Welcome to my site & blog! I'm a Computer Science & Statistics student at the University of Waterloo interested in machine learning and programming languages. I'm currently seeking a machine learning internship for 2021 (start date flexible).

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.


Feb 2021

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


Blog Posts

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]