Email: ude.yelekreb@leen

About Me

Neel Somani

I'm working on something new! I was previously a quantitative analyst at Citadel in Chicago, IL, working on the Central Research team in Commodities. I graduated from UC Berkeley with a triple major in computer science, mathematics, and business administration in May 2019.

My research projects focused on type systems, differential privacy, and highly-scalable machine learning systems. Fun fact: I'm a licensed Realtor with the brokerage Keller Williams!


  • Citadel (Quantitative Research Analyst ← Quantitative Developer): As a developer, I built research tools like the model execution framework and the distributed compute system for the Commodities organization.
Sep. 2020 - Feb. 2022
  • Airbnb (Software Engineer): I worked on the Growth team to attribute bookings to ad campaigns and optimize spend to maximize return on investment.
Aug. 2019 - Sep. 2020
  • Two Sigma (Software Engineering Intern): I built a Python library to easily estimate the expected return and volatility of various economic and financial factors over time.
May - July 2019
  • Bain & Company (Associate Consultant Intern): I worked with a large biotechnology company to expose the cost drivers of clinical trials. My work implemented more accurate budgeting practices in over 500 cost centers globally.
Jun. - Aug. 2018

Selected Research

  • PrivGuard: I worked with Prof. Joseph Near and Prof. Dawn Song to define a type system capable of automatically enforcing popular privacy and security regulations, including GDPR and HIPAA.
  • Duet: This is a type system that allows developers to check their code for differential privacy, statically and automatically. I used dedicated hardware (Intel SGX) and machine learning algorithms to build privacy-preserving services.

Selected Projects

  • Intro to power pricing: I write technical blog posts in my free time. In this post, I explain the basics of the electricity market. This article reached the top of Hacker News.
  • Cirrus: I worked with Prof. Randy Katz in the UC Berkeley RISELab to build a highly-scalable machine learning framework that runs in a serverless environment using AWS Lambda.