I’m an engineer focused on designing robust robotic systems across embedded hardware, motion control, and electromechanical integration. My academic work at RIT emphasized robotics, artificial intelligence, and machine learning, supported by hands-on experience in multidisciplinary teams.
At RIT, I contributed to the Electric Vehicle Team and the University Rover Challenge Team, building a strong foundation in C/C++, PCB design, Python, and system-level robotics development.
I’m currently a Research Engineer II at the Robotics and Automation Design Lab at Texas A&M University, where I support hardware and software development for robotic manipulators intended for use in spaceflight and mission-critical environments.
Education
Bachelor of Science in Electrical Engineering
Rochester Institute of Technology (RIT), Rochester, NY
Graduated with a 3.86 GPA, summa cum laude.
Member of Tau Beta Pi - Inducted for academic excellence.
View BS DegreeMaster of Science in Electrical Engineering
Rochester Institute of Technology (RIT), Rochester, NY
Focus Area: Robotics and AI/ML
Graduated with a 3.92 GPA.
View MS DegreeRelevant Courses
- Robotic Systems: Comprehensive study of robotic systems covering control systems, locomotion techniques, mobile robot dynamics, sensors, actuators, and robot design tools like SolidWorks, laser cutting, and 3D printing.
- Principles of Robotics†*: Core robotics concepts covering kinematics, sensors, ROS, mobile and arm robots, SLAM, and motor control, with hands-on labs featuring industrial and mobile robot platforms.
- Advanced Robotics†*: Focuses on advanced concepts in robot motion and control, including kinematics and trajectory planning for both mobile and arm robots, as well as localization, perception, and navigation.
- Introduction to Artificial Intelligence: Covers foundational AI concepts, including intelligent agents, classical machine learning, neural networks, reinforcement learning, and ethical considerations.
- AI Explorations†: Advanced topics in artificial intelligence, including intelligent agents, heuristic search, adversarial games, neural networks, reinforcement learning, and multi-agent systems, with a focus on practical applications and ethical considerations.
- Biorobotics†: Focused on biological signal processing and machine learning, covering topics like neural networks, deep learning, transfer learning, and experimental design for robotics applications.
- Deep Learning†: Comprehensive study of neural network architectures and concepts, including CNNs, RNNs, attention mechanisms, self-supervised learning, transformers, domain adaptation, compression, and advanced techniques like generative models, explainable AI, and meta-learning.
- Robot Perception†: Computer vision course focusing on perception applications for robotics. Topics include object detection, semantic and video segmentation, multiple object tracking, trajectory prediction, visual localization, 3D measurement, and applications in autonomous driving and control systems.
*Courses where I also served as a Teaching Assistant
†Graduate-level courses