June 11, 2021
Privacy-preserving UX research
As part of our time in Mozilla's Data Futures Lab, I conducted user research to better understand who our users are and how we might better serve their needs.
March 19, 2021
I'm a fellow with the International Women's Media Foundation!
I'm excited to announce that I'm joining the IWMF as a Next Gen Safety Trainer
February 01, 2021
Signalboost is now part of Mozilla's DataFutures Lab
An open source project I've been working on has been awarded $100,000 as part of Mozilla's inaugural Data Futures Lab!
January 03, 2020
Some great community-oriented conferences & events
Some friends have asked me about diverse, non-corporate, & smaller scale tech conferences and events to go to. Here is a (non-exhaustive) list.
September 01, 2019
A primer on web accessibility
This post has been repurposed for the ACLU's Tech & Analytics blog! You can check it out below:
March 08, 2019
Machine learning in production with Flask, Twilio, Docker, and Google Cloud
Making machine learning models work in the wild, and what I learned along the way.
February 11, 2019
async/await vs .then() in JS Promises
February 04, 2019
Dynamic Programming with Python
Today I worked on a LeetCode problem called House Robber: given an array of numbers, each representing the amount of money in the house, find the maximum amount of money you can rob without robbing neighboring houses.
January 30, 2019
Hardware hacking with Arduino, Neopixels, and MTA data
As a New Yorker, I've had this dream, for awhile now - to be able to glance at a display on my wall and know when my train is coming. The MTA has a website: http://subwaytime.mta.info that provides this experience, but the user experience is pretty terrible and you can't even bookmark your subway stop.
January 23, 2019
Fast AI - Notes on Stochastic Gradient Descent
In the last third of the lesson 2 video, Jeremy goes into detail about linear regression and stochastic gradient descent. I found this part of the lesson really helpful, so I took some in depth notes!
January 21, 2019
Fast AI - Week 2
This week in the fast.ai course we got more into the details of getting data for image classification models, playing around with the different training parameters, and running them on sample data. This blog post is focused on some of the main code steps in the [lesson 2 Jupyter notebook](https://github.com/fastai/course-v3/blob/master/nbs/dl1/lesson2-download.ipynb), and how I used it to build my own penguin image classifier!
January 13, 2019
Setting Up Fast AI on RC's Community Cluster
RC's community cluster is a great resource to take advantage of! I had a rough time getting the Fast AI course set up, so I wrote down some instructions (hopefully) help others save some time.
January 11, 2019
Fast AI - Week One
During my time at the Recurse Center, I’m taking a machine learning course via Jeremy Howard’s Fast.ai - “Practical Deep Learning for Coders.” What attracted me to this course over others (like Andrew Ng’s Coursera course) was the top-down learning approach: the structure is hands on building models first, and diving into the theory later.