Statistics Developer in Santa Clara, CA, United States

Python-based app to control a multipoint focused microscope to run a light-sensitive experiment. Given a sample of light-sensitive and optically oscillatory solution compartmentalized on a 2D grid, the goal was to focus light on selected cells in order to excite or inhibit them such that the entire grid would be trained in unison (just like fireflies) or any other given structure.

A combination of deterministic and machine learning models was implemented in Python to train a model that would learn the temporal oscillations of the chemical solution and determine which cells to inhibit/excite by exposing them to light.

This was part of my dissertation: https://search.proquest.com/openview/aa8113b66c0fcb2d9a4f97fe7cfc5091/1?pq-origsite=gscholar&cbl=18750&diss=y

The goal of the project was to build a model that can predict the amount of tip a driver of a Green Taxi would receive at the end of his/her ride in NYC.

Data were obtained from the TLC Trip Record Data. After a deep analysis of features for statistical significance, two random forest models were optimized and combined to predict the tip with an MSE of about 14. Several features were revealed to be very significant such as whether a rider pays with cash or electronically, trip duration, and speed which would give an idea of traffic congestion.

https://camillegirabawe.shinyapps.io/nycgreentaxi/

Built a machine learning model to score participants of classes for a Toptal client. A model was built using multivariate linear regression algorithms. Since the client expects to gain a larger audience, the models were regularized to overcome any source of overfitting.
Tech Stack: Python, MongoDB, Node.js.

I built an exploratory data analysis (EDA) tool that can be used to visually explore a dataset, run statistics on it, and add comments in real-time which can be saved or printed later. I used Python-Flask in the back end and D3.js on the front end.