Hi! I’m Rohan, a computer science student at UCLA. My main interests are machine learning/deep learning, math, running, playing chess, reading, and writing. I’m originally from San Jose, California.


I will be joining Affirm as a machine learning intern in the spring of 2018 and Facebook as a software engineering intern in the summer of 2018.

Over the summer of 2017, I worked at Blend as a software engineering intern on the intelligence team and over the summer of 2016 I worked at Hudl as a software engineering intern on the messaging team.


From Fall 2015 to Winter 2017, I was an undergraduate machine learning researcher at UCLA’s Ozcan Research Group, where I implemented the lab’s first deep learning models that were used to automatically diagnose bloodborne and waterborne pathogen disease. Our group’s work in the form of an abstract was presented at UCLA’s Annual Undergraduate Research Day and a few other conferences. A poster and abstract are available.


With the help of several officers, I currently run AI at UCLA, a committee of the umbrella organization ACM at UCLA. We teach machine learning topics through regular workshops as well as work on projects and read papers and books together. Most of our work is currently in deep learning. This past spring, we hosted a series of weekly Tensorflow workshops.

Other than that, I’ve previously tutor lower division computer science classes as a part of Upsilon Pi Epsilon, and hosted potential UCLA freshman as part of an outreach program by UCLA’s Regents Scholars Society.

Personal Projects

I occassionally work on projects mostly in the field of machine learning and deep learning to learn more about the field and how to use what I’ve learned in practice. Some of my more interesting projects are Machine Learning on Movie Reviews, a hackathon project that dealt with identifying similar athletes, a bare-bones implementation of a Neural Network, and a machine learning library in Python.

Other in progress and miscellaneous projects are on my Github.


I have enjoyed writing since high school, and I exercise that enjoyment on Quora and this blog. Below is a list of my favorite pieces I’ve written.

  1. Getting Started in Machine Learning

  2. My answer to “Why is the 0-1 loss a poor choice of loss function?

  3. An explanation of kernelized Support Vector Machines

  4. What is it like to be a Computer Science Student at UCLA?

  5. What are the most promising startups to watch in 2017?

  6. Welcome to AI at UCLA!

  7. Scaled ML Notes and Thoughts


I’ve found that one of the best ways to learn something is by being able to explain it (ie the Feynman Technique). I’ve attempted to explain a few of the tricker concepts I’ve had trouble in and I’m more interested in, in the form of blog posts or Jupyter notebooks:

  1. Word2Vec and Language Modeling and discussion on HN

  2. Bias and Variance in Machine Learning

  3. Tensorflow workshop series to which all other AI officers contributed to.

  4. Implementing a Neural Network in Python

Contact me