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SUMMARY:Machine Learning for the Developing World
DTSTART:20180628T133000
DTEND:20180628T163000
DTSTAMP:20260407T064216Z
UID:19777dbea8cc7ebf6ddd7e1624ac0cf0b583f144af3750d689e75348
CATEGORIES:Conferences - Seminars
DESCRIPTION:Maria De-Arteaga\nResearchers from across the social and compu
 ter sciences are increasingly using machine learning to study and address 
 global development challenges. In this talk\, I examine the burgeoning fie
 ld of machine learning for the developing world (ML4D). First\, a brief re
 view of prominent literature is presented\, followed by suggestions of bes
 t practices drawn from the literature for ensuring that ML4D projects are 
 relevant to the advancement of development objectives. Finally\, I discuss
  how developing world challenges can motivate the design of novel machine 
 learning methodologies. This talk provides insights into systematic differ
 ences between ML4D and more traditional machine learning applications. It 
 also discusses how technical complications of ML4D can be treated as novel
  research questions\, how ML4D can motivate new research directions\, and 
 where machine learning can be most useful.\n\nMaria De-Arteaga is a fourth
  year PhD student in the joint Machine Learning and Public Policy program 
 at Carnegie Mellon University\, where she is advised by Artur Dubrawski. S
 he is passionate about creating novel machine learning algorithms that are
  motivated by existing societal challenges\, improving fairness qualities 
 of machine learning systems\, and understanding how machine learning can b
 etter help us achieve global development goals. During her PhD\, Maria has
  applied her work to counter-human trafficking and sexual violence initiat
 ives\, as well as various projects in the healthcare domain. In 2017\, sh
 e co-organized the NIPS Workshop on Machine Learning for the Developing Wo
 rld.
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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