Email: ude.yelekreb@leen

Twitter: @neeljsomani

About

Neel Somani

I'm a software engineer in the Bay Area. I graduated from UC Berkeley with a triple major in computer science, mathematics, and business administration in May 2019.

In the past, I've worked as a Modeling Engineering Intern at the hedge fund Two Sigma, as an Associate Consultant Intern at Bain & Company, and as a Software Engineering Intern at Google. Over the last few years, I've worked on a type system to statically enforce differential privacy, a highly-scalable serverless machine learning framework, and a platform to facilitate the meta-analysis of neuroimaging literature.

Industry

  • 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 is made visible to the top executives of the hedge fund. (May - July 2019)
  • Oasis Labs (Software Engineering Intern): I worked on a product to enable private machine learning by third parties 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, to be used in future budgets. My work implemented more accurate budgeting practices in over 500 cost centers globally. (Jun. - Aug. 2018)
  • Google (Software Engineering Intern): On the analytics team for real-time communications apps, I built a highly scalable backend tool for Google Hangouts, Meet, Allo, etc., which processes 100+ terabytes of raw logs. (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.
  • 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).
  • Brainspell: This is a statistical platform for the human curation of neuroimaging literature, which I'm working 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).

Selected Projects

  • 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.
  • Clockins: A number of years ago (in high school), I built an employee check-in system that used the GPS capability on Android devices. I've released a stripped-down version of the code, and I released the calendar component as open-source as well.