Zhiyuan Lin - Research Scientist at Meta

Zhiyuan Lin

Research Scientist at Meta

Palo Alto, California, United States

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Summary

👤

Senior

🎓

Top School

Zhiyuan Lin is a research scientist at Meta with 13 years of experience specializing in applied machine learning, Bayesian optimization, and adaptive experimentation. He holds a CS PhD from Stanford and a BS from Georgia Tech, and contributes to prominent open-source projects such as PyTorch’s BoTorch and Meta’s Ax, where he’s implemented core Pairwise Preference Learning and improved visualization for experimentation. At Meta’s Central Applied Science team he bridges research and production, turning probabilistic models and preference-learning methods into deployable experimentation tools. His background spans industry research internships at Facebook, Tencent, eBay and Yahoo, reflecting a long-running focus on recommender systems, user behavior modeling, and scalable ML. An often-overlooked strength is his hands-on work on low-level model integration (e.g., PairwiseGP and input transforms) that directly improves the fidelity of active learning and preference-based optimization.

12 years of coding experience

3 years of employment as a software developer

Bachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at Georgia Institute of Technology

Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Stanford University

Chinese, English

Github Skills (20)

pytorch10

python10

model-driven10

machine-learning10

gaussian-processes10

model-building10

bayesian10

optimisation10

modeling10

model-driven-development10

optimization10

data-visualizations9

apidoc9

data-visualisation9

api9

Programming languages (3)

HTMLJupyter NotebookPython

Github contributions (5)

pytorch/botorch

Nov 2019 - Oct 2022

Bayesian optimization in PyTorch

Role in this project:

Back-end Developer / ML Engineer

Contributions:21 commits, 32 PRs, 6 comments in 2 years 11 months

Contributions summary:Zhiyuan primarily contributes to the `botorch` repository, a Bayesian optimization library in PyTorch. The commits show the user modifying the `PairwiseGP` model, implementing and adjusting features like model fitting and the use of a `ScaleKernel`. They also worked on supporting input transforms within the PairwiseGP model. Their work directly impacts core model functionality and its integration with active learning and preference learning tasks.

pytorchoptimizationmultiobjective-optimizationmachine-learningbayesian-optimization

facebook/Ax

Sep 2021 - Jun 2022

Adaptive Experimentation Platform

Role in this project:

Back-end Developer & ML Engineer

Contributions:5 commits, 47 PRs, 3 comments in 9 months

Contributions summary:Zhiyuan contributed to the Ax experimentation platform by enhancing tooltip functionality within contour plots to display parameter and metric values. They also implemented color-coding for tradeoff plots, improving the visualization of in-sample data. Furthermore, the user added support for Pairwise Preference Learning within Ax, creating `PairwiseModelBridge` and integrating it with the `RankingDataset`. Additionally, the user made modifications to support PE/PLBO by revamping the previous implementation to pass the pref_model into the EUBO acqf or serve as the objective, extracting it from a `PairwiseModelBridge`.

experimentationadaptivesimulationadaptive-experimentation-platformexperimentation-platform

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