The Quad Fellowship is a recent initiative for students from Australia, India, Japan, the United States, or any of the 10 Southeast Asian Countries – Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, or Vietnam. It aims to boost interdisciplinary scientific and technological innovation while simultaneously building ties among the world's next generation of STEM leaders. Applicants can be prospective graduate students who have already applied to a graduate program or current graduate students at US universities. This year, two U-M alum have been awarded the Quad Fellowship: Quang Vu Dao and Akira Nishii.
Quang is currently a third-year PhD student in Computer Science at Carnegie Mellon University with a research focus on the security of zero-knowledge proof systems in practice, as well as building advanced cryptographic primitives that are secure against quantum computers. He earned his MS in Mathematics from the University of Michigan in 2022.
His research has won a Distinguished Paper Award at IEEE Security & Privacy, and a Best Junior Paper Award at CRYPTO. He hopes to build a future where people have greater control over their personal data and have stronger trust in the integrity of their digital interactions. He is passionate about math and computer science outreach to high school and undergraduate students in both the US and Vietnam.
Akira Nishii graduated from the University of Michigan with Bachelor’s degrees in Cellular and Molecular Biology, Sociology of Health and Medicine, and Chemical Engineering. He is currently pursuing his Master’s in Biomedical Data Science at Stanford University. He has also worked as a data and machine learning engineer at 2seventy bio (now Regeneron Cell Medicines)."
When asked why he applied for the award, Akira noted that it is important for those working at the intersection of AI and biology to consider the social implications of their work, and that by bridging STEM and science policy, he hopes to champion an equitable approach to technological development throughout his professional journey, “In my work, I develop computational models to better understand complex diseases and enhance drug discovery, which I hope will lead to more affordable healthcare solutions. By bridging the gap between machine learning and biology, I hope to ensure that the next generation of medical innovations will be accessible and affordable for everyone.”
