Short Bio

Alexander Robey is a postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University, where he is advised by J. Zico Kolter. He is also affiliated with Gray Swan, a start-up that aims to develop AI models resistant to adversarial attacks.

In August of 2024, he received his Ph.D. from the Department of Electrical and Systems Engineering at the University of Pennsylvania, where he was advised by Hamed Hassani and George J. Pappas; his dissertation was titled Algorithms for Adversarially Robust Deep Learning. While at Penn, he was a member of the ASSET Center for Safe, Explainable and Trustworthy Machine Learning, the Warren Center for Network and Data Sciences, and the GRASP Robotics Laboratory. He has previously worked as a Visiting Instructor in the Department of Engineering at Swarthmore College (2024), a research intern at Google Cloud AI (2022-23), and a computational intern at Lawrence Livermore National Laboratory (2017). He holds a B.A. in Mathematics and a B.S. in Engineering from Swarthmore College, both of which were obtained in 2018.

Alexander Robey was named a Rising Star in Adversarial Machine Learning at the NeurIPS 2024 workshop on Frontiers in Adversarial Machine Learning. He was also the recipient of the Best Paper Award for the paper Adversarial Training Should Be Cast as a Non-Zero-Sum Game at the ICML 2023 workshop on Frontiers in Adversarial Machine Learning. In 2023, he was awarded a fellowship from Amazon to support research on fair and trustworthy AI. He also received the Dean's Fellowship from the University of Pennsylvania in 2018 and the Undergraduate Research Fellowship from Swarthmore College in 2016. His work has been covered by WIRED, IEEE Spectrum, and the Washington Post .