Sankeerthana Satini
Nanyang Technological University, Agency for Science, Technology and Research
Papers
2
Total Citations
12
H-Index
2
About
Sankeerthana Satini is a researcher at the forefront of computer vision and autonomous systems, with a particular focus on object detection and semi-supervised learning. Her work addresses critical challenges in deploying robust perception models for service robots operating in dynamic, real-world environments. In her highly cited survey, “A Survey on Object Detection Performance with Different Data Distributions” (2021), she systematically analyzed how varying data distributions impact detection accuracy—a foundational study that has garnered 9 citations for its practical insights. Building on this, Satini introduced an innovative framework in “Creating Semi-supervised learning-based Adaptable Object Detection Models for Autonomous Service Robot” (2022), demonstrating how limited labeled data can be leveraged to create adaptable models that maintain performance across shifting conditions. This work, with 3 citations, highlights her commitment to bridging the gap between theoretical machine learning and real-world robotics. Satini’s contributions are particularly notable for their emphasis on data efficiency and model generalization, making her research essential for engineers developing cost-effective, scalable autonomous systems. Her findings continue to influence the design of resilient perception pipelines in service robotics.
Research Focus
Key Achievements
Top Papers
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