We use our resources to invest in research initiatives by College of Engineering faculty who seek to catapult their research in new directions. Seed funding grants and faculty scholar awards provide capacity-building support, encouragement, and connectivity with others. Through research projects, information-sharing activities, and other efforts, we seed a vibrant community of collaborators.
Faculty Scholar Awards
We provide Faculty Scholar Awards, or Assistant Professorships, to help faculty explore new research areas, write proposals, collaborate with colleagues across UW-Madison, and pursue high-caliber students.
Assistant Professor of Mechanical Engineering
“The GIE Faculty Scholar Award has helped me develop my research programs, which focus on manufacturing for design, sustainable smart manufacturing, and industrial internet of things. Thanks to the award, I have been able to meet program directors, collaborators, and fund research projects that were not previously possible. My NSF CAREER award would not have been possible without the Faculty Scholar Award and the support of the GIE proposal team.”
Seed Fund Grants
We seek to invest in pre-competitive research projects by UW-Madison College of Engineering faculty who aim to address new and exciting research challenges. Supported projects have the potential to lay the groundwork for larger scale transformative research in one or more of our critical impact areas. Grants have been awarded for new studies and experiments, the procurement of collaborative equipment, and the creation of seminars and workshops designed for sharing knowledge and ideas.
Machine Learning for Medical Imaging
Varun Jog, Assistant Professor of Electrical and Computer Engineering, in collaboration with Diego Hernando, Assistant Professor of Radiology, used GIE seed funding to address the limitations on the accessibility of deep learning techniques for physicians and researchers who do not have expertise or experience in computer programming, machine learning or data science. By creating an open-source, fully graphical and user-friendly tool, the researchers expect to increase the accessibility of deep learning methods that are proving increasingly important in the detection, classification, and diagnosis of disease. The software tool is on track for public release soon.