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About

Armin Masoumian is a computer vision and robotics researcher whose work spans depth estimation, autonomous systems, and control engineering. He is best known for his influential 2022 review paper, "Monocular Depth Estimation Using Deep Learning," which has accumulated an impressive 156 citations, establishing him as a key synthesizer of knowledge in this rapidly evolving field. His research addresses the critical challenge of extracting precise 3D spatial information from single 2D images — a problem with direct applications in autonomous vehicles and robotics navigation. Beyond survey contributions, Masoumian has advanced practical depth estimation methodologies through multi-scale deep architectures enhanced with curvilinear saliency features, demonstrating a strong commitment to improving real-world performance. His broader research interests extend into robotic control systems, including PID and fuzzy logic controllers for dynamic stabilization problems such as the inverted pendulum, as well as innovative applications of event-based vision sensors for real-time slip detection in tactile sensing. Together, his body of work reflects a versatile researcher bridging theoretical deep learning with hands-on robotics engineering, making meaningful contributions to the intelligent perception systems that underpin next-generation autonomous technologies.