Tania Rivas

Papers

Total Citations

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About

Tania Rivas is a researcher at the forefront of applying machine learning to critical nuclear nonproliferation and security challenges. Her work focuses on developing intelligent systems for in-field safeguards inspections, where she leverages edge computing to enable real-time, autonomous data analysis without reliance on cloud infrastructure. In her notable 2024 paper, "Machine learning at the edge to improve in-field safeguards inspections," Rivas demonstrates how compact, low-latency algorithms can enhance the detection of undeclared nuclear materials and activities, directly supporting international monitoring efforts. Though early in her career, her contributions are already gaining traction, with her most-cited work accumulating 2 citations as a foundational piece in this emerging niche. By bridging the gap between advanced AI and practical field deployment, Rivas is shaping a future where inspectors can operate with greater speed, accuracy, and security. Her research not only advances technical frontiers but also strengthens global safeguards, making her a promising voice in the intersection of machine learning and nuclear security.