Three computer science graduate students have been named Amazon Fellows by the JHU + Amazon Initiative for Interactive Artificial Intelligence, or AI2AI. Drew Prinster, Yiqing Shen, and Yunjuan Wang were selected as 2024–2025 fellows based on their outstanding publication records, research proposals, and mentor support.
Amazon Fellows receive a full stipend, 20% tuition, and student health insurance for the fall and spring semesters and will be nominated for a paid summer internship at Amazon, during which they will gain valuable industry insights and experiences via engagement with Amazon researchers.
Meet the Fellows
Drew Prinster is a fourth-year PhD student advised by Suchi Saria and Anqi Liu; he is expected to graduate in 2026. His research aim is to improve the reliability and regulatability of AI and machine learning systems for high-stakes settings such as health care. Specifically, Prinster primarily focuses on developing statistical tools for black-box AI systems to ultimately communicate to end users whether individual predictions can be trusted and to help monitor post-deployment risks. Prior to attending Johns Hopkins, Prinster received his BS in computer science and mathematics from Yale University in 2021. He grew up in Boulder, Colorado and in his free time enjoys playing soccer, climbing, hiking, playing board games with friends, reading, and drawing.
Yiqing Shen is a third-year PhD student advised by Mathias Unberath. Expected to graduate in 2027, Shen focuses on developing visual foundation models that can tackle real-world challenges; these models aim to understand and interpret visual information in ways that can be applied to various fields, such as health care. Prior to his doctoral studies, Shen earned his bachelor’s degree in mathematics and applied mathematics from Shanghai Jiao Tong University in 2018. Originally from Shanghai, he enjoys playing various kinds of sports during his spare time.
Yunjuan Wang is a sixth-year PhD student advised by Raman Arora and is expected to graduate in 2025. Her research focuses on trustworthy AI, deep learning theory, transfer learning, and theoretical machine learning in general, with a specific focus on the theoretical understanding of robust adversarial learning and building robust models against various attacks. Wang has a bachelor’s degree in computer science from Xi’an Jiaotong University and a master’s degree in computer science from the Johns Hopkins University. Hailing from Tianjin, China, Wang likes to spend her spare time cooking, reading, and traveling.