WID’s Data Science Hub is part of the COVID-19 Data Science Research Group that is interpreting data, using that data to create models, and sharing information and findings.
Discovery Fellow Jim Luedtke, a professor of industrial and systems engineering at the University of Wisconsin-Madison, specializes in stochastic and integer optimization, a natural fit for power systems.
A new data science project, “WEREWOLF”, puts powerful modeling tools into the hands of Wisconsin policymakers to create the energy systems of tomorrow.
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.
Discover Fellow Andreas Velten and collaborators, drawing on the lessons of classical optics, have shown that it is possible to image complex hidden scenes using a projected “virtual camera” to see around barriers.
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 seeks to add to its roster of excellent faculty with two new hires in emerging cutting-edge fields.
WID’s new hubs—Data Science, Multi-Omics, and Illuminating Discovery—represent a new path forward for collaborative research projects and fields.
The Wisconsin Institute for Discovery will launch a suite of hubs designed to bring together researchers from across campus and provide access to specialized tools and resources.
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.
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.