Begum Sunal

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About

Begüm Sunal is a researcher advancing the frontiers of robotic-assisted surgery, with a primary focus on autonomous surgical systems and medical robotics. Her work bridges the gap between machine learning and precision manipulation, particularly in the context of minimally invasive procedures. Sunal’s most notable contribution is her 2021 paper on “Learning Medical Suturing Primitives for Autonomous Suturing,” which has garnered 6 citations. In this work, she pioneered a learning-from-demonstration approach, employing a conditional neural network to generate suturing trajectories that adapt to specific anatomical contexts. This innovation enables users to plan and execute suturing tasks through an intuitive GUI, marking a significant step toward semi-autonomous surgical assistance. Additionally, her 2019 study on “Adaptive Inverse Kinematics of a 9-DOF Surgical Robot” (2 citations) addressed a critical challenge in tele-operation: balancing fine, precise motion with the need for broader coverage during procedures. By proposing a kinematics-based adaptive solution, Sunal enhanced the dexterity and efficiency of multi-degree-of-freedom surgical robots. Her work is foundational for developing more intuitive and capable robotic systems, with direct implications for improving surgical outcomes and reducing operator fatigue.