BioE and Materials Science MEng capstone students used Finite Element Analysis to determine if less-invasive hip implants will be superior in reducing stress shielding and preventing unnecessary bone loss while maintaining functionality.
Ricardo Juarez Martinez, current MEng candidate studying Bioengineering with a focus on Biomaterials and Medical Device Design, shares his vision for innovation and inclusivity in the medical device space.
Congratulations to BioE MEng student Diego Espinoza, winner of the 2021 Fung Institute Mission Award for his capstone project, “MEDiRoller: Revolutionizing Low-Cost Vaccine and Drug Delivery in Low-Resource Communities.” The award goes to the team best exemplifying the mission of the institute: Creating inclusive leaders who solve the world’s problems through innovation, technology, and collaboration across boundaries.
Congratulations to BioE MEng student Omokhowa Agbojo who received the 2021 Fung Institute Technical Leadership Capstone Award for his project, “Harnessing Life Cycle Assessment as a Decision-Making Tool for Environmentally Conscious Design of Cell Therapy Manufacturing Processes.”
This MEng capstone team of bioengineering and mechanical engineering students has designed the I-OPener Tube Shunt, a low-cost patient-specific device which accurately regulates intraocular pressure (IOP) using two degradable blockers encased within a microfluidic chamber to improve the failure rate of tube shunt surgery for glaucoma patients.
BioE MEng student Khowa Agbojo talks about her career transition into bioengineering and desire to create opportunities for people in the developing world.
An MEng capstone team of bioengineers and mechanical engineers designed a level-controlled cooling bath to precisely freeze biomaterials as they are printed, providing the bioink with the structural integrity needed to support larger structures and ensuring living cellular material is preserved.
An MEng capstone team of BioE, ME and IEOR students developed a smart device to track patients’ eye drop medication adherence.
This project by a team of Bioengineering and Mechanical Engineering MEng students uses machine learning techniques to allow the EvoWalk to analyze patients’ gait patterns and provide a personalized stimulation algorithm that most efficiently improves their walking outcome.