable nitrogen and to transfer this ability to the bioenergy crop poplar.
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.
Machine learning is a form of artificial intelligence by which algorithms are “trained” to analyze new information using existing data. Researchers are using it to identify individuals with a genetic condition known as fragile X premutation.
In a paper in Cell Systems, Sushmita Roy and colleagues develop a probabilistic graphical model-based method, multi-species regulatory network learning that uses a phylogenetic framework to infer regulatory networks in multiple species simultaneously.
WID researchers Stephen Wright and Robert Nowak are part of a UW2020: WARF Discovery Initiative project to create machine learning tools that dramatically reduce the time and cost associated with screening compounds for therapeutic relevance.
Systems Biology researcher Sushmita Roy is leading an effort putting computational methods to work characterizing the gene regulatory networks responsible for cell differentiation.
WID Optimization researchers have partnered with faculty across campus to work on ways to use computers to make better use of human brain power.
Systems Biology researchers Deborah Chasman and Sushmita Roy are using machine learning to identify virus and pathogenicity-specific regulatory networks which may guide the design of effective therapeutics for infectious diseases. The work is described in a recent paper in PLOS Computational Biology.
The big data phenomenon also begun to take hold in and around the city, from institutions like state and city government to the health care industry.
