I am a Master of Science in Robotics student with a strong foundation in robotics software development, autonomy, and simulation. I specialize in building reliable and intelligent robotic systems by combining knowledge of control theory, dynamics, kinematics, perception, artifical intelligence, and embedded development. My work spans mulit-agent systems, autonomous navigation, simulation frameworks, machine learning, and hardware-software integration. I am passionate about solving complex real-world problems in robotics through systems-level thinking and rigorous engineering practices.
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- Resume 📄
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Developed a ROS2-based system for navigating a maze with the TurtleBot3 Burger. The project integrates computer vision, LiDAR, odometry, PID control, and SLAM to enable the robot to detect road signs, avoid obstacles, and follow dynamic waypoints. Navigation behaviors were built modularly across packages, combining object tracking, environment mapping, and motion control into a full autonomy stack.
Keywords: ROS2, TurtleBot3, SLAM, Computer Vision, LiDAR, PID Control, Autonomous Navigation
Contributed to the software stack for a 500g autonomous sumo-style robot in Georgia Tech’s RoboJackets team. Developed sensor integration pipelines, opponent detection logic, and real-time decision-making systems using C++. Developed and implemented embedded control software in C++ on a Teensy microcontroller, integrating sensor inputs, motor drivers, and actuator control for autonomous operation. Designed and programmed real-time navigation, opponent detection, and strategy logic using state machines and sensor fusion techniques. Led software development using Git, managing feature branches, code reviews, and iterative integration with hardware and electrical teams.
Keywords: C++, Object-Oriented Programming, State Machines, Real-Time Robotics, Embedded Systems, SolidWorks, PCB Design
GitHub Repo ➡️ robowrestling
Predicting airline delays based on historical flight and weather data using regression models. Includes preprocessing pipelines, feature engineering, and model comparisons.
Keywords: Python, scikit-learn, Pandas, Regression Models, Data Visualization
GitHub Repo ➡️ airline_delay_prediction
I currently contribute to the Robotarium at Georgia Tech — a lab focused on swarm robotics and multi-agent systems. The Robotarium project provides a remotely accessible swarm robotics research platform that remains freely accessible to anyone. Currently, Robotics research requires significant investments in terms of manpower and resources to competitively participate. However, we believe that anyone with new, amazing ideas should be able to see their algorithms deployed on real robots, rather than purely simulated. In order to make this vision a reality, we have created a remote-access, robotics lab where anyone can upload and test their ideas on real robotic hardware. Below, I outline my ongoing and future work in the Robotarium.
Migrating the Robotarium backend communication from MQTT to ROS2, improving scalability and compatibility with modern robotics stacks. Developed MATLAB and Python interfaces and built publisher/subscriber and server/client logic to control multiple robots.
Keywords: ROS2, MQTT, MATLAB, Python, Distributed Systems
Integrating distance, INS, and encoder sensors into new robot platforms to enhance real-world perception and simulation fidelity. Tasks include:
- Sensor evaluation and physical integration
- ROS2 driver development and real-time data fusion
- Simulation model creation mimicking sensor behavior
- Deployment on physcial test-bed robots
Expected Outcomes: Accurate sensor-based data for use on a remote-access swarm robotics testbed and improved simulation-to-reality match.
Keywords: ROS2, Sensor Fusion, Hardware Integration, Simulation Fidelity, Robotics Research
Develop and deploy decentralized coverage control algorithms for multi-robot systems operating in GPS-denied environments. The project focuses on achieving effective area coverage using only local sensing and neighbor-to-neighbor communication, eliminating reliance on global tracking or centralized state information. Solutions will be validated in simulation and deployed on the Robotarium to enable scalable, truly decentralized multi-agent behavior.
Key Components
- Decentralized coverage control using local sensing and limited communication
- Local environment representation and density estimation without global position information
- Integration with CBF-based safety logic for collision and constraint enforcement
- Deployment in Python and MATLAB simulation environments
- Real-world deployment on Robotarium ground agents using onboard sensing
Tools: Python, MATLAB, ROS2, Robotarium API, Control Barrier Functions, Local Sensing & Estimation Methods
Keywords: Coverage Control, GPS-Denied Navigation, Decentralized Control, Multi-Robot Systems, Safety-Critical Control
GitHub Repo ➡️ [Coming Soon]