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David Neiman
Mid-level Robotics Researcher specializing in motion planning and vehicle routing
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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.
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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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Experience
Education
Awards
Skills
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Publications
3 publications
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