PyData Triangle November 2021 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 402827.

Speakers:

• Amanda Newport-Foster
• Harsh Parikh
• 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 Amanda Newport-Foster
7:15-8:15 Harsh Parikh
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, Yanlei Peng, Dhruv Sakalley, Gene Ferruzza, or Mark Hutchinson through meetup messages.

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Presenter: Amanda Newport-Foster

Title: What are you watching? A multi-faceted approach to CTV inventory identification

Presentation Overview:
The world of digital advertising is messy. Connected TV advertising, as ad tech's newest frontier, is even more so. Standardization around CTV has been slow in arriving, even as millions of Americans drop their cable subscriptions for streaming services. Vericast, an advertising technology company, has spent the past year and a half untangling the CTV space to create scalable targeting solutions. In this talk, we will explore how we have developed a framework of data pipelines, algorithms and good old fashioned manual processes to ensure that we are only serving our clients ads to legitimate, high-quality CTV inventory.

Bio:
Amanda Newport-Foster is a Senior Data Scientist at Vericast, a marketing technology company. She is the data science technical lead on the digital advertising optimization team and over her past 6 years with the company has had a hand in improving performance on everything from display, video and rich media to CTV and OTT. In 2018, Amanda won the IAB’s Data Rock Star - Rising Star Award for her work on revamping Vericast viewability solutions and turning the company into a market leader in viewability performance.

Presenter: Harsh Parikh

Title: Introducing MALTS & PyMALTS

Presentation Overview:
MALTS - Matching After Learning to Stretch
Uses exact matching for discrete variables and learned, generalized Mahalanobis distances for continuous variables. Instead of a predetermined distance metric, the covariates contributing more towards predicting the outcome are given higher weights.

PyMALTS is a Python implementation of the MALTS algorithm.

Bio:
Harsh is a Ph.D student in the Almost Matching Exactly (AME) Lab at Duke University. He has received 2020 Amazon Graduate Research Fellowship (Sept 2020 - Jan 2022) for working on 'Evaluating Causal Methods'.

AME Lab's goal is to develop and apply interpretable machine learning algorithms to estimate causal effects using observational data. In general, our algorithms match units with similar covariate distributions, creating high quality, exact, or almost exact matches for treatment effect estimation.
https://almost-matching-exactly.github.io/

The AME Lab algorithms:

• Dynamic Almost Matching Exactly (DAME)
• Fast Large-Scale Almost Matching Exactly (FLAME)
• Matching After Learning to Stretch (MALTS)
• Adaptive Hyper-Box Matching (AHB)