Name: Machine Learning: Bias & Drift
Start: 2020-11-24T18:00:00-05:00
End: 2020-11-24T20:00:00-05:00

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Join us for this example led walk through of available statistical techniques for bias measurement. Covering techniques such as statistical parity and equalized odds. The event will be example led using public data sets.

In addition, we will cover ideas helpful to monitor models for drift after production deployment.

Agenda
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6:00 to 6:15: meet and greet
6:15 to 7:15: presentation and demo
7:15 to 8:00: Q & A

About the Presenters
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Mohamed Ibrahim is a Director of Technology in Assurance Engineering at FINRA. He manages the assurance engineering practice for several projects including market data projects. Mohamed plays a role in setting practice guidelines for Assurance Engineering including machine learning. Mohamed has a Ph.D. in Computers Engineering from the University of Miami, 2005.​

Tim Book is a data scientist with FINRA's Quality Assurance team in Market Regulation. He works on models detecting market manipulation, ensuring their quality and longevity and enacting plans to prevent model decay. Tim has a master's degree in statistics and over 4 years experience as a data scientist in the DC area.​

Alex Eftimiades is a data scientist within FINRA’s Quality Assurance team in Bluesheets. He works on models that detect insider trading and fraud as well as quality assurance fronts such as explainability, detecting model drift, and assessing credibility from sample size.​

Ahmed Ibrahim

Fintech Maryland

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Machine Learning: Bias & Drift​

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Machine Learning: Bias & Drift​

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