Philip Huang is a robotics-focused researcher and engineer with nine years of experience building perception, planning, and learning systems across academic and industry settings. Currently a PhD student and Graduate Research Assistant at Carnegie Mellon, he brings a strong track record from the University of Toronto where he led work on task and path planning, continual model-based reinforcement learning, and coordinated multi-robot flight. His background spans hands-on field robotics (Crazyflie teams, sunflower pollination), leading an object detection and tracking team, and practical ML systems work at Qualcomm accelerating neural networks for Snapdragon SoCs. Philip combines rigorous research with production-minded engineering, often bridging simulation and real-world deployment of robot systems. Based in Pittsburgh, he blends academic depth in robotics and ML with proven leadership on multidisciplinary teams.
8 years of coding experience
5 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of Toronto
Doctor of Philosophy - PhD, Robotics, Doctor of Philosophy - PhD, Robotics at Carnegie Mellon University
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