I'm a robotics engineer completing my Master's in Robotics (ECE concentration) at Northeastern University, graduating April 2026. My journey began with a childhood fascination for how machines move, evolved through strategic foundation-building in Electronics & Communication Engineering at Anna University, and has culminated in specialized expertise in dynamic locomotion and autonomous systems.
What drives me is the challenge of making robots move intelligently through complex environments—whether it's a humanoid navigating urban spaces or a quadruped adapting its gait to rough terrain. I'm particularly excited about the intersection of classical control theory and reinforcement learning, where mathematical rigor meets adaptive intelligence.
Currently seeking: Full-time opportunities in dynamic locomotion, legged robotics, autonomous navigation, and mobile manipulation, starting May 2026. Open to roles across the robotics stack—from controls and planning to perception and integration.
Built my first gesture-controlled robot in high school, sparking a lifelong passion for human-robot interaction and intelligent movement. This early project taught me the fundamentals of sensor integration and real-time control.
Strategic decision to master electronics fundamentals at Anna University's College of Engineering Guindy. Completed projects ranging from agricultural monitoring rovers to continuum robots, each building toward robotics applications. Developed strong embedded systems expertise and hardware-software integration skills.
Research internship at CSIR Central Scientific Instruments Organisation developing portable vaccine storage systems. Experienced real research methodology and discovered passion for systematic problem-solving through sensor fusion and control optimization. This experience solidified my interest in pursuing graduate research.
Led development of vision-based CNC pick-and-place system with machine learning. Integrated computer vision (CNNs, template matching), G-code programming, and Raspberry Pi control. First complete demonstration of perception-action integration and published as bachelor's thesis.
Systematic exploration through 9 graduate courses: Robot Sensing & Navigation, Mobile Robotics, Robot Mechanics & Controls, Legged Robotics, Reinforcement Learning, Verifiable ML, and more. Discovered passion for dynamic locomotion where classical control meets learning-based approaches. Published research on soft robotics manipulation and completed multiple navigation/localization projects.
Working on soft robotics origami arms and in-hand manipulation. Contributing to published research on 4-DoF soft origami robot arm. Developed ROS2 architectures for multi-sensor coordination and proof-of-concept manipulation protocols. This experience clarified my preference for rigid-body dynamic systems over soft robotics.
Graduating with specialized expertise in dynamic locomotion, navigation, and learning-based control. Seeking opportunities to contribute to the next generation of legged robots and autonomous systems. Ready to tackle challenges in humanoid robotics, quadruped locomotion, or mobile manipulation.
Capstone project integrating ML-based object recognition with CNC control. Implemented contour detection, edge detection, and template matching algorithms. Trained CNNs for adaptive object classification enabling dynamic pick-and-place operations. Published as undergraduate thesis.
Key Achievement: Fully automated system with real-time object localization
Implemented autonomous navigation for two TurtleBot3 robots with collaborative mapping. Integrated SLAM with AMCL localization and developed map merging system for unified environment representation. Achieved efficient multi-robot exploration with real-time visualization in RViz.
Key Achievement: Successful multi-robot SLAM coordination
Re-implemented research paper on Fast Spectral Scan Matching for global localization. Combined SVM-based place recognition with particle filtering for coarse-to-fine position estimation. Achieved significant reduction in processing time and memory usage compared to traditional SSM.
Key Achievement: Faster convergence with reduced computational load
Designed 3-finger and 4-finger origami grippers using Kresling structures. Set up robotic testing system with UR3 arm and developed proof-of-concept in-hand manipulation protocol. Used Arduino DUE for precise PWM control of pneumatic actuation.
Key Achievement: Demonstrated feasibility of compliant grasping
Trained MNIST classifier and systematically attacked it using constant offsets, noise, FGSM, and targeted FGSM. Explored vulnerabilities of neural networks to understand safety implications for robotic perception systems. Critical for verifiable ML in safety-critical applications.
Key Achievement: Demonstrated NN vulnerabilities for safety analysis
Designed unique arch-shaped chassis for non-disruptive crop navigation. Integrated soil moisture and humidity sensors with Raspberry Pi control for autonomous monitoring. Demonstrated complete system combining mechanical design, sensor fusion, and IoT connectivity.
Key Achievement: Novel chassis design enabling crop-friendly navigation
Re-implemented Forward-Backward Reachability Analysis for verifying neural network controllers. Applied technique to ground robot and double integrator systems. Demonstrated conclusive safety verification where traditional forward-only approaches yield inconclusive results.
Key Achievement: Improved safety verification for NN controllers
Designed and fabricated continuum robot with infinite DoF using tendon actuation. Developed control algorithms on Arduino UNO and custom PCB shield for servo integration. Explored soft robotic arm concepts for constrained space manipulation.
Key Achievement: Custom hardware-software integration for flexible manipulation
Published: Soft Robotics (Journal) - 2025
My Contribution: Led fabrication through 3D printing and annealing processes. Developed ROS2 launch file architecture for multi-sensor coordination. Integrated motion capture markers for real-time tracking. Contributed to closed-loop control validation achieving <5mm error in 2D and <10mm in 3D paths.
Impact: Demonstrated novel joint arrangements from origami structures for soft robotic arms with everyday manipulation capabilities.
Published: Bachelor's Thesis, Anna University - May 2024
Supervisor: Dr. N. Ramadass, Department of Electronics and Communication Engineering
Overview: Complete autonomous CNC system combining computer vision algorithms (contour detection, Canny edge detection, template matching) with CNN-based object classification. Utilized Raspberry Pi for coordination of vision processing and G-code generation for precise pick-and-place operations.
I'm actively looking for full-time positions starting May 2026 in:
Dynamic Locomotion • Legged Robotics • Autonomous Navigation • Mobile Manipulation • Controls Engineering
Available on F-1 OPT | Open to relocation within the United States