Arizona State University
MS in Robotics and Autonomous Systems
2024 – Present
GPA: 3.9 / 4
Education
Amrita Vishwa Vidyapeetham
B.Tech in Mechanical Engineering
2020 – 2024
GPA: 8.59 / 10
Experience
ASU NICE Lab
Volunteer
Apr 2025 – Present
Working with the Stretch 2 mobile robot, developing autonomous manipulation and perception algorithms using Python for control and vision‑driven planning. Focus areas include robot kinematics, motion planning, sensor fusion, and real‑time environment mapping to enable robust autonomous operation and intelligent task execution.
Dassault Systèmes
Intern
Feb 2024 – Jul 2024
Created and optimized complex mechanical assemblies for advanced robotics and precision engineering projects using SOLIDWORKS and simulation tools. Modeled and refined over 2,000 components for the U‑Boat Worx NEMO submarine, enhancing design efficiency and supporting detailed virtual prototyping.
Projects
Dynamic Drone Landing
Implemented vision-based flight control on Parrot Mambo drones, enabling dynamic landing and accurate path following. Utilized MATLAB & Simulink for simulations, and to refine algorithms for stable real‑time performance.
3D Depth Estimation
Built an enhanced GRU‑based variant of MVSNet to infer high‑resolution depth maps from unstructured multi‑view images. Achieved precise, scalable 3D scene reconstruction faster and more efficiently than traditional approaches.
Robotic End-Effector Control
Designed a GUI-based MATLAB Application to model, simulate, and control robotic end-effectors. Automated manipulator dynamics and integrated PD, Impedance control for smooth and precise motion.
Balancing of a Two-Wheeled Segway
Modeled and simulated the dynamics of a two‑wheeled Segway robot using state‑space methods. Performed stability analysis to ensure robust balance and motion control.
Motion Dynamics of Flapping Wings
Develeoped a bird-inspired quick-return flap mechanism to enhance flight efficiency. Simulated the mechanism in ADAMS and built a working prototype.
Gear Box Fault Detection
Built a data acquisition and analysis system for gearbox fault detection using LabVIEW. Applied SVC and Random Forest models for reliable classification of fault conditions.