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David Neiman
Mid-level Robotics Researcher specializing in motion planning and vehicle routing
Robotics PhD Research13 years experienceMid-LevelRoboticsTransportation & LogisticsEducation
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About
CMU robotics PhD/PhD researcher and former CMU Robotics Club project lead who built a novel Bayes-filter-based system to localize within music so robotic instruments can follow a human’s tempo in real time. Also works on simulation-heavy multi-agent vehicle routing with traffic-signal scheduling, optimizing for real-time performance via profiling, multithreading, and neural-network surrogates for signal control.
Experience
Robotics PhD ResearchCarnegie Mellon University
Teaching Assistant: Introduction to RoboticsCarnegie Mellon University
Teaching Assistant: Robot Kinematics and DynamicsCarnegie Mellon University
Math Research (Master’s Thesis)Carnegie Mellon University
Robotics Extern/PhD ResearchCarnegie Mellon University
Robotics CapstoneCarnegie Mellon University
Computational Modeling of Ant BehaviorNew Jersey Institute of Technology
Education
Carnegie Mellon Universitydoctorate, Robotics (2026)
Carnegie Mellon Universitymaster, Mathematical Sciences (2019)
Carnegie Mellon Universitybachelor, Mathematical Sciences (Additional Major in Robotics) (2019)
Key Strengths
- Led and implemented a robotics club software project end-to-end (project ideation, onboarding, implementation)
- Applied Bayes filtering to real-time musical tempo-following/localization with multiple sensor/input streams
- Performance optimization under real-time constraints (matrix math acceleration, resolution/window tradeoffs)
- Systematic debugging of ROS-based robotics experiments using topic/node inspection
- Pragmatic experimental problem-solving (used motion capture for ground truth; mitigated camera glare issues)
- Advanced algorithmic optimization for multi-agent routing simulations (profiling, multithreading, surrogate neural nets, anytime planning)
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