A new data science project, “WEREWOLF”, puts powerful modeling tools into the hands of Wisconsin policymakers to create the energy systems of tomorrow.
The fall Crossroads of Ideas series kicks off in the Discovery Building on Tuesday, September 24 at 7:00 pm. WID researchers will be featured throughout the fall series.
Researchers at the Wisconsin Institute for Discovery are co-Principal Investigators and co-Investigators on four UW2020: WARF Discovery Initiative projects.
WID researchers used a collaborative combination of computational and wet lab experimental techniques to find a connection between a transcription factor and a neurodevelopment gene.
WID Director Jo Handelsman and biochemistry professor Ophelia Venturelli are part of a multi-university interdisciplinary team awarded a grant to study information transmission in microbial communities and how biological networks communicate.
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.
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.