check out my code, writing, or get in contact: [email protected] (pgp)
April 01, 2022
After completing my safety training fellowship with the IWMF, I'll be teaching online and an intro to digital security workshop at some upcoming Hostile First Aid Trainings with the IWMF, and online abuse workshops with PEN America.
June 11, 2021
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 excited to announce that I'm joining the IWMF as a Next Gen Safety Trainer
February 01, 2021
An open source project I've been working on has been awarded $100,000 as part of Mozilla's inaugural Data Futures Lab!
April 06, 2020
Here are some notes and resources to get started.
January 03, 2020
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
This post has been repurposed for the ACLU's Tech & Analytics blog! You can check it out below:
March 08, 2019
Making machine learning models work in the wild, and what I learned along the way.
February 11, 2019
Promises are at the core of asynchronous programming in JavaScript, but sometimes the patterns surrounding their use can be really confusing. First of all, there are two main things you can do with promises: *create* and *handle* them. This post will be mainly focused on the latter...
February 04, 2019
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
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
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
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
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
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.
