Clockwise from top left: Ding Yuan, Ning Yan, Opher Baron, Ronald Kluger, Cindi Morshead, David Sinton and Xiao Yu (Shirley) Wu (supplied images)
Seven faculty members recognized with 2026 Derrick Rossi Innovation Awards
Published: August 18, 2026
Seven University of Toronto researchers have been recognized with 2026 Derrick Rossi Innovation Awards.
The annual awards, inaugurated in 2025, aim to support the translation of groundbreaking research into real-world applications with significant socio-economic impact. They were created with the support of Derrick Rossi, co-founder of mRNA vaccine-maker Moderna and a U of T alum.
"I am absolutely thrilled to see these innovative and potentially transformative proposals receive funding," said Rossi. "This is a big win for science, discovery and biomedical innovation. Kudos to the visionaries and their teams for driving these projects forward."
The recipients of 2026 Derrick Rossi Innovation Awards are:
- Opher Baron, Rotman School of Management – SiMLQ: Automated prescriptive digital twins for health-care systems
- Ronald Kluger, department of chemistry, Faculty of Arts & Science – Production and application of improved oxygen carriers for enhanced ex vivo organ preservation
- Cindi Morshead, department of surgery, Temerty Faculty of Medicine – Transforming stroke recovery: Reprogramming gene therapies for neural repair
- David Sinton, department of mechanical and industrial engineering, Faculty of Applied Science & Engineering – Electrified recovery of critical minerals from sulphide tailings and ores
- Xiao Yu (Shirley) Wu, Leslie Dan Faculty of Pharmacy – Translating a glucose-responsive microneedle patch technology for clinical feasibility and commercialization
- Ning Yan, department of chemical engineering and applied chemistry, Faculty of Applied Science & Engineering – Green lignin-based flame-retardant for composites
- Ding Yuan, Edward S. Rogers Sr. department of electrical and computer engineering, Faculty of Applied Science & Engineering – AI-native platform for automated failure diagnosis and resolution