Michael Danielczuk
Robotics Research Scientist at Ambi Robotics
Berkeley, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Danielczuk is a robotics research scientist at Ambi Robotics with 11 years of engineering experience spanning robot motion planning, perception, and manipulation. A UC Berkeley PhD and NSF GRFP fellow trained under Prof. Ken Goldberg, he blends rigorous academic research—on topics from instance segmentation and area contact modeling to mechanical search—with practical system-building for real robots. He has industrial experience applying simulation-learned models to collision-aware planning at NVIDIA and has contributed to open-source geometric tooling by enhancing mesh-slicing capabilities in the widely used trimesh library. A Princeton BSE in electrical engineering and a background building autonomous rovers and imaging systems illustrate his comfort across hardware, control, and learning stacks. Colleagues describe him as someone who scales ideas from simulated datasets to physical robots while keeping an eye for elegant geometric and computational optimizations.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (PhD), Electrical Engineering, Doctor of Philosophy (PhD), Electrical Engineering at University of California, BerkeleyHigh School, High School at The Loomis Chaffee SchoolBachelor of Science in Engineering (BSE), Electrical Engineering, Magna Cum Laude, Bachelor of Science in Engineering (BSE), Electrical Engineering, Magna Cum Laude at Princeton University
English, Italian
Github Skills (6)
mesh10
geometry10
python10
numpy10
testing9
scipy7
Programming languages (6)
Github contributions (5)
mikedh/trimesh
Oct 2018 - Jul 2022
Python library for loading and using triangular meshes.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:11 commits, 9 PRs, 12 comments in 3 years 9 months
Contributions summary:Michael primarily contributed to the `trimesh` library, focusing on implementing and enhancing mesh slicing functionalities. Their work involved adding a new `slice_mesh_plane` function and improving existing slicing methods with optimizations. Additionally, they refactored code to slice with multiple planes, added a cap argument to slice_mesh_plane and included tests. These changes indicate a focus on extending and refining core geometric operations within the library.
pythonmeshtriangular-meshesgeometry
ambi-robotics/ray
Mar 2024 - Mar 2026
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Contributions:32 pushes, 7 branches in 2 years
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