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] |

- Learning to Communicate and Correct Pose Errors.
**Nicholas Vadivelu**, Mengye Ren, James Tu, Jingkang Wang, Raquel Urtasun.*Conference on Robot Learning (CoRL),*Virtual, 2020. [arxiv] [abstract] [video] - Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization.
Pranav Subramani*,
**Nicholas Vadivelu***, Gautam Kamath.*NeuRIPS Privacy-Preserving Machine Learning Workshop,*Virtual, 2020. [arxiv] [code] [abstract]

## 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] |

- Optimizing Machine Learning Code in NumPy & SciPy
*University of Waterloo Data Science Club,*Waterloo, Mar 2022. [video] [description] [code] [slides] - Contributing to Open Source Data Science Libraries
*University of Waterloo Data Science Club,*Waterloo, Jan 2022. [video] [description] [slides] - Clustering for Image Analysis (with Kanika Chopra)
*WiSTEM High School Student Conference,*Virtual, Feb 2021. [description] [slides] [notebook] - Establishing a Productive Machine Learning Workflow
*Hack the North++,*Virtual, Jan 2021. [video] [description] [code] [slides] [syllabus] [hackpack] - Interactive Data Visualization with Altair
*Hack the North++,*Virtual, Jan 2021. [video] [description] [notebook] [syllabus] [hackpack] - Overview of Data Science and Data Science Careers.
*University of Waterloo Data Science Club,*Waterloo, Aug 2020. [video] [description] [slides] - What You See is What You Get: Exploiting Visibility for 3D Object Detection.
*Uber ATG Paper Reading Group,*Toronto, July 2020. [description] [slides] - Introduction to JAX for Machine Learning and More.
*University of Waterloo Data Science Club,*Waterloo, July 2020. [video] [description] [notebook] - Stand-Alone Self-Attention in Vision Models.
*Uber ATG Paper Reading Group,*Toronto, April 2020. [description] [slides] - Neural Network Optimization Methods
*Reading Group,*Waterloo, December 2019. [description] [slides] - Introduction to Neural Networks in TensorFlow 2.0.
*Laurier Developer Student Club,*Waterloo, Nov 2019. [video] [description] [notebook] - Introduction to Machine Learning with Scikit-learn.
*Hack the North,*Waterloo, Sep 2019. [video] [description] [notebook] [slides] - Introduction to Pandas for Python.
*Hack the North,*Waterloo, Sep 2019. [video] [description] [notebook]