Nicholas Vadivelu
Quantitative Researcher & Data Scientist
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolNicholas Vadivelu is a quantitative researcher and data scientist based in New York with a decade of experience applying ML, optimization, and probabilistic methods to finance and research problems. A University of Waterloo grad in Computer Science and Statistics (3.93 GPA), he currently works in Citadel’s Data Strategies Group after internships at Google Brain, NVIDIA, Uber, and others where he focused on neural optimization, inference algorithms, and high-performance ML. He contributes to prominent open-source JAX projects like Optax and Flax, adding scheduler primitives and usability fixes that improve training stability and developer experience. Nicholas combines rigorous academic research—spanning differential privacy, epidemiological modeling, and neural bug detection—with production-minded engineering, including work on TensorRT acceleration and KFAC optimization. Colleagues describe him as the sort of engineer who bridges cutting-edge research and deployable systems, with a knack for making complex optimization tools more usable.
10 years of coding experience
3 years of employment as a software developer
High School Diploma, High School Diploma at Marc Garneau Collegiate InstituteBachelor of Mathematics, Computer Science and Statistics, 3.93, Bachelor of Mathematics, Computer Science and Statistics, 3.93 at University of Waterloo
Spanish, French, English
Stackoverflow
61reputation
704reached
1answer
3questions
Github Skills (14)
machine-learning10
jax10
python10
decay10
flax10
optimization10
algorithms8
data-structures8
algorithm8
documentation8
data-structure8
pandas6
altair6
tensorflow6
Programming languages (7)
Github contributions (5)
google-deepmind/optax
Jan 2021 - Feb 2022
Optax is a gradient processing and optimization library for JAX.
Role in this project:
ML Engineer
ML EngineerContributions:32 reviews, 58 commits, 14 PRs in 1 year 1 month
Contributions summary:Nicholas implemented and refined several schedule functions for the `optax` library, specifically focusing on learning rate and weight decay schedules. They added functions such as `piecewise_interpolate_schedule`, `linear_onecycle_schedule`, and `cosine_onecycle_schedule`, which are crucial for optimizing model training. Furthermore, they made improvements to existing functionalities, including simplifying the `piecewise_interpolate_schedule` and refactoring the code for better readability. These contributions directly impact the library's usability for machine learning model training.
jaxmachine-learningoptimization
google/flax
May 2021 - May 2022
Flax is a neural network library for JAX that is designed for flexibility.
Role in this project:ML Engineer
ML EngineerContributions:24 reviews, 11 commits, 4 PRs in 1 year
Contributions summary:Nicholas primarily contributed to the Flax library, focusing on improvements to the `linen` module. Their work included fixing docstring examples, exposing functions like `merge_param` at the top level, and making examples runnable. Additionally, the user addressed minor issues in examples, added deprecation warnings, and removed outdated documentation, indicating a focus on code quality, usability, and documentation accuracy within the library.
jaxneural-network
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