Austin Ebel is a third-year Ph.D. Candidate in the ECE department, advised by Brandon Reagen. Prior to coming to NYU, he received a B.S. degree in Electrical Engineering and Computer Science from Columbia University, and spent some time designing ultra-low power, deep learning algorithms, and developing mathematical frameworks for optimal investments in information security. Out of this experience, he has narrowed down his research interests to “the intersection of machine learning and computer architecture,” and hopes to realize this focus by developing a “hardware/software co-design for privacy-preserving machine learning here at NYU.” In 2025, Austin won a Best Paper Award at the ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) for his work on Orion, a novel framework that allows AI models to practically and efficiently operate directly on encrypted data, without the need to decrypt it first. In his free time, Ebel enjoys swimming, exploring NYC (especially for good pizza), and working on several gaming-related projects.
Personal website: austinebel.net