Alex Wang
PhD Candidate
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlex Wang is a PhD candidate in Computer Science at Stanford with 11 years of applied ML and software engineering experience spanning industry research and quantitative finance. He has driven production-facing research at organizations including AWS, Nuance, Citadel, and Cartesia, working on time-series forecasting, graph neural networks, neural machine translation, and LLMs for alpha research. A hands-on backend contributor to gpytorch, Alex has implemented core Gaussian Process components (e.g., CatLazyTensor and MultiDeviceKernel testing), reflecting deep expertise in probabilistic modeling and PyTorch internals. He combines rigorous academic training (Cornell BA/MS, Stanford PhD) with a track record of shipping robust research code into real-world systems, and often focuses on long-context and memory-centric models. Based in Palo Alto, he blends quantitative rigor with practical engineering to bridge research and production.
11 years of coding experience
1 year of employment as a software developer
Bachelor of Arts - BA Computer Science Mathematics Physics, Bachelor of Arts - BA Computer Science Mathematics Physics at Cornell UniversityDoctor of Philosophy - PhD Computer Science (Machine Learning), Doctor of Philosophy - PhD Computer Science (Machine Learning) at Stanford University
English, Chinese, Spanish, French
Github Skills (7)
pytorch10
machine-learning10
python10
gaussian-processes10
back-end-development9
unit-testing8
gpu-acceleration8
Programming languages (15)
Github contributions (5)
cornellius-gp/gpytorch
Dec 2018 - Apr 2021
A highly efficient implementation of Gaussian Processes in PyTorch
Role in this project:
Back-end Developer / ML Engineer
Back-end Developer / ML EngineerContributions:110 commits, 29 PRs, 103 pushes in 2 years 3 months
Contributions summary:Alex's commits primarily focus on implementing and testing components related to Gaussian Processes (GPs) in PyTorch, as indicated by the "gpytorch" directory. They added and refined the `CatLazyTensor` class, a core component for handling concatenations within the GP framework. The commits involved implementing new features, fixing bugs, and adding tests within the `gpytorch/lazy` module, highlighting contributions to the library's core functionality. Further contributions demonstrate integration and testing of the MultiDeviceKernel.
pytorchgpu-accelerationgaussianstochastic-processesmodular
KeAWang/pensieve
Jan 2022 - Feb 2023
Contributions:44 commits, 35 pushes, 1 branch in 1 year
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