Asif Rashid
Papers
2
Total Citations
35
H-Index
2
About
Asif Rashid is a researcher at the intersection of advanced manufacturing and surgical robotics, making notable contributions to both fields. His work in directed energy deposition (DED) focuses on optimizing additive manufacturing processes, where he developed machine learning models to predict and improve bead geometry for complex corner structures—a critical challenge in metal 3D printing. This work, published in 2024, has already garnered 26 citations, reflecting its timely impact on the manufacturing community. In the realm of medical robotics, Rashid contributed to the design and modeling of a sub-2 mm steerable neuroendoscopic grasping tool, a continuum robot aimed at enhancing dexterity and safety in minimally invasive procedures like endoscopic third ventriculostomy (ETV). This 2023 paper, with 9 citations, addresses the critical need for compatible end-effectors for compliant surgical robots. By bridging machine learning-driven process optimization with precision surgical tool design, Rashid demonstrates a versatile engineering approach, tackling challenges from manufacturing efficiency to life-saving medical interventions.
Research Focus
Key Achievements
Top Papers
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