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


I had the awesome opportunity to work as a software engineering intern on the messaging squad at Hudl in the summer of 2016. I worked mostly on the backend, and shipped several messaging services that other microservices could use. I am especially thankful to my mentors Andy Pryor and Graham McCulloch who made my first industry experience an excellent one.

In the summer of 2017, I got to work at Blend on the Intelligence team along some great engineers, QAs, product managers, and designers. I was fortunate enough to have an excellent mentor in Albert Lee, who was always available to help me with my JavaScript difficulties and really wanted to see me succeed as an engineer.

Other Experiences

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.

From Fall 2015 to Winter 2017, I was an undergraduate 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.

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

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 (in no particular order).

  1. Getting Started in Machine Learning

  2. Implementing a Neural Network in Python

  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 Modelling

  2. Bias and Variance in Machine Learning

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


I maintain a list of my favorite reads, as well as my current reading list.

2017 Reading List


  • The Tipping Point by Malcolm Gladwell
  • Ego is the Enemy by Ryan Holiday
  • Elon Musk (biography)

In Progress

  • The Black Swan


  • Meditations by Marcus Auerelius

Contact me