Rosa Hoekstra is reader in global perspectives on neurodevelopmental disorders at Kings College London in the United Kingdom.

Rosa Hoekstra
Lecturer
Kings College London
From this contributor
Remembering Zemi Yenus: An ambassador for autism in Africa
Zemi Yenus was the mother of a child with autism, founder of Ethiopia’s first school for autistic children and a tireless advocate for autism awareness and research in Africa.

Remembering Zemi Yenus: An ambassador for autism in Africa
How to address autism in Ethiopia and other low-income nations
Even short programs with a focus on mental health can train community health workers to help children with autism in Ethiopia and elsewhere.

How to address autism in Ethiopia and other low-income nations
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Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.

Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.

Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.

The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.