Building systems that enable robots to perceive their environment and take intelligent action
Computer Vision Engineer with deep expertise in perception pipelines, stereo geometry, sensor calibration, and real-time deployment on edge hardware. Based in Austin, TX.
"Research should be designed to have practical applications in real-world scenarios"
Deploying perception systems in production environments across rail, retail, and industrial robotics.
Tools and technologies I use to build perception, robotics, and edge AI systems.
Peer-reviewed work in robotics, deep learning, and computer vision.
Selected projects from my personal time.
Models board and card games as Markov Games to find optimal strategies using the Minimax-Q reinforcement learning algorithm. Handles multiple goal states and probabilistic card draws with PyGame visualization.
Optimizes roller coaster track design using a Genetic Algorithm, balancing excitement variables (G-force, speed, loop count) against safety constraints across age-based demographic groups.
Python and C++ tools to extract synchronized depth and RGB image frames from ROS bag files. Outputs JPG and NumPy formats for downstream ML pipelines, built on OpenCV and CVBridge.
Kaggle competition entry for large-scale image retrieval of landmark photos. Used VGG16 for feature extraction and K-means clustering for nearest-neighbor matching. Ranked 59 out of 132 teams.
Automated in house robot picks up socks using end to end VLA system and 2D Cameras
A hobby — capturing moments through the lens. Follow along on Instagram.
Open to collaborations, consulting, and technical roles in
robotics and computer vision.