Shengyang Sun
Member Of Technical Staff at xAI
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolShengyang Sun is an AI research scientist and engineering leader with 11 years of experience, combining deep academic training (PhD, University of Toronto; BE, Tsinghua) with industry impact across NVIDIA, xAI, Amazon, DeepMind and Google. He has led large-model alignment and post-training efforts—serving as a leading author on Nemotron-4-340B-Instruct—and now guides AI experts toward AGI-focused LLM reasoning at xAI while holding a senior research role at NVIDIA. His open-source contributions include Bayesian deep-learning work on the well-regarded zhusuan library, where he implemented Bayesian neural network tutorials and advanced variational dropout techniques. Comfortable spanning research and product-facing ML, he has repeatedly moved cutting-edge models into production-relevant contexts for advertising relevance and large-scale model instruction tuning.
11 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of TorontoBachelor of Engineering - BE, Electrical and Electronics Engineering, 4.0, Bachelor of Engineering - BE, Electrical and Electronics Engineering, 4.0 at Tsinghua University
Github Skills (9)
generative-model10
variational-inference10
bayesian10
deep-learning10
probabilistic-programming10
tensorflow10
python10
bayesian-inference10
mc8
Programming languages (3)
Github contributions (5)
thu-ml/zhusuan
Oct 2016 - Mar 2018
A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow
Role in this project:
ML Engineer
ML EngineerContributions:14 commits, 1 PR, 4 pushes in 1 year 5 months
Contributions summary:Shengyang contributed to the development of a Bayesian neural network model within the zhusuan library. Their work included implementing a Bayesian neural network tutorial and making code improvements, such as adding a matrix variate normal distribution. The commits show a focus on variational dropout techniques and addressing API-related issues.
information-theorydeep-learningbayesian-inferencegraphical-modelsmachine-learning
ssydasheng/FBNN
Apr 2019 - Jul 2020
Contributions:11 commits, 6 pushes, 4 comments in 1 year 3 months
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