Ming-fang Chang
Sr. Software Engineer, Perception, Autonomy at Carnegie Mellon University
California, United States
Summary
🤩
Rockstar🎓
Top SchoolMing-fang Chang is a senior software engineer specializing in perception and autonomy with 11 years of experience bridging robot vision research and production-grade systems. Currently at Rivian and pursuing a PhD at Carnegie Mellon, he focuses on 3D reconstruction, mapping, and deep learning applied to real-world autonomous systems. His background includes research internships at Meta Reality Labs and Argo AI and early industry work on ISP and HDR algorithms for camera pipelines, giving him a rare blend of perception research and low-level imaging expertise. He has contributed to influential open-source projects like CMU's Peloton—improving query execution and MVCC-related behaviors—demonstrating strong systems and database engineering skills alongside robotics research. Notably, he maintains a public portfolio of recent work and experiments, signaling an active commitment to reproducible research and practical demos.
11 years of coding experience
3 years of employment as a software developer
Master's degree Automatic control and robot vision systems, Master's degree Automatic control and robot vision systems at National Taiwan UniversityMaster's degree Robotics, Master's degree Robotics at Carnegie Mellon University
Chinese, English, japanese(jlpt n1)
Github Skills (13)
databases10
indices10
c-language10
postgresql10
cprogramming-language10
indexer10
indexing10
relational-databases10
sql-database10
database10
concurrency9
concurrent9
query-optimization9
Programming languages (4)
Github contributions (5)
cmu-db/peloton
Jun 2015 - Dec 2015
The Self-Driving Database Management System
Role in this project:
Back-end Developer & Database Engineer
Back-end Developer & Database EngineerContributions:382 commits, 180 pushes, 16 branches in 6 months
Contributions summary:Ming-fang focused on enhancing the database system by modifying the query execution and incorporating more functionality. They addressed performance issues by modifying the code for several operations within index scan and merge join methods. The user also implemented additional testing scripts, including those for demonstrating outer joins, and worked on incorporating changes related to the MVCC protocol.
management-systemrdbmsdatabase-management-systemself-drivingdatabase
klekkala/15-441-project-1
Aug 2014 - Nov 2014
Contributions:68 commits in 2 months
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