PyData Triangle November 2020 Meetup

PyData Triangle welcomes you to another exciting event.

This will be an online event. You must RSVP to this meetup event in order to see the Zoom URL. If prompted, the password is 787309

Speakers:
• Jamie Jennings - Teaching Assistant Professor in the NCSU Computer Science department
• Mehul Gangavelli - Principal Data Scientist at Fidelity Investments
• Kelsey McDonald - PhD candidate at Duke University Institute for Brain Sciences

YOU: Lightning Talks
• Sign-up for a 5 minute lightning talk slot at the meeting by posting in the chat. Or pre-sign-up by posting a comment into this announcement.

Schedule:
6:00-6:15 announcements
6:15-7:15 Jamie Jennings
7:15-7:45 Mehul Gangavelli
7:45-8:15 Kelsey McDonald
8:15-8:30 Lightning talks

The PyData code of conduct ( http://pydata.org/code-of-conduct.html ) is enforced at this Meetup. Attendees violating these rules may be asked to leave the meetup at the sole discretion of the meetup organizer.

NOTE: This meeting will be recorded.

Please propose a presentation or speaker for a future PyData Triangle meetup. Contact any of the organizers, Alice Broadhead, Gene Ferruzza, or Mark Hutchinson through meetup messages.

Follow us on twitter at: https://twitter.com/pydatatriangle

Presenter: Jamie Jennings

Title: The Rosie Pattern Language Project

Presentation Overview:
The Rosie Language allows us to process text at scale and speed. The project started at IBM and has expanded out into open source tools and environments. Jamie will teach us about Rosie and how it can be used in a Python program to

1. extract data from semi-structured text,
2. parse all kinds of data in a language-independent way,
3. protect data from misinterpretation/exploitation by Excel and other spreadsheets. This item is new work which has not yet been publicly presented.

https://rosie-lang.org/about/

Presenter: Mehul Gangavelli

Title: Pandas Indexes

Presentation Overview:
Mehul will present pandas indexes and multi-indexes. Mehul will explain why it might be worth the hassle to use them (Pros v cons)

Presenter: Kelsey McDonald

Title: Modeling Dynamic, Competitive Decision-Making in Social and Non-Social Contexts

Presentation Overview:
Understanding how the human brain makes real-world decisions in dynamic environments is a key goal of decision neuroscience. Neuroscientists and psychologists, however, have largely used experimental paradigms aimed at constraining behavioral complexity, primarily for computational tractability. Here, I describe a line of work that uses Gaussian Process models to investigate how humans behave and make goal-directed decisions in dynamic, competitive environments, and how this decision-making differs when interacting with a human opponent versus a computer opponent in a video game.