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, quantitative research on alternative data at Citadel, 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 (though not so active recently...)! 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.


Jan 2022

Runner-Up for the CRA Outstanding Undergraduate Research Award [award page]

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]

Sep 2018

Communicating Data Science to Professionals [preview]

Sep 2018

Lessons Learned from Analyzing Competitive Pokemon [preview]