Aditya Vaidya
Visiting Researcher at The University of Texas at Austin
San Francisco Bay Area United States
Summary
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Senior🎓
Top SchoolAditya Vaidya is a research-focused software engineer and PhD candidate in Computer Science at UT Austin, currently a visiting researcher at UC Berkeley with 14 years of hands-on experience spanning academia and industry. He specializes in numerical computing and machine learning infrastructure, contributing to high-impact open-source projects like JAX and TensorFlow Probability where he implemented and tested core mathematical primitives such as cross products and improved ndarray utilities. Based in the San Francisco Bay Area, he blends rigorous theoretical training (Turing Scholars honors, triple-major background including mathematics and linguistics) with practical backend engineering for GPU/TPU-accelerated workflows. His work often targets subtle dtype and shape-handling bugs that improve reliability for downstream probabilistic and differentiable programming users. He has interned at Microsoft and GoDaddy, bringing production-focused perspectives into research code. Colleagues describe him as a detail-oriented developer who turns mathematically tricky problems into robust, well-tested library code.
14 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at The University of Texas at AustinTexas Academy of Mathematics and Science
Hindi, Marathi, Spanish, English
Github Skills (14)
numerics10
tensorflow10
numerical10
jax10
computation10
numeric10
python10
linear-algebra10
numpy10
testing10
machine-learning9
probabilistic-programming8
statistics8
data-science7
Programming languages (10)
Github contributions (5)
jax-ml/jax
Mar 2019 - Jul 2019
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 PRs, 7 comments, 1 issue in 4 months
Contributions summary:Aditya contributed to the JAX library by implementing and testing mathematical functions, specifically focusing on the `cross` product for NumPy arrays. They added the `cross` function and its tests, fixed data type issues, and addressed problems with the `atleast_nd` functions. Furthermore, the user made modifications and corrections to the `average` function. They demonstrated expertise in numerical computation and testing within the JAX framework.
pytorchpythonjitautomatic-differentiationgpu
tensorflow/probability
Mar 2019 - Jul 2019
Probabilistic reasoning and statistical analysis in TensorFlow
Role in this project:ML Engineer
ML EngineerContributions:9 commits in 3 months
Contributions summary:Aditya contributed to the implementation and testing of mathematical functions, specifically the cross product, within the JAX/NumPy framework of the repository. Their work included adding new functions, fixing data type issues, and modifying existing test cases. The contributions demonstrate a focus on core mathematical operations and their integration within the probabilistic reasoning and statistical analysis tools offered by the project. The user also worked on the 'atleast_nd' functions, improving their functionality.
statisticspythonprobabilistic-reasoningdata-sciencedeep-learning
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