Member Of Technical Staff at Thinking Machines Lab
Berkeley, California, United States
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Summary
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Rockstar
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Top School
Long Lian is a PhD student in EECS at UC Berkeley with 12 years of engineering experience focused on multi-modal generative models, efficient deep learning, and RL-augmented reasoning for LLMs/VLMs. He combines academic research with industry experience across NVIDIA and Meta, contributing to large-scale pretraining and multi-agent reinforcement learning projects. Practically minded, he has shipped front-end features for the widely used julia-vscode extension (plot navigator with thumbnails) and built production web and backend systems during internships at Yelp and startups. His work spans medical imaging applications, unsupervised video and long-tailed recognition, and tooling for the Julia data-science stack, reflecting a rare mix of deep research and hands-on product engineering. Based in Berkeley, he bridges cutting-edge ML research and developer-facing software, often focusing on making complex models efficient and usable.
12 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD Electrical Engineering and Computer Sciences, Doctor of Philosophy - PhD Electrical Engineering and Computer Sciences at University of California, Berkeley
High School Summer College, High School Summer College at Stanford University
Contributions:5 reviews, 10 commits, 1 PR in 28 days
Contributions summary:Long primarily focused on developing the plot panel feature for the Julia extension in Visual Studio Code. They implemented a webview-based plot navigator, including the ability to add and display plot thumbnails. Furthermore, the user updated the styles for the thumbnail elements and updated JavaScript file locations. The contributions directly enhance the user interface for visualizing and interacting with plots within the VS Code environment, enhancing the usability of the Julia extension.
Contributions:20 commits, 16 pushes, 1 branch in 6 days
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Long Lian - Member Of Technical Staff at Thinking Machines Lab