Welcome to my site & blog! I'm a Computer Science & Statistics student at the University of Waterloo interested in machine learning and programming languages.

Previously, I did deep learning research on collaborative self-driving at Uber ATG, helped improve the neural network optimization algorithm library K-FAC at Google Brain, accelerated BERT inference at NVIDIA TensorRT, and developed data-driven models to identify fraud at John Hancock Financial. I've also worked as a Research Assistant for Prof. Gautam Kamath in differential privacy, Prof. Lin Tan in deep learning-driven software analysis, and Prof. Pascal Poupart on neural network parameter learning.

Follow me on twitter, I mostly tweet about technical topics! Checkout my resources page for links to cool blog posts, talks, and other gems. Feel free to reach out through email: nicholas.vadivelu [at] gmail [dot] com.


May 2021

Awarded the 2021 Jessie W.H. Zou Memorial Award. [article]

May 2021

Open sourced my implementation of Progressively Growing GANs in Flax. [GitHub]

May 2021

Released JAX ResNet - Implementations and checkpoints of ResNet variants in Flax [GitHub] [PyPI]

Feb 2021

[FINISHED] I'm currently running a course-group based on Bayesian Data Analysis by Aki Vehtari.   Join us! [Discord] [problem solutions/demos]

Jan 2021

Released ShapeCheck - A framework-agnostic library for checking array/tensor shapes at runtime. [GitHub] [PyPI]

Dec 2020

Released Awesome JAX - A curated list of resources. [GitHub]


(* = equal contribution)

Blog Posts

May 2021

Optimizing k-Means in NumPy & SciPy [preview]

Mar 2021

Rejection & Importance Sampling Explained in Code [preview]

Feb 2021

Groupby-by From Scratch "Part 2" [preview]

Jan 2021

Interactive Data Visualization with Altair [preview]

Jan 2021

Comparing Array Partition Algorithms [preview]

Sep 2020

How to get your first Undergraduate Research Assistant Role [preview]

Aug 2020

First Steps for Learning Data Science [preview]

Nov 2019

Introduction to Neural Networks in TensorFlow 2 [preview]

Sep 2019

Introduction to Random Forests [preview]

Sep 2019

Introduction to Data Cleaning with Pandas [preview]