Tianjin Luo
MSRED Candidate, 2008
West New York, New Jersey, United States
Summary
👤
Senior🎓
Top SchoolTianjin Luo is a multidisciplinary professional bridging real estate strategy and applied computer science, currently an MSRED candidate at MIT with over a decade of experience in real estate finance, valuation, development, and bilingual project management. He previously led complex urban and regional planning projects for the American Planning Association China Program and holds an MS in Regional and Environmental Planning from the University of Virginia. Tianjin applies data- and systems-oriented thinking to infrastructure, transportation, and urban policy challenges, blending quantitative valuation with practical project delivery. In parallel he contributes to prominent open-source ML projects such as ONNX and ONNX-MLIR, improving operator schemas, tests, and compiler setup scripts—evidence of a rare cross-disciplinary fluency. His background suggests a knack for translating technical compiler- and model-level improvements into better tooling for real-world modeling and planning workflows. Based in West New York, NJ, he brings a global perspective and bilingual leadership to interdisciplinary teams.
11 years of coding experience
3 years of employment as a software developer
Massachusetts Institute of TechnologyMS, Regional and Environmental Planning, MS, Regional and Environmental Planning at University of Virginia
Github Skills (14)
machine-learning10
c-language10
onnx10
cprogramming-language10
mlr10
build-automation9
compiler-development9
deep-neural-networks9
deep-learning9
python8
tensorflow7
pytorch7
protobuffer6
protobuf6
Programming languages (8)
Github contributions (5)
onnx/onnx-mlir
Mar 2020 - Jun 2021
Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure
Role in this project:
Back-end Developer
Back-end DeveloperContributions:104 reviews, 88 commits, 237 PRs in 1 year 4 months
Contributions summary:Tianjin's commits primarily involve modifying the `install-mlir.sh` script, indicating involvement in setting up or updating the MLIR compiler infrastructure used by the project. They upgraded MLIR commit IDs, integrated updates from other branches, and incorporated a specific commit ID from another project. Their changes also include supporting attribute promotion, which optimizes the ONNX models.
pytorchrepresentationdeep-learningmlironnx-models
onnx/onnx
Oct 2017 - Apr 2018
Open standard for machine learning interoperability
Role in this project:ML Engineer
ML EngineerContributions:7 commits, 11 PRs, 90 comments in 6 months
Contributions summary:Tianjin primarily contributes to the definitions and tests within the ONNX framework. They've updated and corrected operator schemas, including modifying attributes and docstrings to clarify functionality. Additionally, they addressed padding behavior in convolutions and implemented dimension denotations in the proto files, showing a focus on improving model descriptions and usability within the ONNX ecosystem.
pytorchmxnetdeep-learninginteroperabilitymachine-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.