On Brain-Computer Interfaces and Explainable Workflows using Python

We’ll be sending a Zoom link to this virtual event closer to the event date.

AGENDA

7pm | Opening Remarks + Announcement

~7:05pm | How to Create Brainwave-Sensing Headphones using OpenBCI and Python
ANGELA VUJIC, PhD researcher, MIT Media Lab

~8:00pm | Explainable Workflows Using Python
AUSTIN EOVITO, Data Scientist, IBM

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TALK SUMMARY

How to Create Brainwave-Sensing Headphones using OpenBCI and Python

Find out during this tutorial how to interface with the OpenBCI using Python and useful Python libraries for electrophysiology. You will also learn how to integrate an OpenBCI with over-ear headphones to create an at-home beginner's electroencephalography (EEG) device. An OpenBCI is not required to attend!

ABOUT THE SPEAKER

Angela Vujic is a PhD researcher at the MIT Media Lab. She works at the intersection of computer science, neuroscience and design to develop biosensing technology for mental and emotional health.

Motivated by discoveries linking gut health to mental health, Angela seeks to start a new area in her field coined gut-brain computer interfacing (GBCI). She developed and is testing a GBCI that could enable individuals to sense and modulate their gut state, with the goal of connecting it to their mental wellbeing.

Previously, she completed her bachelor’s in computer science at Georgia Tech and was part of the GT BrainLab in brain-computer interfaces (BCI). She invented MoodLens, novel fiber optic display integrated in brainwave-sensing glasses, built to help individuals with severe paralysis express emotion via eye contact.

She has presented internationally, first-authored prestigious peer-reviewed publications, and won multiple awards for her work.

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TALK SUMMARY

Explainable Workflows using Python

This talk approaches the typical data science workflow with a focus on explainability. Simply put, it focuses on skills and tactics used to help data scientists articulate their findings to end-users, stakeholders, and other data scientists. From data ingestion, cleaning and feature selection, and ultimately model selection, explainability can be incorporated into a data scientist's workflow. Using a combination of semi-automated and open source software, this talk walks you through an explainable workflow.

ABOUT THE SPEAKER

Austin Eovito is a Data Scientist on the Technical Marketing and Evangelism team at IBM in San Francisco, California. As a recent graduate student of Florida State University, Austin is focused on the balance of bleeding-edge research produced by academia and the tools used in applied data science. His Master's thesis was on White Collar Crime using Time-aware Joint-Topic-Sentiment Analysis (TTS), and his areas of interest are NLP, applied data science, and Explainable AI. Outside of work, he enjoys spending time with his dog and traveling.