Polsky Center Announces 2020 Fall Cohort of the George Shultz Innovation Fund

Published on Wednesday, September 30, 2020

To date, the George Shultz Innovation Fund has invested $7 million in 62 startups. (Image: iStock.com/ipopba)

The Polsky Center for Entrepreneurship and Innovation at the University of Chicago has announced the companies selected for this year’s fall cohort of the George Shultz Innovation Fund.

The program invests in promising startups from an ecosystem that includes the University of Chicago, Argonne National Laboratory, Fermilab, and the Marine Biological Laboratory.

The mission of the George Shultz Innovation Fund is to help researchers turn their innovations into ventures that advance cutting-edge technologies, generate significant financial returns, and create lasting impact for humankind.

The 2020 fall cohort includes:

NanoPattern Technologies, Inc. is commercializing a patented Quantum Dot ink to enable tricolored microLED displays for the next generation of Augmented Reality applications. For the consumer, NanoPattern ink will enable Augmented Reality headsets that are 1) lighter, 2) brighter, and 3) prevent nausea caused by extended use (AR sickness). Team: Dmitri Talapin (PI), Yu Kambe (Lead), Danielle Chamberlin (Business Lead).

ReAx Biotechnologies is developing new protein activity profiling technologies for next-generation diagnostics and drug development support. ReAx can perform functional analysis of dozens of protein targets in a matter of hours using picograms of biological sample, a paradigm shift relative to legacy methods that require expensive equipment, milligrams of sample, and weeks of analysis. ReAx will discover novel biomarkers for next-generation liquid biopsies and provide pan-pipeline support to pharma partners, improving clinical outcomes for patients. Team: Ray Moellering (PI), Jeff Montgomery (Science Lead), Eric Chapman (Commercial Lead).

Zero-Burden Labs (ZLB) is developing generalizable risk predictors for complex disorders across the human disease spectrum, via AI-enabled pattern discovery in medical encounters. ZBL enables clinicians to achieve improved outcomes with precision personalized interventions where early diagnosis is currently impeded by an incomplete understanding of the disease processes and other technological barriers. Team: Ishanu Chattopadhyay (PI / lead), Jim van Horne (lead), Dmytro Onishchenko (scientist).