Tirth Patel
Software Engineer at NVIDIA
United States
Summary
🤩
Rockstar🎓
Top SchoolTirth Patel is a software engineer with 7 years of experience building high-performance systems and ML tooling, currently at NVIDIA after roles at Tesla and multiple positions at Google. He combines production engineering with deep open-source contributions to flagship scientific Python projects—NumPy, SciPy, scikit-learn, PyMC, and Keras—often improving correctness, interoperability (DLPack), and Gaussian Process/Bayesian modeling features. His work spans documentation, backend fixes, and new probabilistic distributions, showing a rare mix of developer ergonomics and numerical rigor. Notably, he has contributed to widely used libraries that underpin scientific and ML stacks, helping edge-case behavior and cross-backend compatibility. He began his open-source trajectory through Google Summer of Code projects and brings practical research experience from student researcher roles at Google. Reachable at tirthasheshpatel@gmail.com, he pairs production-grade engineering with a strong focus on scientific correctness.
7 years of coding experience
3 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Nirma University
Github Skills (35)
bayesian-statistics10
pytorch10
scipy10
probabilistic-programming10
restructuredtext10
python10
data-science10
scikit10
testing10
statistics10
machine-learning10
rs10
distributions10
statistic10
numpy10
Programming languages (8)
Github contributions (5)
pymc-devs/pymc4
Jan 2020 - Jul 2020
Experimental PyMC interface for TensorFlow Probability. Official work on this project has been discontinued.
Role in this project:
Data Scientist
Data ScientistContributions:2 reviews, 12 commits, 19 PRs in 6 months
Contributions summary:Tirth's contributions primarily focused on enhancing and adding new distributions, specifically the BetaBinomial, Flat, and HalfFlat distributions, to the PyMC4 library. Their work included implementing the distributions, integrating them into the testing framework, refactoring existing distribution code, and addressing linting errors. Furthermore, the user was involved in implementing a basic Gaussian Process (GP) interface, including adding GP functionality and related testing. These changes indicate a focus on expanding the library's capabilities for Bayesian modeling and statistical inference.
pythonpymcmachine-learningdiscontinuedprobability
scipy/scipy
Jul 2020 - Dec 2022
SciPy library main repository
Role in this project:Back-end Developer
Back-end DeveloperContributions:1145 reviews, 88 commits, 166 PRs in 2 years 5 months
Contributions summary:Tirth contributed to the documentation of the Poisson distribution's `pmf` return value, focusing on edge cases. They also improved the `scipy.signal` module by enabling lists in `gauss_spline`. Additionally, the user added the negative hypergeometric distribution to `scipy.stats` and made enhancements to the multivariate hypergeometric distribution.
scipypythonscientific-computing
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.