Rate My Professor Sara El Alaoui

Sara El Alaoui is an Assistant Professor of Computer Science at San Francisco State University, having joined the Department of Computer Science in 2024. She holds a Ph.D. in Computer Engineering from the University of Nebraska-Lincoln, completed in 2020, where her dissertation, "Routing Optimization in Heterogeneous Wireless Networks for Space and Mission-Driven Internet of Things (IoT) Environments," was supervised by Byrav Ramamurthy. El Alaoui also earned her M.S. in Computer Science from the University of Nebraska-Lincoln in 2015. Before her faculty appointment, she was a postdoctoral researcher at Argonne National Laboratory, where she conducted research in computational biology, applying mathematical modeling and machine learning techniques.

El Alaoui's research specializes in applied machine learning, algorithms, network mathematical modeling, and multiomic data analysis. Her doctoral work focused on optimizing routing in heterogeneous wireless networks, particularly for space networks and mission-driven IoT environments, addressing challenges like diverse communication technologies and limited connectivity. Notable publications include "MARS: A Multi-Attribute Routing and Scheduling Algorithm for DTN-based Space Networks" published in IEEE/ACM Transactions on Networking in 2020, "EAODR: A Novel Routing Algorithm Based on the Modified Temporal Graph Model" in Computer Networks in 2017, "Routing Optimization for DTN-based Space Networks Using a Temporal Graph Model" in 2016, "Multi-layer Energy Savings in Optical Core Networks," and a recent preprint "Simulating Iron Deficiency in Plant Plastids With a Flexible Physics-Informed Neural Network Approach" on bioRxiv in 2025. During her time at the University of Nebraska-Lincoln, she received the Outstanding Master's Thesis Award from the Department of Computer Science and Engineering. At San Francisco State University, El Alaoui supervises undergraduate and graduate student projects on machine learning applications such as tumor detection in medical images and multi-modal classification models. She serves as Co-Principal Investigator for the PINC project and has presented graduate seminars on network optimization using mathematical modeling and machine learning.