Two research teams tied to the University of Missouri – Kansas City have received proof-of-concept funding support through Comeback KC Ventures and will take the next step toward bringing their innovations from the university lab to market to solve problems related to the COVID-19 pandemic.
The funding from Comeback KC Ventures focuses on the translation of research to a commercial setting: building the technology development plan and the business concept to accompany the sophisticated technology.
“Along with the funding, we aim to support these research teams with outside experts to serve as a braintrust as they map their path to market,” says Chris Rehkamp, associate director of the UMKC Innovation Center’s Technology Venture Studio. “Comeback KC Ventures and these proof-of-concept funds aim to build real pathways to market and amplify the efforts of our local researchers.”
Comeback KC Ventures, funded by a SPRINT Challenge grant from the U.S. Economic Development Administration, wrapped 26 local, early-stage innovations in support, resources, mentorship and financial assistance to accelerate COVID-related solutions. Led by KC Digital Drive and the UMKC Innovation Center, the program is sprinting toward 10 new businesses, 30 new jobs and $5 million in follow-on funding in 18 months.
Meet the next two Comeback KC Ventures fellows
These innovators are commercializing new solutions to problems in measuring the safe health practices of communities and consumer research.
AI2Insight LLC, Dr. Ye Wang and Dr. YugYung Lee
We provide solutions to time-sensitive consumer/user feedback for business/marketing development and evaluation in health care, advertising and campaigning, with a focus on big data from qualitative and mixed methods. Our solutions aim at shortening the research cycle for consumer insight/experience, quantifying a large amount of qualitative data and offering interpretable analytics by leveraging natural language processing and AI/machine learning.
Comeback KC Ventures funding will help the business with crucial research and development on creating a pipeline that connects the database, natural language processing/AI models and user interfaces.
MOSAIC, Dr. Sejun Song
Growing evidence shows that face masks and social distancing can considerably reduce the spread of respiratory viruses like COVID-19. However, the current pandemic trajectory predictions take overly simplified static policy input rather than actual and dynamic observations of practices in a crowd.
MOSAIC (Modeling Safety Index in Crowd) is a vision-based machine-learning system for building a safe community cluster by monitoring and understanding the extent of safety policies (e.g., masking and social distancing) in practice and assessing the safety level in a scalable manner.
“Thanks to the Comeback KC Ventures proof-of-concept funding, we can quickly prototype MOSAIC as a front-end app for intelligent cameras and a smartphone application,” says Sejun Song, associate professor of science and engineering at University of Missouri – Kansas City. “MOSIAC can illustrate each community’s detailed safety levels and trends and predict users’ exposure by applying the routes. The data and experience acquired by the feasibility and usage test of MOSAIC in the field will offer significant societal safety measures against COVID and beyond.”