IAA & Berman Seminar Series – Kadija Ferryman, “Race Matters in Health Data”

Seminar Recording
Abstract: In this talk, Dr. Ferryman will describe how processes of racialization become material, or evident in health data. She will draw on the two meanings of the word matter, as substance and importance, to argue that because race is made material in health data, that it demands attention. The talk concludes with thoughts and provocations on how we can contend with race matters in health data.

HEMI's Extreme Tea

Come out and join us for HEMI’s Extreme Tea!
Take some time out of your day and enjoy tea, coffee, cake and camaraderie.
 
When: Every Tuesday from 3:30 p.m. – 4:30 p.m.
Where: Malone Hall Lobby
 
 

HEMI's Extreme Tea

Come out and join us for HEMI’s Extreme Tea!
Take some time out of your day and enjoy tea, coffee, cake and camaraderie.
 
When: Every Tuesday from 3:30 p.m. – 4:30 p.m.
Where: Malone Hall Lobby
 
 

HEMI's Extreme Tea

Come out and join us for HEMI’s Extreme Tea!
Take some time out of your day and enjoy tea, coffee, cake and camaraderie.
 
When: Every Tuesday from 3:30 p.m. – 4:30 p.m.
Where: Malone Hall Lobby
 
 

HEMI's Extreme Tea

Come out and join us for HEMI’s Extreme Tea!
Take some time out of your day and enjoy tea, coffee, cake and camaraderie.
 
When: Every Tuesday from 3:30 p.m. – 4:30 p.m.
Where: Malone Hall Lobby
 
 

2022 JHU Robotics Industry Day

Update Jan 28: Industry Day will now be virtual as we won’t know the COVID climate in the future. In order to reduce zoom fatigue, we are splitting the event into 2 half days. Industry Day will be Monday March 21 1-4pm and and Tuesday March 22 1-4pm.
2022 Industry Day Agenda/Program

Monday 3/21
Zoom

1:00 pm
Welcome WSE: Larry Nagahara, Associate Dean for Research

1:05 pm
Introduction to LCSR: Russell H. Taylor, Director

1:25 pm
LCSR Education: Louis Whitcomb, Deputy Director

1:40 pm
Student Research Talk 1 – Max Li

IAA Seminar Series – Phil Thomas, UMass Amherst, “Safe and Fair Machine Learning: A Seldonian Approach”

Link to Recording
Abstract: Machine learning algorithms are everywhere, ranging from simple data analysis and pattern recognition tools used across the sciences to complex systems that achieve superhuman performance on various tasks. Ensuring that they are safe—that they do not, for example, cause harm to humans or act in a racist or sexist way—is therefore not a hypothetical problem to be dealt with in the future, but a pressing one that we can and should address now.

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