Alycia Halladay is chief science officer of the Autism Science Foundation.
Alycia Halladay
Chief science officer
Autism Science Foundation
From this contributor
New program offers $35K grants to study ‘profound autism’
People who have ‘profound autism’ — those with severe intellectual disability, limited communication abilities or both — tend to be excluded from research. The Autism Science Foundation seeks to change that.

New program offers $35K grants to study ‘profound autism’
Questions for Amaral, Halladay: Boosting brainpower
A new network of brain banks aims to collect and disburse tissue donations to U.S. autism researchers.

Questions for Amaral, Halladay: Boosting brainpower
Explore more from The Transmitter
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.