Sprenger
Five chemical and biological engineering graduate students and one ChBE undergraduate student have received 2024 National Science Foundation Graduate Research Fellowships, a prestigious award that recognizes and supports outstanding students in a wide variety of science-related disciplines.
Assistant Professors Kayla Sprenger and Laurel Hind are on a collaborative mission to explore solutions for mitigating cognitive decline in individuals living with HIV. This decline can be caused by both the virus itself and the antiretroviral drugs used to treat it.
Assistant Professor Kayla Sprenger has been honored with the 2023 Outstanding Partner Award from Boulder's Research & Innovation Office (RIO). The RIO Outstanding Partner Award is an annual honor presented to a campus employee who
Assistant Professors Kayla Sprenger and Ankur Gupta were selected for the prestigious AICHE “35 Under 35” award.- The dean’s office of Boulder's College of Engineering and Applied Science has chosen PhD student Emily Rhodes as the recipient of The Teets Family Endowment in Nano-Technology Graduate Fellowship for the 2022-2023 academic
- Two professors from the Department of Chemical and Biological Engineering were recently honored with AB Nexus Awards, which aim to foster interdisciplinary research collaborations between Anschutz and Boulder. Under
The proliferation of plastic products has created an environmental challenge: what should be done with unusable, discarded plastic waste that can harm the environment? Faculty from the Department of Chemical and Biological Engineering are working on a National Science Foundation (NSF)-funded project, Hydrogenolysis for Upcycling of Polyesters and Mixed Plastics, to address this serious environmental issue.
No universal vaccines exist for infectious diseases like HIV and influenza, largely due to the high frequency with which the pathogens that cause these diseases acquire mutations in their surface proteins. Hear from Assistant Professor Kayla Sprenger as she describes our efforts to address this challenge for HIV using a variety of computational methods that include homology modeling, molecular simulations, mathematical modeling, and machine learning.
