Amal Chakraborty

Virginia Tech, Vellore Institute of Technology University

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About

Amal Chakraborty’s research bridges high-performance computing and autonomous robotics, with a focus on solving complex polynomial systems and advancing mobile robot path planning. In the late 1980s, Chakraborty pioneered work on granularity issues for globally convergent algorithms on hypercube architectures, addressing the critical challenge of efficiently solving polynomial systems that arise in fields like solid modelling, robotics, and chemical engineering. These foundational contributions, cited over 39 times collectively, demonstrated how parallel computing could overcome the limitations of locally convergent methods, ensuring robust, all-solutions convergence. More recently, Chakraborty introduced Beast-RRT*, an improved path planning algorithm that integrates path optimization for mobile robots, showcasing a continued commitment to practical, real-world applications. This work, published in 2025, reflects an enduring ability to adapt classical computational techniques to emerging technologies. Chakraborty’s career exemplifies a rare versatility—from foundational parallel algorithm design to cutting-edge autonomous navigation—making their research a valuable resource for students and engineers tackling computational geometry and robotic motion planning.