Farinaz Alamiyan Harandi
Papers
2
Total Citations
10
H-Index
2
About
Farinaz Alamiyan Harandi’s research lies at the intersection of robotics, machine learning, and intelligent control, with a particular focus on enabling autonomous systems to perceive and adapt to their environments. Her most cited work, “A new feature selection method based on task environments for controlling robots” (2019, 8 citations), introduces a novel approach that tailors feature selection to the specific demands of a robot’s operating context—a critical step toward more efficient and responsive robotic control. Building on this, her 2018 study “Feature Extraction from Depth Data using Deep Learning for Supervised Control of a Wheeled Robot” (2 citations) demonstrates how deep learning can extract meaningful spatial features from depth sensors, allowing wheeled robots to navigate and perform tasks under supervised guidance. Together, these contributions advance the practical integration of perception and decision-making in robotics, offering scalable solutions for real-world applications. Harandi’s work is particularly notable for bridging theoretical feature-selection frameworks with hands-on robotic implementation, making her research valuable for engineers and researchers developing adaptive, sensor-driven autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2