Hello!

Email: ude.yelekreb@leen

About Me

Neel Somani

I'm a quantitative developer at Citadel in Chicago, IL. I graduated from UC Berkeley with a triple major in computer science, mathematics, and business administration in May 2019.

In the past, I've mostly worked on full stack applications and distributed systems. In college, I explored software engineering, management consulting, entrepreneurship, and finance. My research projects focused on type systems, differential privacy, and highly-scalable machine learning systems.

Industry

  • Citadel (Quantitative Developer): I build research tools like the model execution framework and the distributed compute system for the Commodities organization.
 Sep. 2020 - Present
  • Airbnb (Software Engineer): I worked on the Growth Foundation team to better ingest advertising spend from various partners. I also launched new features and campaigns for our Referrals and Affiliates programs.
 Aug. 2019 - Sep. 2020
  • Two Sigma (Modeling Engineering Intern): I built a Python library to easily estimate the expected return and volatility of various economic and financial factors over time, which are made visible to the top executives of the hedge fund.
 May - July 2019
  • Oasis Labs (Software Engineering Intern): I created a product to enable private machine learning by third parties (paper) by expanding on my previous research project Duet.
 Jan. - May 2019
  • Bain & Company (Associate Consultant Intern): I worked with a large biotechnology company on exposing the cost drivers of clinical trials. My work implemented more accurate budgeting practices in over 500 cost centers globally.
 Jun. - Aug. 2018
  • Google (Software Engineering Intern): I built a highly scalable backend tool capable of processing 100+ terabytes of raw logs to assess the stability of Google Hangouts, Meet, Allo, and Duo.
 May - Aug. 2017

Selected Research

  • Cirrus: I worked with Professor Randy Katz in the UC Berkeley RISELab to build a highly-scalable machine learning framework that runs in a serverless environment using AWS Lambda.
 2019
  • Duet: I worked with Professor Joseph Near and Professor Dawn Song to build privacy-preserving services using dedicated hardware (Intel SGX) and machine learning algorithms. Duet is a type system that allows developers to check their code for differential privacy, statically and automatically (paper).
 2019
  • Brainspell: This is a statistical platform for the human curation of neuroimaging literature, which I worked on with the Berkeley Institute for Data Science (BIDS). The application previously used a LAMP stack. I rebuilt it using the Tornado framework in Python, designed an API for collaborators to contribute, and deployed the application on Heroku. The project was presented at the 2018 Organization for Human Brain Mapping conference (abstract).
 2018

Selected Projects

  • Literature card game bots: I write technical blog posts in my free time. Here's an example of a post where I train a neural net to play a card game called Literature using a modified implementation of Q-learning. I later built an open-source React app for the game.
 2020
  • Foley Rounds: This iPhone app consists of a series of patient safety checklists that I built for Dr. Samir Desai at the Baylor College of Medicine. My app has been adopted for use by 50 physicians, and it's been awarded a grant from the Alliance for Academic Internal Medicine.
 2017