Qing Feng - Research Scientist at Meta

Qing Feng

Research Scientist at Meta

San Francisco, California, United States

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Summary

🤩

Rockstar

🎓

Top School

Qing Feng is a research scientist with a decade of experience applying statistical rigor and machine learning to large-scale internet products, currently advancing recommendations at Meta's Threads. He holds a PhD in Statistics with a CS minor from UNC Chapel Hill and brings deep expertise in Bayesian optimization, causal inference, and reinforcement learning from both research and production roles. Qing led an advanced experimentation team at Meta, shipping novel BO models (LCE-A, LCE-M, SAC) and integrating them into prominent open-source PyTorch projects like BoTorch and Ax, contributing device-aware kernels and unit tests. Prior work at Uber combined causal methods and representation learning to improve marketplace pricing and forecasting across hundreds of markets, showing a rare blend of principled statistics and impact-driven engineering. Colleagues rely on him for bridging cutting-edge research with robust backend implementations that scale in production.

10 years of coding experience

10 years of employment as a software developer

Doctor of Philosophy (PhD) Statistics minor in computer sciense, Doctor of Philosophy (PhD) Statistics minor in computer sciense at University of North Carolina at Chapel Hill

English, Chinese

Github Skills (13)

pytorch10

machine-learning10

bayesian10

api10

gpytorch10

optimisation10

python10

optimization10

mcmc9

testing8

apidoc8

apim4

api-design4

Programming languages (2)

Jupyter NotebookPython

Github contributions (5)

facebook/Ax

Mar 2020 - Sep 2022

Adaptive Experimentation Platform

Role in this project:

Back-end Developer

Contributions:19 commits, 15 PRs, 7 comments in 2 years 6 months

Contributions summary:Qing's contributions primarily revolve around improving the `ax` library, an adaptive experimentation platform. They focused on refactoring and enhancing existing functionalities within the codebase. This included moving and re-using functions, updating logic to discern between different model types (MTGP and ModelListGP), and handling potential errors in samplers. Furthermore, the user added new models, such as LCE-A, LCE-M, and SAC Botorch models, indicating a focus on expanding the platform's capabilities.

experimentationadaptivesimulationadaptive-experimentation-platformexperimentation-platform

pytorch/botorch

Nov 2020 - Sep 2022

Bayesian optimization in PyTorch

Role in this project:

ML Engineer

Contributions:3 reviews, 7 commits, 14 PRs in 1 year 10 months

Contributions summary:Qing's primary contributions revolve around the development and modification of Bayesian optimization techniques within the PyTorch framework. They added new models (LCE-A, LCE-M, and SAC) and made contextual kernels device-aware, indicating a focus on model implementation and optimization. The commits also include the integration of these models within the existing botorch framework. The user further contributed unit tests to validate the implemented changes.

pytorchoptimizationmultiobjective-optimizationmachine-learningbayesian-optimization

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