Lisa Shulman is director of autism clinical services at the Children’s Evaluation and Rehabilitation Center at Montefiore Medical Center in New York City.

Lisa Shulman
Director of autism clinical services
Children’s Evaluation and Rehabilitation Center, Montefiore Medical Center
From this contributor
How to help underserved groups gain access to autism care
Place your autism center in the community you serve, remove barriers to care, cast a wide net for autism signs, and do as much as possible in the first visit: These principles can help build a lifelong relationship with the community.

How to help underserved groups gain access to autism care
Children who ‘recover’ from autism still struggle
Some children with autism lose their diagnosis over time, but still struggle with language, learning and anxiety, says Lisa Shulman.
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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.