Lindsay Shea is director of the Policy and Analytics Center at the A.J. Drexel Autism Institute at Drexel University in Philadelphia, Pennsylvania. She is also interim leader of the institute’s Life Course Outcomes Research Program. She focuses on research that is conducted in partnership with and that directly impacts communities and policymakers.

Lindsay Shea
Director, Policy and Analytics Center
A.J. Drexel Autism Institute
From this contributor
Pitfalls in using autism claims data: Q&A with Lindsay Shea
Insurance claims data are useful for autism research, but the field needs to standardize how they are mined, Shea says.

Pitfalls in using autism claims data: Q&A with Lindsay Shea
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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.