Whye Fong
Senior Engineer at Motional
Singapore, Singapore
Summary
🤩
Rockstar🎓
Top SchoolWhye Fong is a Senior Engineer based in Singapore with six years of experience building machine learning and data pipelines for autonomous systems and defense applications. At Motional he focuses on R&D for perception and large-scale data curation, while earlier roles at DSTA combined ML prototyping in computer vision, ASR and NLP with systems engineering. He contributed evaluation tooling and visualization features to the widely used nuScenes devkit, showing a practical blend of research-grade metrics work and production-minded implementation. Trained as a mechanical engineer (First Class Honours, NUS) and upskilled in NLP via Udacity, he brings a cross-disciplinary mindset that helps bridge data, models and engineering constraints. Colleagues rely on him for pragmatic solutions that turn complex sensor data into actionable perception capabilities.
6 years of coding experience
6 years of employment as a software developer
Natural Language Processing Nanodegree, Artificial Intelligence, Natural Language Processing Nanodegree, Artificial Intelligence at UdacityBachelor of Engineering - BE, Mechanical Engineering, First Class Honors, Bachelor of Engineering - BE, Mechanical Engineering, First Class Honors at National University of Singapore
English, Chinese
Github Skills (8)
computer-vision10
machine-learning10
python10
evaluation10
metric10
numpy10
tensorflow9
argparse9
Programming languages (1)
Github contributions (3)
nutonomy/nuscenes-devkit
Sep 2020 - Mar 2022
The devkit of the nuScenes dataset.
Role in this project:
ML Engineer
ML EngineerContributions:100 reviews, 24 commits, 67 PRs in 1 year 6 months
Contributions summary:Whye primarily contributed to the development of evaluation code for the nuScenes-lidarseg challenge, implementing functionalities for assessing lidar segmentation performance. This included creating classes for mapping lidar classes, computing metrics, and generating stratified evaluation results. Furthermore, the user introduced features to render and visualize the evaluation metrics. They also modified the model to include ReLU layers.
deep-learningdatasetmachine-learningnuscenesdevkit
tianweiy/nuscenes-devkit
May 2020 - Jun 2020
The devkit of the nuScenes dataset.
Contributions:4 commits in 25 days
deep-learningdatasetmachine-learningnuscenesdevkit
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