A Zuckerman Postdoctoral Fellow at the Harvard John A. Paulson School of Engineering and Applied Sciences, Rana Shahout is also the recipient of the prestigious Eric and Wendy Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences. Her research bridges the theoretical and practical worlds, focusing on the development of novel data structures and probabilistic algorithms for optimizing large-scale machine learning and networking systems. She strongly believes that practicality at a large scale necessitates a solid theoretical foundation, which can only be achieved through a deep understanding of the underlying engineering challenges. Her ultimate goal is to provide practical, reliable, and accessible solutions for network telemetry and data storage management. She hopes these will be applied to industry to enhance big-data solutions and large-scale systems.
Dr. Shahout earned her PhD in Computer Science at Technion–Israel Institute of Technology, where she designed and developed streaming algorithms to provide data analytics for network and database systems. Her solutions reduced space requirements and enhanced system speed.
Dr. Shahout was a mentor for the El Nokhba and WISEMAMA programs. She worked at Cornell Tech, Yahoo! Research, and Mellanox Technologies, a software company in Haifa, Israel.