Li Ma
Software Engineer - Kaggle at Google
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolLi Ma is a software engineer with 13 years of experience specializing in machine learning and applied AI, currently working on Kaggle and Project Starline at Google. He holds a PhD in Machine Learning from Nanyang Technological University and has built production ML solutions across companies including Facebook, Alibaba Cloud, and Rolls‑Royce. His background spans research, mentorship (Udacity Self‑Driving Car Nanodegree), and applied data science in industries from automotive to finance and cloud. An active contributor to the popular Keras ecosystem, he has improved memory efficiency and added practical examples that make deep learning recipes more accessible. Colleagues know him for translating advanced research into usable models and examples that help teams and the community iterate faster. Based in Seattle, he combines rigorous academic training with a strong track record shipping real-world ML systems.
13 years of coding experience
8 years of employment as a software developer
Bachelor of Computer Science Computer Science, Bachelor of Computer Science Computer Science at Zhejiang UniversityNanoDegree Self Driving Car, NanoDegree Self Driving Car at UdacityDoctor of Philosophy (Ph.D.) Machine Learning, Doctor of Philosophy (Ph.D.) Machine Learning at Nanyang Technological University Singapore
Github Skills (8)
neural-network10
keras10
machine-learning10
deep-learning10
tensorflow10
python10
data-science7
computer-vision7
Programming languages (8)
Github contributions (5)
keras-team/keras
Jul 2016 - Sep 2016
Deep Learning for humans
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 11 PRs, 72 comments in 1 month
Contributions summary:Li primarily contributed to example scripts and code within the Keras library, focusing on improving memory efficiency and updating/adding examples. The contributions include optimizing a pre-trained word embedding example, updating a neural style transfer example, and adding a new example for Net2Net experiments with MNIST. These changes suggest an involvement in showcasing and improving the practical application of deep learning models using the Keras framework.
pythondata-sciencedeep-learningneural-networksmachine-learning
dolaameng/deeplearning-exploration
Apr 2015 - Jun 2018
Contributions:158 commits, 3 PRs, 169 pushes in 3 years 2 months
pytorchexploredeep-learningmachine-learningtensorflow
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.