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.
Writing in Nature Ecology & Evolution, WID’s Seyfullah Kotil and Kalin Vetsigian uncover an assembly mechanism that can lead to the spontaneous formation of microbial communities.
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.
Ten highly innovative projects have been chosen to receive University of Wisconsin–Madison Data Science Initiative funding, including two led by Wisconsin Institute for Discovery investigators.
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.
A new paper in Microbiology and Molecular Biology Reviews describes how the steps of virus reproduction contribute to timing and productivity of cell infection.
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.
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 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.
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.
Algorithms for data mining and machine learning, and their applications to biomedical data, especially clinical and high-throughput genetic and other molecular data. Of particular interest are inductive logic programming (ILP) and other multi-relational learning techniques capable of dealing with complex data points (such as molecules or clinical histories) and producing logical rules.
Dynamics of microbial interactions in natural and synthetic microbial communities. The lab develops protocols for quantifying the community dynamics at the phenotypic and genetic levels, and seek simplified theoretical models that reproduce aspects of the experimentally measured dynamics.
A computational biology group interested in developing statistical computational methods to understand regulatory networks driving cellular functions. The lab works to identify networks under different environmental, developmental and evolutionary contexts, comparing these networks across contexts, and construct predictive models from these networks.
Envisioning new ways to help biologists capture their ideas as models in the larger context of Evolutionary Systems Biology. Our lab aims to improve the quality of these models by quantifying evolution with increasing precision.