I've spoken at local talks and conferences many times over the years. This page is an attempt to keep track of those talks and to showcase them.

Spark: Distributed computing in Python


In this talk, I covered the basics of multithreading/multiprocessing in Python and the gap that it leaves. I then discussed how Spark can fill that gap and the basics of how that works. I then moved into writing some simple Spark applications and how we can run them locally and on hosted services such as Amazon's EMR.

Finite State Machines in Python; Or How I Learned to Stop Worrying and Love the Automaton

I gave this talk twice. Once was for the South Jersey Python and Web Development Meet-UP:

It was originally given at PyCon IE:

I authored this talk while I was working at Telnyx to showcase some of the work we were doing on our back-end order processing system.


Finite state machines are usually the thing of nightmares for CS undergrads. The first question any CS student asks after seeing them is "But where will I ever use this?". The answer surprised us too: You can use FSM's almost everywhere. In this talk we will do a recap on Finite State Machines, and show you some examples of where we use them at Telnyx. We will also show the transactions library and how we use this library to process FSMs in a distributed manner. And we end on a small demo of how you can use FSM machines in a real world application.

Data Classes in Python 3.7: Why and How do They Compare to Existing Solutions?

I partnered with a coworker to give this talk based on some work we were doing at Telnyx on our back-end ordering system.


Python prides itself on being a language where “There should be one – and preferably only one – obvious way to do it” (PEP 20). One place where this isn’t really true is when it comes to the question of how to store data. There are several options: dictionaries, tuples, named tuples, vanilla Python classes, and Python classes decorated with the attrs library. PEP 557 adds a new way: Data classes. In this talk we will compare and contrast each approach, give listeners a way to figure out which one is best for their particular project, and share some performance metrics for those who are concerned with speed and memory footprints.

Intro to Pandas


Wanna punch some data in the face? Need to back-hand some numbers? Hate SQL? Hate yourself? Then Pandas is for you! Come learn how to wrangle some dataframes and how to haggle some sweet, sweet results out of tables with one of the arguably hottest data science Python libraries out there.

Missing Talks

Once upon a time, I used to be a regular at the Philly Linux User's Group and even spoke at Central PA Open Source Conference. These talks were well before I was using GitHub and have been lost to time.